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\n  \n 2024\n \n \n (31)\n \n \n
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\n \n\n \n \n \n \n \n \n Physicochemical graph neural network for learning protein–ligand interaction fingerprints from sequence data.\n \n \n \n \n\n\n \n Koh, H. Y.; Nguyen, A. T. N.; Pan, S.; May, L. T.; and Webb, G. I.\n\n\n \n\n\n\n Nature Machine Intelligence. June 2024.\n \n\n\n\n
\n\n\n\n \n \n \"PhysicochemicalPaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@article{koh_physicochemical_2024,\n\ttitle = {Physicochemical graph neural network for learning protein–ligand interaction fingerprints from sequence data},\n\tcopyright = {All rights reserved},\n\tissn = {2522-5839},\n\turl = {https://doi.org/10.1038/s42256-024-00847-1},\n\tdoi = {10.1038/s42256-024-00847-1},\n\tabstract = {In drug discovery, determining the binding affinity and functional effects of small-molecule ligands on proteins is critical. Current computational methods can predict these protein–ligand interaction properties but often lose accuracy without high-resolution protein structures and falter in predicting functional effects. Here we introduce PSICHIC (PhySIcoCHemICal graph neural network), a framework incorporating physicochemical constraints to decode interaction fingerprints directly from sequence data alone. This enables PSICHIC to attain capabilities in decoding mechanisms underlying protein–ligand interactions, achieving state-of-the-art accuracy and interpretability. Trained on identical protein–ligand pairs without structural data, PSICHIC matched and even surpassed leading structure-based methods in binding-affinity prediction. In an experimental library screening for adenosine A1 receptor agonists, PSICHIC discerned functional effects effectively, ranking the sole novel agonist within the top three. PSICHIC’s interpretable fingerprints identified protein residues and ligand atoms involved in interactions, and helped in unveiling selectivity determinants of protein–ligand interaction. We foresee PSICHIC reshaping virtual screening and deepening our understanding of protein–ligand interactions.},\n\tjournal = {Nature Machine Intelligence},\n\tauthor = {Koh, Huan Yee and Nguyen, Anh T. N. and Pan, Shirui and May, Lauren T. and Webb, Geoffrey I.},\n\tmonth = jun,\n\tyear = {2024},\n}\n\n\n\n
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\n In drug discovery, determining the binding affinity and functional effects of small-molecule ligands on proteins is critical. Current computational methods can predict these protein–ligand interaction properties but often lose accuracy without high-resolution protein structures and falter in predicting functional effects. Here we introduce PSICHIC (PhySIcoCHemICal graph neural network), a framework incorporating physicochemical constraints to decode interaction fingerprints directly from sequence data alone. This enables PSICHIC to attain capabilities in decoding mechanisms underlying protein–ligand interactions, achieving state-of-the-art accuracy and interpretability. Trained on identical protein–ligand pairs without structural data, PSICHIC matched and even surpassed leading structure-based methods in binding-affinity prediction. In an experimental library screening for adenosine A1 receptor agonists, PSICHIC discerned functional effects effectively, ranking the sole novel agonist within the top three. PSICHIC’s interpretable fingerprints identified protein residues and ligand atoms involved in interactions, and helped in unveiling selectivity determinants of protein–ligand interaction. We foresee PSICHIC reshaping virtual screening and deepening our understanding of protein–ligand interactions.\n
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\n \n\n \n \n \n \n \n Time-LLM: Time Series Forecasting by Reprogramming Large Language Models.\n \n \n \n\n\n \n Jin, M.; Wang, S.; Ma, L.; Chu, Z.; Zhang, J. Y; Shi, X.; Chen, P.; Liang, Y.; Li, Y.; Pan, S.; and others\n\n\n \n\n\n\n In International Conference on Learning Representations (ICLR), 2024. \n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@inproceedings{jin_time-llm:_2024,\n\ttitle = {Time-{LLM}: {Time} {Series} {Forecasting} by {Reprogramming} {Large} {Language} {Models}},\n\tcopyright = {All rights reserved},\n\tbooktitle = {International {Conference} on {Learning} {Representations} ({ICLR})},\n\tauthor = {Jin, Ming and Wang, Shiyu and Ma, Lintao and Chu, Zhixuan and Zhang, James Y and Shi, Xiaoming and Chen, Pin-Yu and Liang, Yuxuan and Li, Yuan-Fang and Pan, Shirui and {others}},\n\tyear = {2024},\n}\n\n\n\n
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\n \n\n \n \n \n \n \n Sign is Not a Remedy: Multiset-to-Multiset Message Passing for Learning on Heterophilic Graphs.\n \n \n \n\n\n \n Liang, L.; Kim, S.; Shin, K.; Xu, Z.; Pan, S.; and Qi, Y.\n\n\n \n\n\n\n In International Conference on Machine Learning (ICML), 2024. \n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@inproceedings{liang_sign_2024,\n\ttitle = {Sign is {Not} a {Remedy}: {Multiset}-to-{Multiset} {Message} {Passing} for {Learning} on {Heterophilic} {Graphs}},\n\tcopyright = {All rights reserved},\n\tbooktitle = {International {Conference} on {Machine} {Learning} ({ICML})},\n\tauthor = {Liang, Langzhang and Kim, Sunwoo and Shin, Kijung and Xu, Zenglin and Pan, Shirui and Qi, Yuan},\n\tyear = {2024},\n}\n\n\n\n
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\n \n\n \n \n \n \n \n Graph Structure Reshaping Against Adversarial Attacks on Graph Neural Networks.\n \n \n \n\n\n \n Wang, H.; Zhou, C.; Chen, X.; Wu, J.; Pan, S.; Li, Z.; Wang, J.; and Philip, S Y.\n\n\n \n\n\n\n IEEE Transactions on Knowledge & Data Engineering (TKDE), (01): 1–14. 2024.\n Publisher: IEEE Computer Society\n\n\n\n
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@article{wang_graph_2024,\n\ttitle = {Graph {Structure} {Reshaping} {Against} {Adversarial} {Attacks} on {Graph} {Neural} {Networks}},\n\tcopyright = {All rights reserved},\n\tnumber = {01},\n\tjournal = {IEEE Transactions on Knowledge \\& Data Engineering (TKDE)},\n\tauthor = {Wang, Haibo and Zhou, Chuan and Chen, Xin and Wu, Jia and Pan, Shirui and Li, Zhao and Wang, Jilong and Philip, S Yu},\n\tyear = {2024},\n\tnote = {Publisher: IEEE Computer Society},\n\tpages = {1--14},\n}\n\n\n\n
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\n \n\n \n \n \n \n \n MADE: Multicurvature Adaptive Embedding for Temporal Knowledge Graph Completion.\n \n \n \n\n\n \n Wang, J.; Wang, B.; Gao, J.; Pan, S.; Liu, T.; Yin, B.; and Gao, W.\n\n\n \n\n\n\n IEEE Transactions on Cybernetics (TCYB). 2024.\n Publisher: IEEE\n\n\n\n
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@article{wang_made:_2024,\n\ttitle = {{MADE}: {Multicurvature} {Adaptive} {Embedding} for {Temporal} {Knowledge} {Graph} {Completion}},\n\tcopyright = {All rights reserved},\n\tjournal = {IEEE Transactions on Cybernetics (TCYB)},\n\tauthor = {Wang, Jiapu and Wang, Boyue and Gao, Junbin and Pan, Shirui and Liu, Tengfei and Yin, Baocai and Gao, Wen},\n\tyear = {2024},\n\tnote = {Publisher: IEEE},\n}\n\n\n\n
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\n \n\n \n \n \n \n \n Augmented Commonsense Knowledge for Remote Object Grounding.\n \n \n \n\n\n \n Mohammadi, B.; Hong, Y.; Qi, Y.; Wu, Q.; Pan, S.; and Shi, J. Q.\n\n\n \n\n\n\n In Proceedings of the AAAI Conference on Artificial Intelligence (AAAI), volume 38, pages 4269–4277, 2024. \n Issue: 5\n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@inproceedings{mohammadi_augmented_2024,\n\ttitle = {Augmented {Commonsense} {Knowledge} for {Remote} {Object} {Grounding}},\n\tvolume = {38},\n\tcopyright = {All rights reserved},\n\tbooktitle = {Proceedings of the {AAAI} {Conference} on {Artificial} {Intelligence} ({AAAI})},\n\tauthor = {Mohammadi, Bahram and Hong, Yicong and Qi, Yuankai and Wu, Qi and Pan, Shirui and Shi, Javen Qinfeng},\n\tyear = {2024},\n\tnote = {Issue: 5},\n\tpages = {4269--4277},\n}\n\n\n\n
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\n \n\n \n \n \n \n \n GOODAT: Towards Test-time Graph Out-of-Distribution Detection.\n \n \n \n\n\n \n Wang, L.; He, D.; Zhang, H.; Liu, Y.; Wang, W.; Pan, S.; Jin, D.; and Chua, T.\n\n\n \n\n\n\n In AAAI Conference on Artificial Intelligence (AAAI), 2024. \n \n\n\n\n
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@inproceedings{wang_goodat:_2024,\n\ttitle = {{GOODAT}: {Towards} {Test}-time {Graph} {Out}-of-{Distribution} {Detection}},\n\tcopyright = {All rights reserved},\n\tbooktitle = {{AAAI} {Conference} on {Artificial} {Intelligence} ({AAAI})},\n\tauthor = {Wang, Luzhi and He, Dongxiao and Zhang, He and Liu, Yixin and Wang, Wenjie and Pan, Shirui and Jin, Di and Chua, Tat-Seng},\n\tyear = {2024},\n}\n\n\n\n
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\n \n\n \n \n \n \n \n NestE: Modeling Nested Relational Structures for Knowledge Graph Reasoning.\n \n \n \n\n\n \n Xiong, B.; Nayyeri, M.; Luo, L.; Wang, Z.; Pan, S.; and Staab, S.\n\n\n \n\n\n\n In AAAI Conference on Artificial Intelligence (AAAI), 2024. \n \n\n\n\n
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@inproceedings{xiong_neste:_2024,\n\ttitle = {{NestE}: {Modeling} {Nested} {Relational} {Structures} for {Knowledge} {Graph} {Reasoning}},\n\tcopyright = {All rights reserved},\n\tbooktitle = {{AAAI} {Conference} on {Artificial} {Intelligence} ({AAAI})},\n\tauthor = {Xiong, Bo and Nayyeri, Mojtaba and Luo, Linhao and Wang, Zihao and Pan, Shirui and Staab, Steffen},\n\tyear = {2024},\n}\n\n\n\n
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\n \n\n \n \n \n \n \n Maximizing Malicious Influence in Node Injection Attack.\n \n \n \n\n\n \n Zhang, X.; Bao, P.; and Pan, S.\n\n\n \n\n\n\n In Proceedings of the 17th ACM International Conference on Web Search and Data Mining (WSDM), pages 958–966, 2024. \n \n\n\n\n
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@inproceedings{zhang_maximizing_2024,\n\ttitle = {Maximizing {Malicious} {Influence} in {Node} {Injection} {Attack}},\n\tcopyright = {All rights reserved},\n\tbooktitle = {Proceedings of the 17th {ACM} {International} {Conference} on {Web} {Search} and {Data} {Mining} ({WSDM})},\n\tauthor = {Zhang, Xiao and Bao, Peng and Pan, Shirui},\n\tyear = {2024},\n\tpages = {958--966},\n}\n\n\n\n
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\n \n\n \n \n \n \n \n Guest Editorial: Deep Neural Networks for Graphs: Theory, Models, Algorithms, and Applications.\n \n \n \n\n\n \n Li, M.; Micheli, A.; Wang, Y. G.; Pan, S.; Lió, P.; Gnecco, G. S.; and Sanguineti, M.\n\n\n \n\n\n\n IEEE Transactions on Neural Networks and Learning Systems (TNNLS), 35(4): 4367–4372. 2024.\n Publisher: IEEE\n\n\n\n
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@article{li_guest_2024,\n\ttitle = {Guest {Editorial}: {Deep} {Neural} {Networks} for {Graphs}: {Theory}, {Models}, {Algorithms}, and {Applications}},\n\tvolume = {35},\n\tcopyright = {All rights reserved},\n\tnumber = {4},\n\tjournal = {IEEE Transactions on Neural Networks and Learning Systems (TNNLS)},\n\tauthor = {Li, Ming and Micheli, Alessio and Wang, Yu Guang and Pan, Shirui and Lió, Pietro and Gnecco, Giorgio Stefano and Sanguineti, Marcello},\n\tyear = {2024},\n\tnote = {Publisher: IEEE},\n\tpages = {4367--4372},\n}\n\n\n\n
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\n \n\n \n \n \n \n \n Optimizing OOD Detection in Molecular Graphs: A Novel Approach with Diffusion Models.\n \n \n \n\n\n \n Shen, X.; Wang, Y.; Zhou, K.; Pan, S.; and Wang, X.\n\n\n \n\n\n\n In ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), 2024. \n \n\n\n\n
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@inproceedings{shen_optimizing_2024,\n\ttitle = {Optimizing {OOD} {Detection} in {Molecular} {Graphs}: {A} {Novel} {Approach} with {Diffusion} {Models}},\n\tcopyright = {All rights reserved},\n\tbooktitle = {{ACM} {SIGKDD} {Conference} on {Knowledge} {Discovery} and {Data} {Mining} ({KDD})},\n\tauthor = {Shen, Xu and Wang, Yili and Zhou, Kaixiong and Pan, Shirui and Wang, Xin},\n\tyear = {2024},\n}\n\n\n\n
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\n \n\n \n \n \n \n \n FedPFT: Federated Proxy Fine-Tuning of Foundation Models.\n \n \n \n\n\n \n Peng, Z.; Fan, X.; Chen, Y.; Wang, Z.; Pan, S.; Wen, C.; Zhang, R.; and Wang, C.\n\n\n \n\n\n\n In International Joint Conference on Artificial Intelligence (IJCAI), 2024. \n \n\n\n\n
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@inproceedings{peng_fedpft:_2024,\n\ttitle = {{FedPFT}: {Federated} {Proxy} {Fine}-{Tuning} of {Foundation} {Models}},\n\tcopyright = {All rights reserved},\n\tbooktitle = {International {Joint} {Conference} on {Artificial} {Intelligence} ({IJCAI})},\n\tauthor = {Peng, Zhaopeng and Fan, Xiaoliang and Chen, Yufan and Wang, Zheng and Pan, Shirui and Wen, Chenglu and Zhang, Ruisheng and Wang, Cheng},\n\tyear = {2024},\n}\n\n\n\n
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\n \n\n \n \n \n \n \n Foundation models for time series analysis: A tutorial and survey.\n \n \n \n\n\n \n Liang, Y.; Wen, H.; Nie, Y.; Jiang, Y.; Jin, M.; Song, D.; Pan, S.; and Wen, Q.\n\n\n \n\n\n\n In ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), 2024. \n \n\n\n\n
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@inproceedings{liang_foundation_2024,\n\ttitle = {Foundation models for time series analysis: {A} tutorial and survey},\n\tcopyright = {All rights reserved},\n\tbooktitle = {{ACM} {SIGKDD} {Conference} on {Knowledge} {Discovery} and {Data} {Mining} ({KDD})},\n\tauthor = {Liang, Yuxuan and Wen, Haomin and Nie, Yuqi and Jiang, Yushan and Jin, Ming and Song, Dongjin and Pan, Shirui and Wen, Qingsong},\n\tyear = {2024},\n}\n\n\n\n
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\n \n\n \n \n \n \n \n Towards Model Extraction Attacks in GAN-Based Image Translation via Domain Shift Mitigation.\n \n \n \n\n\n \n Mi, D.; Zhang, Y.; Zhang, L. Y.; Hu, S.; Zhong, Q.; Yuan, H.; and Pan, S.\n\n\n \n\n\n\n In AAAI Conference on Artificial Intelligence (AAAI), 2024. \n \n\n\n\n
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@inproceedings{mi_towards_2024,\n\ttitle = {Towards {Model} {Extraction} {Attacks} in {GAN}-{Based} {Image} {Translation} via {Domain} {Shift} {Mitigation}},\n\tcopyright = {All rights reserved},\n\tbooktitle = {{AAAI} {Conference} on {Artificial} {Intelligence} ({AAAI})},\n\tauthor = {Mi, Di and Zhang, Yanjun and Zhang, Leo Yu and Hu, Shengshan and Zhong, Qi and Yuan, Haizhuan and Pan, Shirui},\n\tyear = {2024},\n}\n\n\n\n
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\n \n\n \n \n \n \n \n Cost-effective Data Labelling for Graph Neural Networks.\n \n \n \n\n\n \n Huang, S.; Lee, G.; Bao, Z.; and Pan, S.\n\n\n \n\n\n\n In Proceedings of the ACM on Web Conference 2024 (WWW), pages 353–364, 2024. \n \n\n\n\n
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@inproceedings{huang_cost-effective_2024,\n\ttitle = {Cost-effective {Data} {Labelling} for {Graph} {Neural} {Networks}},\n\tcopyright = {All rights reserved},\n\tbooktitle = {Proceedings of the {ACM} on {Web} {Conference} 2024 ({WWW})},\n\tauthor = {Huang, Shixun and Lee, Ge and Bao, Zhifeng and Pan, Shirui},\n\tyear = {2024},\n\tpages = {353--364},\n}\n\n\n\n
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\n \n\n \n \n \n \n \n Gradformer: Graph Transformer with Exponential Decay.\n \n \n \n\n\n \n Liu, C.; Yao, Z.; Zhan, Y.; Ma, X.; Pan, S.; and Hu, W.\n\n\n \n\n\n\n In International Joint Conference on Artificial Intelligence (IJCAI), 2024. \n \n\n\n\n
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@inproceedings{liu_gradformer:_2024,\n\ttitle = {Gradformer: {Graph} {Transformer} with {Exponential} {Decay}},\n\tcopyright = {All rights reserved},\n\tbooktitle = {International {Joint} {Conference} on {Artificial} {Intelligence} ({IJCAI})},\n\tauthor = {Liu, Chuang and Yao, Zelin and Zhan, Yibing and Ma, Xueqi and Pan, Shirui and Hu, Wenbin},\n\tyear = {2024},\n}\n\n\n\n
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\n \n\n \n \n \n \n \n IME: Integrating Multi-curvature Shared and Specific Embedding for Temporal Knowledge Graph Completion.\n \n \n \n\n\n \n Wang, J.; Cui, Z.; Wang, B.; Pan, S.; Gao, J.; Yin, B.; and Gao, W.\n\n\n \n\n\n\n In The Web Conference (WWW), 2024. \n \n\n\n\n
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@inproceedings{wang_ime:_2024,\n\ttitle = {{IME}: {Integrating} {Multi}-curvature {Shared} and {Specific} {Embedding} for {Temporal} {Knowledge} {Graph} {Completion}},\n\tcopyright = {All rights reserved},\n\tbooktitle = {The {Web} {Conference} ({WWW})},\n\tauthor = {Wang, Jiapu and Cui, Zheng and Wang, Boyue and Pan, Shirui and Gao, Junbin and Yin, Baocai and Gao, Wen},\n\tyear = {2024},\n}\n\n\n\n
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\n \n\n \n \n \n \n \n Position Paper: What Can Large Language Models Tell Us about Time Series Analysis.\n \n \n \n\n\n \n Jin, M.; Zhang, Y.; Chen, W.; Zhang, K.; Liang, Y.; Yang, B.; Wang, J.; Pan, S.; and Wen, Q.\n\n\n \n\n\n\n In International Conference on Machine Learning (ICML), 2024. \n \n\n\n\n
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@inproceedings{jin_position_2024,\n\ttitle = {Position {Paper}: {What} {Can} {Large} {Language} {Models} {Tell} {Us} about {Time} {Series} {Analysis}},\n\tcopyright = {All rights reserved},\n\tbooktitle = {International {Conference} on {Machine} {Learning} ({ICML})},\n\tauthor = {Jin, Ming and Zhang, Yifan and Chen, Wei and Zhang, Kexin and Liang, Yuxuan and Yang, Bin and Wang, Jindong and Pan, Shirui and Wen, Qingsong},\n\tyear = {2024},\n}\n\n\n\n
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\n \n\n \n \n \n \n \n ROG _ \\PL\\ : Robust Open-Set Graph Learning via Region-Based Prototype Learning.\n \n \n \n\n\n \n Zhang, Q.; Li, X.; Lu, J.; Qiu, L.; Pan, S.; Chen, X.; and Chen, J.\n\n\n \n\n\n\n In AAAI Conference on Artificial Intelligence (AAAI), 2024. \n \n\n\n\n
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@inproceedings{zhang_rog_2024,\n\ttitle = {{ROG} \\_ \\{{PL}\\} : {Robust} {Open}-{Set} {Graph} {Learning} via {Region}-{Based} {Prototype} {Learning}},\n\tcopyright = {All rights reserved},\n\tbooktitle = {{AAAI} {Conference} on {Artificial} {Intelligence} ({AAAI})},\n\tauthor = {Zhang, Qin and Li, Xiaowei and Lu, Jiexin and Qiu, Liping and Pan, Shirui and Chen, Xiaojun and Chen, Junyang},\n\tyear = {2024},\n}\n\n\n\n
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\n \n\n \n \n \n \n \n Online gnn evaluation under test-time graph distribution shifts.\n \n \n \n\n\n \n Zheng, X.; Song, D.; Wen, Q.; Du, B.; and Pan, S.\n\n\n \n\n\n\n In International Conference on Learning Representations (ICLR), 2024. \n \n\n\n\n
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@inproceedings{zheng_online_2024,\n\ttitle = {Online gnn evaluation under test-time graph distribution shifts},\n\tcopyright = {All rights reserved},\n\tbooktitle = {International {Conference} on {Learning} {Representations} ({ICLR})},\n\tauthor = {Zheng, Xin and Song, Dongjin and Wen, Qingsong and Du, Bo and Pan, Shirui},\n\tyear = {2024},\n}\n\n\n\n
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\n \n\n \n \n \n \n \n Two heads are better than one: Boosting graph sparse training via semantic and topological awareness.\n \n \n \n\n\n \n Zhang, G.; Yue, Y.; Wang, K.; Fang, J.; Sui, Y.; Wang, K.; Liang, Y.; Cheng, D.; Pan, S.; and Chen, T.\n\n\n \n\n\n\n In International Conference on Machine Learning (ICML), 2024. \n \n\n\n\n
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@inproceedings{zhang_two_2024,\n\ttitle = {Two heads are better than one: {Boosting} graph sparse training via semantic and topological awareness},\n\tcopyright = {All rights reserved},\n\tbooktitle = {International {Conference} on {Machine} {Learning} ({ICML})},\n\tauthor = {Zhang, Guibin and Yue, Yanwei and Wang, Kun and Fang, Junfeng and Sui, Yongduo and Wang, Kai and Liang, Yuxuan and Cheng, Dawei and Pan, Shirui and Chen, Tianlong},\n\tyear = {2024},\n}\n\n\n\n
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\n \n\n \n \n \n \n \n Graph spatiotemporal process for multivariate time series anomaly detection with missing values.\n \n \n \n\n\n \n Zheng, Y.; Koh, H. Y.; Jin, M.; Chi, L.; Wang, H.; Phan, K. T; Chen, Y. P.; Pan, S.; and Xiang, W.\n\n\n \n\n\n\n Information Fusion, 106: 102255. 2024.\n Publisher: Elsevier\n\n\n\n
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@article{zheng_graph_2024,\n\ttitle = {Graph spatiotemporal process for multivariate time series anomaly detection with missing values},\n\tvolume = {106},\n\tcopyright = {All rights reserved},\n\tjournal = {Information Fusion},\n\tauthor = {Zheng, Yu and Koh, Huan Yee and Jin, Ming and Chi, Lianhua and Wang, Haishuai and Phan, Khoa T and Chen, Yi-Ping Phoebe and Pan, Shirui and Xiang, Wei},\n\tyear = {2024},\n\tnote = {Publisher: Elsevier},\n\tpages = {102255},\n}\n\n\n\n
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\n \n\n \n \n \n \n \n PLANNER: a multi-scale deep language model for the origins of replication site prediction.\n \n \n \n\n\n \n Wang, C.; He, Z.; Jia, R.; Pan, S.; Coin, L. J.; Song, J.; and Li, F.\n\n\n \n\n\n\n IEEE Journal of Biomedical and Health Informatics. 2024.\n Publisher: IEEE\n\n\n\n
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@article{wang_planner:_2024,\n\ttitle = {{PLANNER}: a multi-scale deep language model for the origins of replication site prediction},\n\tcopyright = {All rights reserved},\n\tjournal = {IEEE Journal of Biomedical and Health Informatics},\n\tauthor = {Wang, Cong and He, Zhijie and Jia, Runchang and Pan, Shirui and Coin, Lachlan JM and Song, Jiangning and Li, Fuyi},\n\tyear = {2024},\n\tnote = {Publisher: IEEE},\n}\n\n\n\n
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\n \n\n \n \n \n \n \n Knowledge Graphs and Pre-trained Language Models enhanced Representation Learning for Conversational Recommender Systems.\n \n \n \n\n\n \n Qiu, Z.; Tao, Y.; Pan, S.; and Liew, A. W.\n\n\n \n\n\n\n IEEE Transactions on Neural Networks and Learning Systems (TNNLS). 2024.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@article{qiu_knowledge_2024,\n\ttitle = {Knowledge {Graphs} and {Pre}-trained {Language} {Models} enhanced {Representation} {Learning} for {Conversational} {Recommender} {Systems}},\n\tcopyright = {All rights reserved},\n\tjournal = {IEEE Transactions on Neural Networks and Learning Systems (TNNLS)},\n\tauthor = {Qiu, Zhangchi and Tao, Ye and Pan, Shirui and Liew, Alan Wee-Chung},\n\tyear = {2024},\n}\n\n\n\n
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\n \n\n \n \n \n \n \n GraphGuard: Detecting and Counteracting Training Data Misuse in Graph Neural Networks.\n \n \n \n\n\n \n Wu, B.; Zhang, H.; Yang, X.; Wang, S.; Xue, M.; Pan, S.; and Yuan, X.\n\n\n \n\n\n\n In IEEE Symposium on Security and Privacy (S&P), 2024. \n \n\n\n\n
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@inproceedings{wu_graphguard:_2024,\n\ttitle = {{GraphGuard}: {Detecting} and {Counteracting} {Training} {Data} {Misuse} in {Graph} {Neural} {Networks}},\n\tcopyright = {All rights reserved},\n\tbooktitle = {{IEEE} {Symposium} on {Security} and {Privacy} ({S}\\&{P})},\n\tauthor = {Wu, Bang and Zhang, He and Yang, Xiangwen and Wang, Shuo and Xue, Minhui and Pan, Shirui and Yuan, Xingliang},\n\tyear = {2024},\n}\n\n\n\n
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\n \n\n \n \n \n \n \n Securing Graph Neural Networks in MLaaS: A Comprehensive Realization of Query-based Integrity Verification.\n \n \n \n\n\n \n Wu, B.; Yuan, X.; Wang, S.; Li, Q.; Xue, M.; and Pan, S.\n\n\n \n\n\n\n In The Network and Distributed System Security Symposium (NDSS), 2024. \n \n\n\n\n
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@inproceedings{wu_securing_2024,\n\ttitle = {Securing {Graph} {Neural} {Networks} in {MLaaS}: {A} {Comprehensive} {Realization} of {Query}-based {Integrity} {Verification}},\n\tcopyright = {All rights reserved},\n\tbooktitle = {The {Network} and {Distributed} {System} {Security} {Symposium} ({NDSS})},\n\tauthor = {Wu, Bang and Yuan, Xingliang and Wang, Shuo and Li, Qi and Xue, Minhui and Pan, Shirui},\n\tyear = {2024},\n}\n\n\n\n
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\n \n\n \n \n \n \n \n Reasoning on graphs: Faithful and interpretable large language model reasoning.\n \n \n \n\n\n \n Luo, L.; Li, Y.; Haffari, G.; and Pan, S.\n\n\n \n\n\n\n In International Conference on Learning Representations (ICLR), 2024. \n \n\n\n\n
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@inproceedings{luo_reasoning_2024,\n\ttitle = {Reasoning on graphs: {Faithful} and interpretable large language model reasoning},\n\tcopyright = {All rights reserved},\n\tbooktitle = {International {Conference} on {Learning} {Representations} ({ICLR})},\n\tauthor = {Luo, Linhao and Li, Yuan-Fang and Haffari, Gholamreza and Pan, Shirui},\n\tyear = {2024},\n}\n\n\n\n
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\n \n\n \n \n \n \n \n Self-supervised learning for time series analysis: Taxonomy, progress, and prospects.\n \n \n \n\n\n \n Zhang, K.; Wen, Q.; Zhang, C.; Cai, R.; Jin, M.; Liu, Y.; Zhang, J.; Liang, Y.; Pang, G.; Song, D.; and others\n\n\n \n\n\n\n IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI). 2024.\n \n\n\n\n
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@article{zhang_self-supervised_2024,\n\ttitle = {Self-supervised learning for time series analysis: {Taxonomy}, progress, and prospects},\n\tcopyright = {All rights reserved},\n\tjournal = {IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI)},\n\tauthor = {Zhang, Kexin and Wen, Qingsong and Zhang, Chaoli and Cai, Rongyao and Jin, Ming and Liu, Yong and Zhang, James and Liang, Yuxuan and Pang, Guansong and Song, Dongjin and {others}},\n\tyear = {2024},\n}\n\n\n\n
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\n \n\n \n \n \n \n \n Unifying Large Language Models and Knowledge Graphs: A Roadmap.\n \n \n \n\n\n \n Pan, S.; Luo, L.; Wang, Y.; Chen, C.; Wang, J.; and Wu, X.\n\n\n \n\n\n\n IEEE Transactions on Knowledge and Data Engineering (TKDE). 2024.\n \n\n\n\n
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@article{pan_unifying_2024,\n\ttitle = {Unifying {Large} {Language} {Models} and {Knowledge} {Graphs}: {A} {Roadmap}},\n\tcopyright = {All rights reserved},\n\tjournal = {IEEE Transactions on Knowledge and Data Engineering (TKDE)},\n\tauthor = {Pan, Shirui and Luo, Linhao and Wang, Yufei and Chen, Chen and Wang, Jiapu and Wu, Xindong},\n\tyear = {2024},\n}\n\n\n\n
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\n \n\n \n \n \n \n \n Contrastive Graph Similarity Networks.\n \n \n \n\n\n \n Wang, L.; Zheng, Y.; Jin, D.; Li, F.; Qiao, Y.; and Pan, S.\n\n\n \n\n\n\n ACM Transactions on the Web, 18(2): 1–20. 2024.\n Publisher: ACM New York, NY\n\n\n\n
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@article{wang_contrastive_2024,\n\ttitle = {Contrastive {Graph} {Similarity} {Networks}},\n\tvolume = {18},\n\tcopyright = {All rights reserved},\n\tnumber = {2},\n\tjournal = {ACM Transactions on the Web},\n\tauthor = {Wang, Luzhi and Zheng, Yizhen and Jin, Di and Li, Fuyi and Qiao, Yongliang and Pan, Shirui},\n\tyear = {2024},\n\tnote = {Publisher: ACM New York, NY},\n\tpages = {1--20},\n}\n\n\n\n
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\n \n\n \n \n \n \n \n Trustworthy graph neural networks: Aspects, methods and trends.\n \n \n \n\n\n \n Zhang, H.; Wu, B.; Yuan, X.; Pan, S.; Tong, H.; and Pei, J.\n\n\n \n\n\n\n Proceeding of the IEEE (PIEEE). 2024.\n \n\n\n\n
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@article{zhang_trustworthy_2024,\n\ttitle = {Trustworthy graph neural networks: {Aspects}, methods and trends},\n\tcopyright = {All rights reserved},\n\tjournal = {Proceeding of the IEEE (PIEEE)},\n\tauthor = {Zhang, He and Wu, Bang and Yuan, Xingliang and Pan, Shirui and Tong, Hanghang and Pei, Jian},\n\tyear = {2024},\n}\n\n\n\n
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\n  \n 2023\n \n \n (43)\n \n \n
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\n \n\n \n \n \n \n \n GNNEvaluator: Evaluating GNN Performance On Unseen Graphs Without Labels.\n \n \n \n\n\n \n Zheng, X.; Zhang, M.; Chen, C.; Molaei, S.; Zhou, C.; and Pan, S.\n\n\n \n\n\n\n In Advances in Neural Information Processing Systems, NeurIPS, 2023. \n \n\n\n\n
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@inproceedings{zheng_gnnevaluator:_2023,\n\ttitle = {{GNNEvaluator}: {Evaluating} {GNN} {Performance} {On} {Unseen} {Graphs} {Without} {Labels}},\n\tcopyright = {All rights reserved},\n\tbooktitle = {Advances in {Neural} {Information} {Processing} {Systems}, {NeurIPS}},\n\tauthor = {Zheng, Xin and Zhang, Miao and Chen, Chunyang and Molaei, Soheila and Zhou, Chuan and Pan, Shirui},\n\tyear = {2023},\n}\n\n\n\n
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\n \n\n \n \n \n \n \n Community preserving social recommendation with Cyclic Transfer Learning.\n \n \n \n\n\n \n Ni, X.; Xiong, F.; Pan, S.; Wu, J.; Wang, L.; and Chen, H.\n\n\n \n\n\n\n ACM Transactions on Information Systems (TOIS), 42(3): 1–36. 2023.\n Publisher: ACM New York, NY\n\n\n\n
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@article{ni_community_2023,\n\ttitle = {Community preserving social recommendation with {Cyclic} {Transfer} {Learning}},\n\tvolume = {42},\n\tcopyright = {All rights reserved},\n\tnumber = {3},\n\tjournal = {ACM Transactions on Information Systems (TOIS)},\n\tauthor = {Ni, Xuelian and Xiong, Fei and Pan, Shirui and Wu, Jia and Wang, Liang and Chen, Hongshu},\n\tyear = {2023},\n\tnote = {Publisher: ACM New York, NY},\n\tpages = {1--36},\n}\n\n\n\n
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\n \n\n \n \n \n \n \n Robust Network Alignment with the Combination of Structure and Attribute Embeddings.\n \n \n \n\n\n \n Peng, J.; Xiong, F.; Pan, S.; Wang, L.; and Xiong, X.\n\n\n \n\n\n\n In 2023 IEEE International Conference on Data Mining (ICDM), pages 498–507, 2023. IEEE\n \n\n\n\n
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@inproceedings{peng_robust_2023,\n\ttitle = {Robust {Network} {Alignment} with the {Combination} of {Structure} and {Attribute} {Embeddings}},\n\tcopyright = {All rights reserved},\n\tbooktitle = {2023 {IEEE} {International} {Conference} on {Data} {Mining} ({ICDM})},\n\tpublisher = {IEEE},\n\tauthor = {Peng, Jingkai and Xiong, Fei and Pan, Shirui and Wang, Liang and Xiong, Xi},\n\tyear = {2023},\n\tpages = {498--507},\n}\n\n\n\n
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\n \n\n \n \n \n \n \n G2Pxy: generative open-set node classification on graphs with proxy unknowns.\n \n \n \n\n\n \n Zhang, Q.; Shi, Z.; Zhang, X.; Chen, X.; Fournier-Viger, P.; and Pan, S.\n\n\n \n\n\n\n In International Joint Conference on Artificial Intelligence (IJCAI), pages 4576–4583, 2023. \n \n\n\n\n
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@inproceedings{zhang_g2pxy:_2023,\n\ttitle = {{G2Pxy}: generative open-set node classification on graphs with proxy unknowns},\n\tcopyright = {All rights reserved},\n\tbooktitle = {International {Joint} {Conference} on {Artificial} {Intelligence} ({IJCAI})},\n\tauthor = {Zhang, Qin and Shi, Zelin and Zhang, Xiaolin and Chen, Xiaojun and Fournier-Viger, Philippe and Pan, Shirui},\n\tyear = {2023},\n\tpages = {4576--4583},\n}\n\n\n\n
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\n \n\n \n \n \n \n \n Graph Convolutional Incomplete Multi-modal Hashing.\n \n \n \n\n\n \n Shen, X.; Chen, Y.; Pan, S.; Liu, W.; and Zheng, Y.\n\n\n \n\n\n\n In Proceedings of the 31st ACM International Conference on Multimedia (MM), pages 7029–7037, 2023. \n \n\n\n\n
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@inproceedings{shen_graph_2023,\n\ttitle = {Graph {Convolutional} {Incomplete} {Multi}-modal {Hashing}},\n\tcopyright = {All rights reserved},\n\tbooktitle = {Proceedings of the 31st {ACM} {International} {Conference} on {Multimedia} ({MM})},\n\tauthor = {Shen, Xiaobo and Chen, Yinfan and Pan, Shirui and Liu, Weiwei and Zheng, Yuhui},\n\tyear = {2023},\n\tpages = {7029--7037},\n}\n\n\n\n
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\n \n\n \n \n \n \n \n Towards Self-Interpretable Graph-Level Anomaly Detection.\n \n \n \n\n\n \n Liu, Y.; Ding, K.; Lu, Q.; Li, F.; Zhang, L. Y.; and Pan, S.\n\n\n \n\n\n\n In Advances in Neural Information Processing Systems, NeurIPS, 2023. \n \n\n\n\n
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@inproceedings{liu_towards_2023,\n\ttitle = {Towards {Self}-{Interpretable} {Graph}-{Level} {Anomaly} {Detection}},\n\tcopyright = {All rights reserved},\n\tbooktitle = {Advances in {Neural} {Information} {Processing} {Systems}, {NeurIPS}},\n\tauthor = {Liu, Yixin and Ding, Kaize and Lu, Qinghua and Li, Fuyi and Zhang, Leo Yu and Pan, Shirui},\n\tyear = {2023},\n}\n\n\n\n
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\n \n\n \n \n \n \n \n GoLoG: Global-To-Local Decoupling Graph Network with Joint Optimization for Hyperspectral Image Classification.\n \n \n \n\n\n \n Yang, B.; Ye, H.; Li, M.; Cao, F.; and Pan, S.\n\n\n \n\n\n\n IEEE Transactions on Geoscience and Remote Sensing (TGRS). 2023.\n Publisher: IEEE\n\n\n\n
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@article{yang_golog:_2023,\n\ttitle = {{GoLoG}: {Global}-{To}-{Local} {Decoupling} {Graph} {Network} with {Joint} {Optimization} for {Hyperspectral} {Image} {Classification}},\n\tcopyright = {All rights reserved},\n\tjournal = {IEEE Transactions on Geoscience and Remote Sensing (TGRS)},\n\tauthor = {Yang, Bing and Ye, Hailiang and Li, Ming and Cao, Feilong and Pan, Shirui},\n\tyear = {2023},\n\tnote = {Publisher: IEEE},\n}\n\n\n\n
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\n \n\n \n \n \n \n \n PREM: A Simple Yet Effective Approach for Node-Level Graph Anomaly Detection.\n \n \n \n\n\n \n Pan, J.; Liu, Y.; Zheng, Y.; and Pan, S.\n\n\n \n\n\n\n In IEEE International Conference on Data Mining (ICDM), 2023. \n \n\n\n\n
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@inproceedings{pan_prem:_2023,\n\ttitle = {{PREM}: {A} {Simple} {Yet} {Effective} {Approach} for {Node}-{Level} {Graph} {Anomaly} {Detection}},\n\tcopyright = {All rights reserved},\n\tbooktitle = {{IEEE} {International} {Conference} on {Data} {Mining} ({ICDM})},\n\tauthor = {Pan, Junjun and Liu, Yixin and Zheng, Yizhen and Pan, Shirui},\n\tyear = {2023},\n}\n\n\n\n
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\n \n\n \n \n \n \n \n Towards Flexible and Adaptive Neural Process for Cold-Start Recommendation.\n \n \n \n\n\n \n Lin, X.; Zhou, C.; Wu, J.; Zou, L.; Pan, S.; Cao, Y.; Wang, B.; Wang, S.; and Yin, D.\n\n\n \n\n\n\n IEEE Transactions on Knowledge and Data Engineering (TKDE). 2023.\n Publisher: IEEE\n\n\n\n
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@article{lin_towards_2023,\n\ttitle = {Towards {Flexible} and {Adaptive} {Neural} {Process} for {Cold}-{Start} {Recommendation}},\n\tcopyright = {All rights reserved},\n\tjournal = {IEEE Transactions on Knowledge and Data Engineering (TKDE)},\n\tauthor = {Lin, Xixun and Zhou, Chuan and Wu, Jia and Zou, Lixin and Pan, Shirui and Cao, Yanan and Wang, Bin and Wang, Shuaiqiang and Yin, Dawei},\n\tyear = {2023},\n\tnote = {Publisher: IEEE},\n}\n\n\n\n
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\n \n\n \n \n \n \n \n MAMDR: A model agnostic learning framework for multi-domain recommendation.\n \n \n \n\n\n \n Luo, L.; Li, Y.; Gao, B.; Tang, S.; Wang, S.; Li, J.; Zhu, T.; Liu, J.; Li, Z.; and Pan, S.\n\n\n \n\n\n\n In 2023 IEEE 39th International Conference on Data Engineering (ICDE), pages 3079–3092, 2023. IEEE\n \n\n\n\n
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@inproceedings{luo_mamdr:_2023,\n\ttitle = {{MAMDR}: {A} model agnostic learning framework for multi-domain recommendation},\n\tcopyright = {All rights reserved},\n\tbooktitle = {2023 {IEEE} 39th {International} {Conference} on {Data} {Engineering} ({ICDE})},\n\tpublisher = {IEEE},\n\tauthor = {Luo, Linhao and Li, Yumeng and Gao, Buyu and Tang, Shuai and Wang, Sinan and Li, Jiancheng and Zhu, Tanchao and Liu, Jiancai and Li, Zhao and Pan, Shirui},\n\tyear = {2023},\n\tpages = {3079--3092},\n}\n\n\n\n
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\n \n\n \n \n \n \n \n Correlation-aware Spatial-Temporal Graph Learning for Multivariate Time-series Anomaly Detection.\n \n \n \n\n\n \n Zheng, Y.; Koh, H. Y.; Jin, M.; Chi, L.; Phan, K. T; Pan, S.; Chen, Y. P.; and Xiang, W.\n\n\n \n\n\n\n IEEE Transactions on Neural Networks and Learning Systems (TNNLS). 2023.\n \n\n\n\n
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@article{zheng_correlation-aware_2023,\n\ttitle = {Correlation-aware {Spatial}-{Temporal} {Graph} {Learning} for {Multivariate} {Time}-series {Anomaly} {Detection}},\n\tcopyright = {All rights reserved},\n\tjournal = {IEEE Transactions on Neural Networks and Learning Systems (TNNLS)},\n\tauthor = {Zheng, Yu and Koh, Huan Yee and Jin, Ming and Chi, Lianhua and Phan, Khoa T and Pan, Shirui and Chen, Yi-Ping Phoebe and Xiang, Wei},\n\tyear = {2023},\n}\n\n\n\n
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\n \n\n \n \n \n \n \n Towards Few-shot Inductive Link Prediction on Knowledge Graphs: A Relational Anonymous Walk-guided Neural Process Approach.\n \n \n \n\n\n \n Zhao, Z.; Luo, L.; Pan, S.; Nguyen, Q. V. H.; and Gong, C.\n\n\n \n\n\n\n In ECML PKDD, 2023. \n \n\n\n\n
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@inproceedings{zhao_towards_2023,\n\ttitle = {Towards {Few}-shot {Inductive} {Link} {Prediction} on {Knowledge} {Graphs}: {A} {Relational} {Anonymous} {Walk}-guided {Neural} {Process} {Approach}},\n\tcopyright = {All rights reserved},\n\tbooktitle = {{ECML} {PKDD}},\n\tauthor = {Zhao, Zicheng and Luo, Linhao and Pan, Shirui and Nguyen, Quoc Viet Hung and Gong, Chen},\n\tyear = {2023},\n}\n\n\n\n
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\n \n\n \n \n \n \n \n Compatible Transformer for Irregularly Sampled Multivariate Time Series.\n \n \n \n\n\n \n Wei, Y.; Peng, J.; He, T.; Xu, C.; Zhang, J.; Pan, S.; and Chen, S.\n\n\n \n\n\n\n In IEEE International Conference on Data Mining (ICDM), 2023. \n \n\n\n\n
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@inproceedings{wei_compatible_2023,\n\ttitle = {Compatible {Transformer} for {Irregularly} {Sampled} {Multivariate} {Time} {Series}},\n\tcopyright = {All rights reserved},\n\tbooktitle = {{IEEE} {International} {Conference} on {Data} {Mining} ({ICDM})},\n\tauthor = {Wei, Yuxi and Peng, Juntong and He, Tong and Xu, Chenxin and Zhang, Jian and Pan, Shirui and Chen, Siheng},\n\tyear = {2023},\n}\n\n\n\n
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\n \n\n \n \n \n \n \n Domain-Adaptive Graph Attention-Supervised Network for Cross-Network Edge Classification.\n \n \n \n\n\n \n Shen, X.; Shao, M.; Pan, S.; Yang, L. T; and Zhou, X.\n\n\n \n\n\n\n IEEE Transactions on Neural Networks and Learning Systems (TNNLS). 2023.\n Publisher: IEEE\n\n\n\n
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@article{shen_domain-adaptive_2023,\n\ttitle = {Domain-{Adaptive} {Graph} {Attention}-{Supervised} {Network} for {Cross}-{Network} {Edge} {Classification}},\n\tcopyright = {All rights reserved},\n\tjournal = {IEEE Transactions on Neural Networks and Learning Systems (TNNLS)},\n\tauthor = {Shen, Xiao and Shao, Mengqiu and Pan, Shirui and Yang, Laurence T and Zhou, Xi},\n\tyear = {2023},\n\tnote = {Publisher: IEEE},\n}\n\n\n\n
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\n \n\n \n \n \n \n \n Boosting Graph Contrastive Learning via Adaptive Sampling.\n \n \n \n\n\n \n Wan, S.; Zhan, Y.; Chen, S.; Pan, S.; Yang, J.; Tao, D.; and Gong, C.\n\n\n \n\n\n\n IEEE Transactions on Neural Networks and Learning Systems (TNNLS). 2023.\n Publisher: IEEE\n\n\n\n
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@article{wan_boosting_2023,\n\ttitle = {Boosting {Graph} {Contrastive} {Learning} via {Adaptive} {Sampling}},\n\tcopyright = {All rights reserved},\n\tjournal = {IEEE Transactions on Neural Networks and Learning Systems (TNNLS)},\n\tauthor = {Wan, Sheng and Zhan, Yibing and Chen, Shuo and Pan, Shirui and Yang, Jian and Tao, Dacheng and Gong, Chen},\n\tyear = {2023},\n\tnote = {Publisher: IEEE},\n}\n\n\n\n
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\n \n\n \n \n \n \n \n Demystifying Uneven Vulnerability of Link Stealing Attacks against Graph Neural Networks.\n \n \n \n\n\n \n Zhang, H.; Wu, B.; Wang, S.; Yang, X.; Xue, M.; Pan, S.; and YUAN, X.\n\n\n \n\n\n\n In International Conference on Machine Learning (ICML), 2023. \n \n\n\n\n
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@inproceedings{zhang_demystifying_2023,\n\ttitle = {Demystifying {Uneven} {Vulnerability} of {Link} {Stealing} {Attacks} against {Graph} {Neural} {Networks}},\n\tcopyright = {All rights reserved},\n\tbooktitle = {International {Conference} on {Machine} {Learning} ({ICML})},\n\tauthor = {Zhang, He and Wu, Bang and Wang, Shuo and Yang, Xiangwen and Xue, Minhui and Pan, Shirui and YUAN, Xingliang},\n\tyear = {2023},\n}\n\n\n\n
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\n \n\n \n \n \n \n \n Finding the Missing-half: Graph Complementary Learning for Homophily-prone and Heterophily-prone Graphs.\n \n \n \n\n\n \n Zheng, Y.; Zhang, H.; Lee, V.; Zheng, Y.; Wang, X.; and Pan, S.\n\n\n \n\n\n\n In International Conference on Machine Learning (ICML), 2023. \n \n\n\n\n
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@inproceedings{zheng_finding_2023,\n\ttitle = {Finding the {Missing}-half: {Graph} {Complementary} {Learning} for {Homophily}-prone and {Heterophily}-prone {Graphs}},\n\tcopyright = {All rights reserved},\n\tbooktitle = {International {Conference} on {Machine} {Learning} ({ICML})},\n\tauthor = {Zheng, Yizhen and Zhang, He and Lee, Vincent and Zheng, Yu and Wang, Xiao and Pan, Shirui},\n\tyear = {2023},\n}\n\n\n\n
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\n \n\n \n \n \n \n \n Structure-free Graph Condensation: From Large-scale Graphs to Condensed Graph-free Data.\n \n \n \n\n\n \n Zheng, X.; Zhang, M.; Chen, C.; Nguyen, Q. V. H.; Zhu, X.; and Pan, S.\n\n\n \n\n\n\n In Advances in Neural Information Processing Systems, NeurIPS, 2023. \n \n\n\n\n
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@inproceedings{zheng_structure-free_2023,\n\ttitle = {Structure-free {Graph} {Condensation}: {From} {Large}-scale {Graphs} to {Condensed} {Graph}-free {Data}},\n\tcopyright = {All rights reserved},\n\tbooktitle = {Advances in {Neural} {Information} {Processing} {Systems}, {NeurIPS}},\n\tauthor = {Zheng, Xin and Zhang, Miao and Chen, Chunyang and Nguyen, Quoc Viet Hung and Zhu, Xingquan and Pan, Shirui},\n\tyear = {2023},\n}\n\n\n\n
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\n \n\n \n \n \n \n \n Shrinking Embeddings for Hyper-Relational Knowledge Graphs.\n \n \n \n\n\n \n Xiong, B.; Nayyer, M.; Pan, S.; and Staab, S.\n\n\n \n\n\n\n In Annual Meeting of the Association for Computational Linguistics (ACL), 2023. \n \n\n\n\n
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@inproceedings{xiong_shrinking_2023,\n\ttitle = {Shrinking {Embeddings} for {Hyper}-{Relational} {Knowledge} {Graphs}},\n\tcopyright = {All rights reserved},\n\tbooktitle = {Annual {Meeting} of the {Association} for {Computational} {Linguistics} ({ACL})},\n\tauthor = {Xiong, Bo and Nayyer, Mojtaba and Pan, Shirui and Staab, Steffen},\n\tyear = {2023},\n}\n\n\n\n
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\n \n\n \n \n \n \n \n A Survey on Fairness-aware Recommender Systems.\n \n \n \n\n\n \n Jin, D.; Wang, L.; Zhang, H.; Zheng, Y.; Ding, W.; Xia, F.; and Pan, S.\n\n\n \n\n\n\n Information Fusion. 2023.\n \n\n\n\n
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@article{jin_survey_2023,\n\ttitle = {A {Survey} on {Fairness}-aware {Recommender} {Systems}},\n\tcopyright = {All rights reserved},\n\tjournal = {Information Fusion},\n\tauthor = {Jin, Di and Wang, Luzhi and Zhang, He and Zheng, Yizhen and Ding, Weiping and Xia, Feng and Pan, Shirui},\n\tyear = {2023},\n}\n\n\n\n
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\n \n\n \n \n \n \n \n Learning Strong Graph Neural Networks with Weak Information.\n \n \n \n\n\n \n Liu, Y.; Ding, K.; Wang, J.; Lee, V.; Liu, H.; and Pan, S.\n\n\n \n\n\n\n In ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), 2023. \n \n\n\n\n
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@inproceedings{liu_learning_2023,\n\ttitle = {Learning {Strong} {Graph} {Neural} {Networks} with {Weak} {Information}},\n\tcopyright = {All rights reserved},\n\tbooktitle = {{ACM} {SIGKDD} {Conference} on {Knowledge} {Discovery} and {Data} {Mining} ({KDD})},\n\tauthor = {Liu, Yixin and Ding, Kaize and Wang, Jianling and Lee, Vincent and Liu, Huan and Pan, Shirui},\n\tyear = {2023},\n}\n\n\n\n
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\n \n\n \n \n \n \n \n ATTIC is an integrated approach for predicting A-to-I RNA editing sites in three species.\n \n \n \n\n\n \n Chen, R.; Li, F.; Guo, X.; Bi, Y.; Li, C.; Pan, S.; Coin, L. J.; and Song, J.\n\n\n \n\n\n\n Briefings in Bioinformatics, 24(3): bbad170. 2023.\n Publisher: Oxford University Press\n\n\n\n
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@article{chen_attic_2023,\n\ttitle = {{ATTIC} is an integrated approach for predicting {A}-to-{I} {RNA} editing sites in three species},\n\tvolume = {24},\n\tcopyright = {All rights reserved},\n\tnumber = {3},\n\tjournal = {Briefings in Bioinformatics},\n\tauthor = {Chen, Ruyi and Li, Fuyi and Guo, Xudong and Bi, Yue and Li, Chen and Pan, Shirui and Coin, Lachlan JM and Song, Jiangning},\n\tyear = {2023},\n\tnote = {Publisher: Oxford University Press},\n\tpages = {bbad170},\n}\n\n\n\n
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\n \n\n \n \n \n \n \n A Minimal Approach for Natural Language Action Space in Text-based Games.\n \n \n \n\n\n \n Ryu, D. K.; Fang, M.; Pan, S.; Haffari, G.; and Shareghi, E.\n\n\n \n\n\n\n In Conference on Computational Natural Language Learning (CoNLL), 2023. \n \n\n\n\n
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@inproceedings{ryu_minimal_2023,\n\ttitle = {A {Minimal} {Approach} for {Natural} {Language} {Action} {Space} in {Text}-based {Games}},\n\tcopyright = {All rights reserved},\n\tbooktitle = {Conference on {Computational} {Natural} {Language} {Learning} ({CoNLL})},\n\tauthor = {Ryu, Dongwon Kelvin and Fang, Meng and Pan, Shirui and Haffari, Gholamreza and Shareghi, Ehsan},\n\tyear = {2023},\n}\n\n\n\n
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\n \n\n \n \n \n \n \n Normalizing Flow-based Neural Process for Few-Shot Knowledge Graph Completion.\n \n \n \n\n\n \n Luo, L.; Li, Y.; Haffari, G.; and Pan, S.\n\n\n \n\n\n\n In ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR), 2023. \n \n\n\n\n
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@inproceedings{luo_normalizing_2023,\n\ttitle = {Normalizing {Flow}-based {Neural} {Process} for {Few}-{Shot} {Knowledge} {Graph} {Completion}},\n\tcopyright = {All rights reserved},\n\tbooktitle = {{ACM} {SIGIR} {Conference} on {Research} and {Development} in {Information} {Retrieval} ({SIGIR})},\n\tauthor = {Luo, Linhao and Li, Yuan-Fang and Haffari, Gholamreza and Pan, Shirui},\n\tyear = {2023},\n}\n\n\n\n
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\n \n\n \n \n \n \n \n Neighbor Contrastive Learning on Learnable Graph Augmentation.\n \n \n \n\n\n \n Shen, X.; Sun, D.; Pan, S.; Zhou, X.; and Yang, L. T\n\n\n \n\n\n\n In AAAI Conference on Artificial Intelligence (AAAI), 2023. \n \n\n\n\n
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@inproceedings{shen_neighbor_2023,\n\ttitle = {Neighbor {Contrastive} {Learning} on {Learnable} {Graph} {Augmentation}},\n\tcopyright = {All rights reserved},\n\tbooktitle = {{AAAI} {Conference} on {Artificial} {Intelligence} ({AAAI})},\n\tauthor = {Shen, Xiao and Sun, Dewang and Pan, Shirui and Zhou, Xi and Yang, Laurence T},\n\tyear = {2023},\n}\n\n\n\n
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\n \n\n \n \n \n \n \n Graph Sequential Neural ODE Process for Link Prediction on Dynamic and Sparse Graphs.\n \n \n \n\n\n \n Luo, L.; Haffari, R.; and Pan, S.\n\n\n \n\n\n\n In ACM International Conference on Web Search and Data Mining (WSDM), 2023. \n \n\n\n\n
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@inproceedings{luo_graph_2023,\n\ttitle = {Graph {Sequential} {Neural} {ODE} {Process} for {Link} {Prediction} on {Dynamic} and {Sparse} {Graphs}},\n\tcopyright = {All rights reserved},\n\tbooktitle = {{ACM} {International} {Conference} on {Web} {Search} and {Data} {Mining} ({WSDM})},\n\tauthor = {Luo, Linhao and Haffari, Reza and Pan, Shirui},\n\tyear = {2023},\n}\n\n\n\n
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\n \n\n \n \n \n \n \n Dual Intent Enhanced Graph Neural Network for Session-based New Item Recommendation.\n \n \n \n\n\n \n Jin, D.; Wang, L.; Zheng, Y.; Song, G.; Jiang, F.; Li, X.; Lin, W.; and Pan, S.\n\n\n \n\n\n\n In Proceedings of the ACM Web Conference, WWW 2023, pages 684–693, 2023. \n \n\n\n\n
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@inproceedings{jin_dual_2023,\n\ttitle = {Dual {Intent} {Enhanced} {Graph} {Neural} {Network} for {Session}-based {New} {Item} {Recommendation}},\n\tcopyright = {All rights reserved},\n\tbooktitle = {Proceedings of the {ACM} {Web} {Conference}, {WWW} 2023},\n\tauthor = {Jin, Di and Wang, Luzhi and Zheng, Yizhen and Song, Guojie and Jiang, Fei and Li, Xiang and Lin, Wei and Pan, Shirui},\n\tyear = {2023},\n\tpages = {684--693},\n}\n\n\n\n
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\n \n\n \n \n \n \n \n Auto-HeG: Automated Graph Neural Network on Heterophilic Graphs.\n \n \n \n\n\n \n Zheng, X.; Zhang, M.; Chen, C.; Zhang, Q.; Zhou, C.; and Pan, S.\n\n\n \n\n\n\n In Proceedings of the ACM Web Conference, WWW 2023, 2023. \n \n\n\n\n
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@inproceedings{zheng_auto-heg:_2023,\n\ttitle = {Auto-{HeG}: {Automated} {Graph} {Neural} {Network} on {Heterophilic} {Graphs}},\n\tcopyright = {All rights reserved},\n\tbooktitle = {Proceedings of the {ACM} {Web} {Conference}, {WWW} 2023},\n\tauthor = {Zheng, Xin and Zhang, Miao and Chen, Chunyang and Zhang, Qin and Zhou, Chuan and Pan, Shirui},\n\tyear = {2023},\n}\n\n\n\n
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\n \n\n \n \n \n \n \n TxAllo: Dynamic Transaction Allocation in Sharded Blockchain Systems.\n \n \n \n\n\n \n Zhang, Y.; Pan, S.; and Yu, J.\n\n\n \n\n\n\n In IEEE International Conference on Data Engineering (ICDE), 2023. \n \n\n\n\n
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@inproceedings{zhang_txallo:_2023,\n\ttitle = {{TxAllo}: {Dynamic} {Transaction} {Allocation} in {Sharded} {Blockchain} {Systems}},\n\tcopyright = {All rights reserved},\n\tbooktitle = {{IEEE} {International} {Conference} on {Data} {Engineering} ({ICDE})},\n\tauthor = {Zhang, Yuanzhe and Pan, Shirui and Yu, Jiangshan},\n\tyear = {2023},\n}\n\n\n\n
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\n \n\n \n \n \n \n \n Beyond Smoothing: Unsupervised Graph Representation Learning with Edge Heterophily Discriminating.\n \n \n \n\n\n \n Liu, Y.; Zheng, Y.; Zhang, D.; Lee, V.; and Pan, S.\n\n\n \n\n\n\n In AAAI Conference on Artificial Intelligence (AAAI), 2023. \n \n\n\n\n
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@inproceedings{liu_beyond_2023,\n\ttitle = {Beyond {Smoothing}: {Unsupervised} {Graph} {Representation} {Learning} with {Edge} {Heterophily} {Discriminating}},\n\tcopyright = {All rights reserved},\n\tbooktitle = {{AAAI} {Conference} on {Artificial} {Intelligence} ({AAAI})},\n\tauthor = {Liu, Yixin and Zheng, Yizhen and Zhang, Daokun and Lee, Vincent and Pan, Shirui},\n\tyear = {2023},\n}\n\n\n\n
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\n \n\n \n \n \n \n \n A Comprehensive Survey on Distributed Training of Graph Neural Networks.\n \n \n \n\n\n \n Lin, H.; Yan, M.; Ye, X.; Fan, D.; Pan, S.; Chen, W.; and Xie, Y.\n\n\n \n\n\n\n Proceedings of the IEEE (PIEEE). 2023.\n \n\n\n\n
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@article{lin_comprehensive_2023,\n\ttitle = {A {Comprehensive} {Survey} on {Distributed} {Training} of {Graph} {Neural} {Networks}},\n\tcopyright = {All rights reserved},\n\tjournal = {Proceedings of the IEEE (PIEEE)},\n\tauthor = {Lin, Haiyang and Yan, Mingyu and Ye, Xiaochun and Fan, Dongrui and Pan, Shirui and Chen, Wenguang and Xie, Yuan},\n\tyear = {2023},\n}\n\n\n\n
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\n \n\n \n \n \n \n \n GOOD-D: On Unsupervised Graph Out-Of-Distribution Detection.\n \n \n \n\n\n \n Liu, Y.; Ding, K.; Liu, H.; and Pan, S.\n\n\n \n\n\n\n In ACM International Conference on Web Search and Data Mining (WSDM), 2023. \n \n\n\n\n
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@inproceedings{liu_good-d:_2023,\n\ttitle = {{GOOD}-{D}: {On} {Unsupervised} {Graph} {Out}-{Of}-{Distribution} {Detection}},\n\tcopyright = {All rights reserved},\n\tbooktitle = {{ACM} {International} {Conference} on {Web} {Search} and {Data} {Mining} ({WSDM})},\n\tauthor = {Liu, Yixin and Ding, Kaize and Liu, Huan and Pan, Shirui},\n\tyear = {2023},\n}\n\n\n\n
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\n \n\n \n \n \n \n \n Explainable Hyperbolic Temporal Point Process for User-Item Interaction Sequence Generation.\n \n \n \n\n\n \n Zhou, Y.; Cao, Y.; Shang, Y.; Zhou, C.; Pan, S.; Lin, Z.; and Li, Q.\n\n\n \n\n\n\n ACM Transactions on Information Systems (TOIS), 41(4): 1–26. 2023.\n Publisher: ACM\n\n\n\n
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@article{zhou_explainable_2023,\n\ttitle = {Explainable {Hyperbolic} {Temporal} {Point} {Process} for {User}-{Item} {Interaction} {Sequence} {Generation}},\n\tvolume = {41},\n\tcopyright = {All rights reserved},\n\tnumber = {4},\n\tjournal = {ACM Transactions on Information Systems (TOIS)},\n\tauthor = {Zhou, Yuchen and Cao, Yanan and Shang, Yanmin and Zhou, Chuan and Pan, Shirui and Lin, Zheng and Li, Qian},\n\tyear = {2023},\n\tnote = {Publisher: ACM},\n\tpages = {1--26},\n}\n\n\n\n
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\n \n\n \n \n \n \n \n Fast Heterogeneous Federated Learning with Hybrid Client Selection.\n \n \n \n\n\n \n Shen, G.; Gao, D.; Song, D.; Zhou, X.; Pan, S.; Lou, W.; and Zhou, F.\n\n\n \n\n\n\n In The Conference on Uncertainty in Artificial Intelligence (UAI), 2023. \n \n\n\n\n
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@inproceedings{shen_fast_2023,\n\ttitle = {Fast {Heterogeneous} {Federated} {Learning} with {Hybrid} {Client} {Selection}},\n\tcopyright = {All rights reserved},\n\tbooktitle = {The {Conference} on {Uncertainty} in {Artificial} {Intelligence} ({UAI})},\n\tauthor = {Shen, Guangyuan and Gao, Dehong and Song, DuanXiao and Zhou, Xukai and Pan, Shirui and Lou, Wei and Zhou, Fang},\n\tyear = {2023},\n}\n\n\n\n
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\n \n\n \n \n \n \n \n Simple and Efficient Heterogeneous Graph Neural Network.\n \n \n \n\n\n \n Yang, X.; Yan, M.; Pan, S.; Ye, X.; and Fan, D.\n\n\n \n\n\n\n In AAAI Conference on Artificial Intelligence (AAAI), 2023. \n \n\n\n\n
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@inproceedings{yang_simple_2023,\n\ttitle = {Simple and {Efficient} {Heterogeneous} {Graph} {Neural} {Network}},\n\tcopyright = {All rights reserved},\n\tbooktitle = {{AAAI} {Conference} on {Artificial} {Intelligence} ({AAAI})},\n\tauthor = {Yang, Xiaocheng and Yan, Mingyu and Pan, Shirui and Ye, Xiaochun and Fan, Dongrui},\n\tyear = {2023},\n}\n\n\n\n
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\n \n\n \n \n \n \n \n Robust Graph Representation Learning for Local Corruption Recovery.\n \n \n \n\n\n \n Zhou, B.; Jiang, Y.; Wang, Y. G.; Liang, J.; Gao, J.; Pan, S.; and Zhang, X.\n\n\n \n\n\n\n In Proceedings of the ACM Web Conference, WWW 2023, 2023. \n \n\n\n\n
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@inproceedings{zhou_robust_2023,\n\ttitle = {Robust {Graph} {Representation} {Learning} for {Local} {Corruption} {Recovery}},\n\tcopyright = {All rights reserved},\n\tbooktitle = {Proceedings of the {ACM} {Web} {Conference}, {WWW} 2023},\n\tauthor = {Zhou, Bingxin and Jiang, Yuanhong and Wang, Yu Guang and Liang, Jingwei and Gao, Junbin and Pan, Shirui and Zhang, Xiaoqun},\n\tyear = {2023},\n}\n\n\n\n
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\n \n\n \n \n \n \n \n Spatio-temporal joint graph convolutional networks for traffic forecasting.\n \n \n \n\n\n \n Zheng, C.; Fan, X.; Pan, S.; Wu, Z.; Wang, C.; and Yu, P. S\n\n\n \n\n\n\n IEEE Transactions on Knowledge and Data Engineering (TKDE). 2023.\n \n\n\n\n
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@article{zheng_spatio-temporal_2023,\n\ttitle = {Spatio-temporal joint graph convolutional networks for traffic forecasting},\n\tcopyright = {All rights reserved},\n\tjournal = {IEEE Transactions on Knowledge and Data Engineering (TKDE)},\n\tauthor = {Zheng, Chuanpan and Fan, Xiaoliang and Pan, Shirui and Wu, Zonghan and Wang, Cheng and Yu, Philip S},\n\tyear = {2023},\n}\n\n\n\n
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\n \n\n \n \n \n \n \n Graph Sequential Neural ODE Process for Link Prediction on Dynamic and Sparse Graphs.\n \n \n \n\n\n \n Luo, L.; Haffari, G.; and Pan, S.\n\n\n \n\n\n\n In Chua, T.; Lauw, H. W.; Si, L.; Terzi, E.; and Tsaparas, P., editor(s), Proceedings of the Sixteenth ACM International Conference on Web Search and Data Mining, WSDM 2023, Singapore, 27 February 2023 - 3 March 2023, pages 778–786, 2023. ACM\n \n\n\n\n
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@inproceedings{luo_graph_2023,\n\ttitle = {Graph {Sequential} {Neural} {ODE} {Process} for {Link} {Prediction} on {Dynamic} and {Sparse} {Graphs}},\n\tcopyright = {All rights reserved},\n\tdoi = {10.1145/3539597.3570465},\n\tbooktitle = {Proceedings of the {Sixteenth} {ACM} {International} {Conference} on {Web} {Search} and {Data} {Mining}, {WSDM} 2023, {Singapore}, 27 {February} 2023 - 3 {March} 2023},\n\tpublisher = {ACM},\n\tauthor = {Luo, Linhao and Haffari, Gholamreza and Pan, Shirui},\n\teditor = {Chua, Tat-Seng and Lauw, Hady W. and Si, Luo and Terzi, Evimaria and Tsaparas, Panayiotis},\n\tyear = {2023},\n\tpages = {778--786},\n}\n\n\n\n
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\n \n\n \n \n \n \n \n GOOD-D: On Unsupervised Graph Out-Of-Distribution Detection.\n \n \n \n\n\n \n Liu, Y.; Ding, K.; Liu, H.; and Pan, S.\n\n\n \n\n\n\n In Chua, T.; Lauw, H. W.; Si, L.; Terzi, E.; and Tsaparas, P., editor(s), Proceedings of the Sixteenth ACM International Conference on Web Search and Data Mining, WSDM 2023, Singapore, 27 February 2023 - 3 March 2023, pages 339–347, 2023. ACM\n \n\n\n\n
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@inproceedings{liu_good-d:_2023,\n\ttitle = {{GOOD}-{D}: {On} {Unsupervised} {Graph} {Out}-{Of}-{Distribution} {Detection}},\n\tcopyright = {All rights reserved},\n\tdoi = {10.1145/3539597.3570446},\n\tbooktitle = {Proceedings of the {Sixteenth} {ACM} {International} {Conference} on {Web} {Search} and {Data} {Mining}, {WSDM} 2023, {Singapore}, 27 {February} 2023 - 3 {March} 2023},\n\tpublisher = {ACM},\n\tauthor = {Liu, Yixin and Ding, Kaize and Liu, Huan and Pan, Shirui},\n\teditor = {Chua, Tat-Seng and Lauw, Hady W. and Si, Luo and Terzi, Evimaria and Tsaparas, Panayiotis},\n\tyear = {2023},\n\tpages = {339--347},\n}\n\n\n\n
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\n \n\n \n \n \n \n \n A Survey of Community Detection Approaches: From Statistical Modeling to Deep Learning.\n \n \n \n\n\n \n Jin, D.; Yu, Z.; Jiao, P.; Pan, S.; He, D.; Wu, J.; Yu, P.; and Zhang, W.\n\n\n \n\n\n\n IEEE Transactions on Knowledge and Data Engineering (TKDE), 35(2): 1149–1170. 2023.\n \n\n\n\n
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@article{jin_survey_2023,\n\ttitle = {A {Survey} of {Community} {Detection} {Approaches}: {From} {Statistical} {Modeling} to {Deep} {Learning}},\n\tvolume = {35},\n\tcopyright = {All rights reserved},\n\tdoi = {10.1109/TKDE.2021.3104155 (Impact Factor: 6.977; JCR Ranked Q1; Top Journal in Data Mining)},\n\tnumber = {2},\n\tjournal = {IEEE Transactions on Knowledge and Data Engineering (TKDE)},\n\tauthor = {Jin, Di and Yu, Zhizhi and Jiao, Pengfei and Pan, Shirui and He, Dongxiao and Wu, Jia and Yu, Philip and Zhang, Weixiong},\n\tyear = {2023},\n\tpages = {1149--1170},\n}\n\n\n\n
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\n \n\n \n \n \n \n \n Learning Graph Representations With Maximal Cliques.\n \n \n \n\n\n \n Molaei, S.; Bousejin, N. G.; Zare, H.; Jalili, M.; and Pan, S.\n\n\n \n\n\n\n IEEE Transactions on Neural Networks and Learning Systems (TNNLS), 34(2): 1089–1096. 2023.\n \n\n\n\n
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@article{molaei_learning_2023,\n\ttitle = {Learning {Graph} {Representations} {With} {Maximal} {Cliques}},\n\tvolume = {34},\n\tcopyright = {All rights reserved},\n\tdoi = {10.1109/TNNLS.2021.3104901 (Impact Factor: 10.451; JCR Ranked Q1)},\n\tnumber = {2},\n\tjournal = {IEEE Transactions on Neural Networks and Learning Systems (TNNLS)},\n\tauthor = {Molaei, Soheila and Bousejin, Nima Ghanbari and Zare, Hadi and Jalili, Mahdi and Pan, Shirui},\n\tyear = {2023},\n\tpages = {1089--1096},\n}\n\n\n\n
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\n \n\n \n \n \n \n \n An Empirical Survey on Long Document Summarization: Datasets, Models and Metrics.\n \n \n \n\n\n \n Koh, H. Y.; Ju, J.; Liu, M.; and Pan, S.\n\n\n \n\n\n\n ACM Computing Surveys (CSUR), 55(8): 154:1–35. 2023.\n \n\n\n\n
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@article{koh_empirical_2023,\n\ttitle = {An {Empirical} {Survey} on {Long} {Document} {Summarization}: {Datasets}, {Models} and {Metrics}},\n\tvolume = {55},\n\tcopyright = {All rights reserved},\n\tdoi = {10.1145/3545176},\n\tnumber = {8},\n\tjournal = {ACM Computing Surveys (CSUR)},\n\tauthor = {Koh, Huan Yee and Ju, Jiaxin and Liu, Ming and Pan, Shirui},\n\tyear = {2023},\n\tpages = {154:1--35},\n}\n\n\n\n
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\n \n\n \n \n \n \n \n Reinforced, Incremental and Cross-lingual Event Detection From Social Messages.\n \n \n \n\n\n \n Peng, H.; Zhang, R.; Li, S.; Cao, Y.; Pan, S.; and Yu, P. S\n\n\n \n\n\n\n IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 45(1): 980–998. 2023.\n \n\n\n\n
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@article{peng_reinforced_2023,\n\ttitle = {Reinforced, {Incremental} and {Cross}-lingual {Event} {Detection} {From} {Social} {Messages}},\n\tvolume = {45},\n\tcopyright = {All rights reserved},\n\tdoi = {10.1109/TPAMI.2022.3144993 (Impact Factor: 16.389; JCR Ranked Q1; Top Journal in AI)},\n\tnumber = {1},\n\tjournal = {IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI)},\n\tauthor = {Peng, Hao and Zhang, Ruitong and Li, Shaoning and Cao, Yuwei and Pan, Shirui and Yu, Philip S},\n\tyear = {2023},\n\tpages = {980--998},\n}\n\n\n\n
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\n \n\n \n \n \n \n \n Reinforced, incremental and cross-lingual event detection from social messages.\n \n \n \n\n\n \n Peng, H.; Zhang, R.; Li, S.; Cao, Y.; Pan, S.; and Philip, S Y.\n\n\n \n\n\n\n IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 45(1): 980–998. 2022.\n Publisher: IEEE\n\n\n\n
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@article{peng_reinforced_2022,\n\ttitle = {Reinforced, incremental and cross-lingual event detection from social messages},\n\tvolume = {45},\n\tcopyright = {All rights reserved},\n\tnumber = {1},\n\tjournal = {IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI)},\n\tauthor = {Peng, Hao and Zhang, Ruitong and Li, Shaoning and Cao, Yuwei and Pan, Shirui and Philip, S Yu},\n\tyear = {2022},\n\tnote = {Publisher: IEEE},\n\tpages = {980--998},\n}\n\n\n\n
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\n \n\n \n \n \n \n \n Neural Temporal Walks: Motif-Aware Representation Learning on Continuous-Time Dynamic Graphs.\n \n \n \n\n\n \n Jin, M.; Li, Y.; and Pan, S.\n\n\n \n\n\n\n In Advances in Neural Information Processing Systems, NeurIPS-22, 2022. Advances in Neural Information Processing Systems, NeurIPS-22\n \n\n\n\n
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@inproceedings{jin_neural_2022,\n\ttitle = {Neural {Temporal} {Walks}: {Motif}-{Aware} {Representation} {Learning} on {Continuous}-{Time} {Dynamic} {Graphs}},\n\tcopyright = {All rights reserved},\n\tbooktitle = {Advances in {Neural} {Information} {Processing} {Systems}, {NeurIPS}-22},\n\tpublisher = {Advances in Neural Information Processing Systems, NeurIPS-22},\n\tauthor = {Jin, Ming and Li, Yuan-Fang and Pan, Shirui},\n\tyear = {2022},\n}\n\n\n\n
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\n \n\n \n \n \n \n \n Multi-relational graph neural architecture search with fine-grained message passing.\n \n \n \n\n\n \n Zheng, X.; Zhang, M.; Chen, C.; Li, C.; Zhou, C.; and Pan, S.\n\n\n \n\n\n\n In 2022 IEEE International Conference on Data Mining (ICDM), pages 783–792, 2022. IEEE\n \n\n\n\n
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@inproceedings{zheng_multi-relational_2022,\n\ttitle = {Multi-relational graph neural architecture search with fine-grained message passing},\n\tcopyright = {All rights reserved},\n\tbooktitle = {2022 {IEEE} {International} {Conference} on {Data} {Mining} ({ICDM})},\n\tpublisher = {IEEE},\n\tauthor = {Zheng, Xin and Zhang, Miao and Chen, Chunyang and Li, Chaojie and Zhou, Chuan and Pan, Shirui},\n\tyear = {2022},\n\tpages = {783--792},\n}\n\n\n\n
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\n \n\n \n \n \n \n \n A Dynamic Variational Framework for Open-World Node Classification in Structured Sequences.\n \n \n \n\n\n \n Zhang, Q.; Li, Q.; Chen, X.; Zhang, P.; Pan, S.; Fournier-Viger, P.; and Huang, J. Z.\n\n\n \n\n\n\n In IEEE International Conference on Data Mining (ICDM), 2022. \n \n\n\n\n
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@inproceedings{zhang_dynamic_2022,\n\ttitle = {A {Dynamic} {Variational} {Framework} for {Open}-{World} {Node} {Classification} in {Structured} {Sequences}},\n\tcopyright = {All rights reserved},\n\tbooktitle = {{IEEE} {International} {Conference} on {Data} {Mining} ({ICDM})},\n\tauthor = {Zhang, Qin and Li, Qincai and Chen, Xiaojun and Zhang, Peng and Pan, Shirui and Fournier-Viger, Philippe and Huang, Joshua Zhexue},\n\tyear = {2022},\n}\n\n\n\n
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\n \n\n \n \n \n \n \n How Far are We from Robust Long Abstractive Summarization?.\n \n \n \n\n\n \n Koh, H. Y.; Ju, J.; Zhang, H.; Liu, M.; and Pan, S.\n\n\n \n\n\n\n In 2022 Conference on Empirical Methods in Natural Language Processing (EMNLP), 2022. \n \n\n\n\n
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@inproceedings{koh_how_2022,\n\ttitle = {How {Far} are {We} from {Robust} {Long} {Abstractive} {Summarization}?},\n\tcopyright = {All rights reserved},\n\tbooktitle = {2022 {Conference} on {Empirical} {Methods} in {Natural} {Language} {Processing} ({EMNLP})},\n\tauthor = {Koh, Huan Yee and Ju, Jiaxin and Zhang, He and Liu, Ming and Pan, Shirui},\n\tyear = {2022},\n}\n\n\n\n
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\n \n\n \n \n \n \n \n Unifying Graph Contrastive Learning with Flexible Contextual Scopes.\n \n \n \n\n\n \n Zheng, Y.; Zheng, Y.; Zhou, X.; Gong, C.; Lee, V.; and Pan, S.\n\n\n \n\n\n\n In IEEE International Conference on Data Mining (ICDM), 2022. \n \n\n\n\n
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@inproceedings{zheng_unifying_2022,\n\ttitle = {Unifying {Graph} {Contrastive} {Learning} with {Flexible} {Contextual} {Scopes}},\n\tcopyright = {All rights reserved},\n\tbooktitle = {{IEEE} {International} {Conference} on {Data} {Mining} ({ICDM})},\n\tauthor = {Zheng, Yizhen and Zheng, Yu and Zhou, Xiaofei and Gong, Chen and Lee, Vincent and Pan, Shirui},\n\tyear = {2022},\n}\n\n\n\n
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\n \n\n \n \n \n \n \n StarSum: A star architecture based model for extractive summarization.\n \n \n \n\n\n \n Shi, K.; Cai, X.; Yang, L.; Zhao, J.; and Pan, S.\n\n\n \n\n\n\n IEEE/ACM Transactions on Audio, Speech, and Language Processing, 30: 3020–3031. 2022.\n Publisher: IEEE\n\n\n\n
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@article{shi_starsum:_2022,\n\ttitle = {{StarSum}: {A} star architecture based model for extractive summarization},\n\tvolume = {30},\n\tcopyright = {All rights reserved},\n\tjournal = {IEEE/ACM Transactions on Audio, Speech, and Language Processing},\n\tauthor = {Shi, Kaile and Cai, Xiaoyan and Yang, Libin and Zhao, Jintao and Pan, Shirui},\n\tyear = {2022},\n\tnote = {Publisher: IEEE},\n\tpages = {3020--3031},\n}\n\n\n\n
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\n \n\n \n \n \n \n \n Towards spatio-temporal aware traffic time series forecasting.\n \n \n \n\n\n \n Cirstea, R.; Yang, B.; Guo, C.; Kieu, T.; and Pan, S.\n\n\n \n\n\n\n In 2022 IEEE 38th International Conference on Data Engineering (ICDE), pages 2900–2913, 2022. IEEE\n \n\n\n\n
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@inproceedings{cirstea_towards_2022,\n\ttitle = {Towards spatio-temporal aware traffic time series forecasting},\n\tcopyright = {All rights reserved},\n\tbooktitle = {2022 {IEEE} 38th {International} {Conference} on {Data} {Engineering} ({ICDE})},\n\tpublisher = {IEEE},\n\tauthor = {Cirstea, Razvan-Gabriel and Yang, Bin and Guo, Chenjuan and Kieu, Tung and Pan, Shirui},\n\tyear = {2022},\n\tpages = {2900--2913},\n}\n\n\n\n
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\n \n\n \n \n \n \n \n A Probabilistic Graphical Model Based on Neural-Symbolic Reasoning for Visual Relationship Detection.\n \n \n \n\n\n \n Yu, D.; Yang, B.; Wei, Q.; Li, A.; and Pan, S.\n\n\n \n\n\n\n In IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR-22), pages 10609–10618, 2022. \n \n\n\n\n
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@inproceedings{yu_probabilistic_2022,\n\ttitle = {A {Probabilistic} {Graphical} {Model} {Based} on {Neural}-{Symbolic} {Reasoning} for {Visual} {Relationship} {Detection}},\n\tcopyright = {All rights reserved},\n\tbooktitle = {{IEEE}/{CVF} {Conference} on {Computer} {Vision} and {Pattern} {Recognition} ({CVPR}-22)},\n\tauthor = {Yu, Dongran and Yang, Bo and Wei, Qianhao and Li, Anchen and Pan, Shirui},\n\tyear = {2022},\n\tpages = {10609--10618},\n}\n\n\n\n
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\n \n\n \n \n \n \n \n Ultrahyperbolic Knowledge Graph Embeddings.\n \n \n \n\n\n \n Xiong, B.; Zhu, S.; Nayyeri, M.; Xu, C.; Pan, S.; Zhou, C.; and Staab, S.\n\n\n \n\n\n\n In ACM SIGKDD Conference on Knowledge Discovery and Data Mining, KDD-22, 2022. \n \n\n\n\n
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@inproceedings{xiong_ultrahyperbolic_2022,\n\ttitle = {Ultrahyperbolic {Knowledge} {Graph} {Embeddings}},\n\tcopyright = {All rights reserved},\n\tbooktitle = {{ACM} {SIGKDD} {Conference} on {Knowledge} {Discovery} and {Data} {Mining}, {KDD}-22},\n\tauthor = {Xiong, Bo and Zhu, Shichao and Nayyeri, Mojtaba and Xu, Chengjin and Pan, Shirui and Zhou, Chuan and Staab, Steffen},\n\tyear = {2022},\n}\n\n\n\n
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\n \n\n \n \n \n \n \n An Empirical Survey on Long Document Summarization: Datasets, Models and Metrics.\n \n \n \n\n\n \n Koh, H. Y.; Ju, J.; Liu, M.; and Pan, S.\n\n\n \n\n\n\n ACM Computing Surveys (CSUR). 2022.\n Publisher: ACM\n\n\n\n
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@article{koh_empirical_2022,\n\ttitle = {An {Empirical} {Survey} on {Long} {Document} {Summarization}: {Datasets}, {Models} and {Metrics}},\n\tcopyright = {All rights reserved},\n\tjournal = {ACM Computing Surveys (CSUR)},\n\tauthor = {Koh, Huan Yee and Ju, Jiaxin and Liu, Ming and Pan, Shirui},\n\tyear = {2022},\n\tnote = {Publisher: ACM},\n}\n\n\n\n
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\n \n\n \n \n \n \n \n Rethinking and Scaling Up Graph Contrastive Learning: An Extremely Efficient Approach with Group Discrimination.\n \n \n \n\n\n \n Zheng, Y.; Pan, S.; Lee, V. C.; Zheng, Y.; and Yu, P. S\n\n\n \n\n\n\n In Advances in Neural Information Processing Systems, NeurIPS-22, 2022. \n \n\n\n\n
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@inproceedings{zheng_rethinking_2022,\n\ttitle = {Rethinking and {Scaling} {Up} {Graph} {Contrastive} {Learning}: {An} {Extremely} {Efficient} {Approach} with {Group} {Discrimination}},\n\tcopyright = {All rights reserved},\n\tbooktitle = {Advances in {Neural} {Information} {Processing} {Systems}, {NeurIPS}-22},\n\tauthor = {Zheng, Yizhen and Pan, Shirui and Lee, Vincent Cs and Zheng, Yu and Yu, Philip S},\n\tyear = {2022},\n}\n\n\n\n
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\n \n\n \n \n \n \n \n Cross-modal Clinical Graph Transformer for Ophthalmic Report Generation.\n \n \n \n\n\n \n Li, M.; Cai, W.; Verspoor, K.; Pan, S.; Liang, X.; and Chang, X.\n\n\n \n\n\n\n In IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR-22), pages 20656–20665, 2022. \n \n\n\n\n
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@inproceedings{li_cross-modal_2022,\n\ttitle = {Cross-modal {Clinical} {Graph} {Transformer} for {Ophthalmic} {Report} {Generation}},\n\tcopyright = {All rights reserved},\n\tbooktitle = {{IEEE}/{CVF} {Conference} on {Computer} {Vision} and {Pattern} {Recognition} ({CVPR}-22)},\n\tauthor = {Li, Mingjie and Cai, Wenjia and Verspoor, Karin and Pan, Shirui and Liang, Xiaodan and Chang, Xiaojun},\n\tyear = {2022},\n\tpages = {20656--20665},\n}\n\n\n\n
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\n \n\n \n \n \n \n \n Task Scheduling in Three-Dimensional Spatial Crowdsourcing: A Social Welfare Perspective.\n \n \n \n\n\n \n Wang, L.; Yang, D.; Yu, Z.; Xiong, F.; Han, L.; Pan, S.; and Guo, B.\n\n\n \n\n\n\n IEEE Transactions on Mobile Computing. 2022.\n Publisher: IEEE\n\n\n\n
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@article{wang_task_2022,\n\ttitle = {Task {Scheduling} in {Three}-{Dimensional} {Spatial} {Crowdsourcing}: {A} {Social} {Welfare} {Perspective}},\n\tcopyright = {All rights reserved},\n\tjournal = {IEEE Transactions on Mobile Computing},\n\tauthor = {Wang, Liang and Yang, Dingqi and Yu, Zhiwen and Xiong, Fei and Han, Lei and Pan, Shirui and Guo, Bin},\n\tyear = {2022},\n\tnote = {Publisher: IEEE},\n}\n\n\n\n
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\n \n\n \n \n \n \n \n Fire Burns, Sword Cuts: Commonsense Inductive Bias for Exploration in Text-based Games.\n \n \n \n\n\n \n Ryu, D.; Shareghi, E.; Fang, M.; Xu, Y.; Pan, S.; and Haf, R.\n\n\n \n\n\n\n In Annual Meeting of the Association for Computational Linguistics (ACL), volume 2, pages 515–522, 2022. \n \n\n\n\n
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@inproceedings{ryu_fire_2022,\n\ttitle = {Fire {Burns}, {Sword} {Cuts}: {Commonsense} {Inductive} {Bias} for {Exploration} in {Text}-based {Games}},\n\tvolume = {2},\n\tcopyright = {All rights reserved},\n\tbooktitle = {Annual {Meeting} of the {Association} for {Computational} {Linguistics} ({ACL})},\n\tauthor = {Ryu, Dongwon and Shareghi, Ehsan and Fang, Meng and Xu, Yunqiu and Pan, Shirui and Haf, Reza},\n\tyear = {2022},\n\tpages = {515--522},\n}\n\n\n\n
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\n \n\n \n \n \n \n \n CGMN: A Contrastive Graph Matching Network for Self-Supervised Graph Similarity Learning.\n \n \n \n\n\n \n Jin, D.; Wang, L.; Zheng, Y.; Li, X.; Jiang, F.; Lin, W.; and Pan, S.\n\n\n \n\n\n\n In International Joint Conference on Artificial Intelligence, IJCAI 2022, 2022. \n \n\n\n\n
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@inproceedings{jin_cgmn:_2022,\n\ttitle = {{CGMN}: {A} {Contrastive} {Graph} {Matching} {Network} for {Self}-{Supervised} {Graph} {Similarity} {Learning}},\n\tcopyright = {All rights reserved},\n\tbooktitle = {International {Joint} {Conference} on {Artificial} {Intelligence}, {IJCAI} 2022},\n\tauthor = {Jin, Di and Wang, Luzhi and Zheng, Yizhen and Li, Xiang and Jiang, Fei and Lin, Wei and Pan, Shirui},\n\tyear = {2022},\n}\n\n\n\n
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\n \n\n \n \n \n \n \n Triformer: Triangular, Variable-Specific Attentions for Long Sequence Multivariate Time Series Forecasting–Full Version.\n \n \n \n\n\n \n Cirstea, R.; Guo, C.; Yang, B.; Kieu, T.; Dong, X.; and Pan, S.\n\n\n \n\n\n\n In International Joint Conference on Artificial Intelligence, IJCAI 2022, 2022. \n \n\n\n\n
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@inproceedings{cirstea_triformer:_2022,\n\ttitle = {Triformer: {Triangular}, {Variable}-{Specific} {Attentions} for {Long} {Sequence} {Multivariate} {Time} {Series} {Forecasting}–{Full} {Version}},\n\tcopyright = {All rights reserved},\n\tbooktitle = {International {Joint} {Conference} on {Artificial} {Intelligence}, {IJCAI} 2022},\n\tauthor = {Cirstea, Razvan-Gabriel and Guo, Chenjuan and Yang, Bin and Kieu, Tung and Dong, Xuanyi and Pan, Shirui},\n\tyear = {2022},\n}\n\n\n\n
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\n \n\n \n \n \n \n \n Cyclic transfer learning for recommender systems with heterogeneous feedbacks.\n \n \n \n\n\n \n Ni, X.; Xiong, F.; Hu, Y.; Pan, S.; Chen, H.; and Wang, L.\n\n\n \n\n\n\n In Proceedings of the 2022 SIAM International Conference on Data Mining (SDM), pages 567–575, 2022. Society for Industrial and Applied Mathematics\n \n\n\n\n
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@inproceedings{ni_cyclic_2022,\n\ttitle = {Cyclic transfer learning for recommender systems with heterogeneous feedbacks},\n\tcopyright = {All rights reserved},\n\tbooktitle = {Proceedings of the 2022 {SIAM} {International} {Conference} on {Data} {Mining} ({SDM})},\n\tpublisher = {Society for Industrial and Applied Mathematics},\n\tauthor = {Ni, Xuelian and Xiong, Fei and Hu, Yutian and Pan, Shirui and Chen, Hongshu and Wang, Liang},\n\tyear = {2022},\n\tpages = {567--575},\n}\n\n\n\n
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\n \n\n \n \n \n \n \n Fine-grained attributed graph clustering.\n \n \n \n\n\n \n Kang, Z.; Liu, Z.; Pan, S.; and Tian, L.\n\n\n \n\n\n\n In Proceedings of the 2022 SIAM International Conference on Data Mining (SDM), pages 370–378, 2022. Society for Industrial and Applied Mathematics\n \n\n\n\n
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@inproceedings{kang_fine-grained_2022,\n\ttitle = {Fine-grained attributed graph clustering},\n\tcopyright = {All rights reserved},\n\tbooktitle = {Proceedings of the 2022 {SIAM} {International} {Conference} on {Data} {Mining} ({SDM})},\n\tpublisher = {Society for Industrial and Applied Mathematics},\n\tauthor = {Kang, Zhao and Liu, Zhanyu and Pan, Shirui and Tian, Ling},\n\tyear = {2022},\n\tpages = {370--378},\n}\n\n\n\n
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\n \n\n \n \n \n \n \n Exploring Relational Semantics for Inductive Knowledge Graph Completion.\n \n \n \n\n\n \n Wang, C.; Zhou, X.; Pan, S.; Dong, L.; Song, Z.; and Sha, Y.\n\n\n \n\n\n\n In AAAI Conference on Artificial Intelligence, AAAI-22, 2022. \n \n\n\n\n
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@inproceedings{wang_exploring_2022,\n\ttitle = {Exploring {Relational} {Semantics} for {Inductive} {Knowledge} {Graph} {Completion}},\n\tcopyright = {All rights reserved},\n\tbooktitle = {{AAAI} {Conference} on {Artificial} {Intelligence}, {AAAI}-22},\n\tauthor = {Wang, Changjian and Zhou, Xiaofei and Pan, Shirui and Dong, Linhua and Song, Zeliang and Sha, Ying},\n\tyear = {2022},\n}\n\n\n\n
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\n \n\n \n \n \n \n \n Attraction and repulsion: Unsupervised domain adaptive graph contrastive learning network.\n \n \n \n\n\n \n Wu, M.; Pan, S.; and Zhu, X.\n\n\n \n\n\n\n IEEE Transactions on Emerging Topics in Computational Intelligence, 6(5): 1079–1091. 2022.\n Publisher: IEEE\n\n\n\n
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@article{wu_attraction_2022,\n\ttitle = {Attraction and repulsion: {Unsupervised} domain adaptive graph contrastive learning network},\n\tvolume = {6},\n\tcopyright = {All rights reserved},\n\tnumber = {5},\n\tjournal = {IEEE Transactions on Emerging Topics in Computational Intelligence},\n\tauthor = {Wu, Man and Pan, Shirui and Zhu, Xingquan},\n\tyear = {2022},\n\tnote = {Publisher: IEEE},\n\tpages = {1079--1091},\n}\n\n\n\n
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\n \n\n \n \n \n \n \n Survey on Graph Neural Network Acceleration: An Algorithmic Perspective.\n \n \n \n\n\n \n Liu, X.; Yan, M.; Deng, L.; Li, G.; Ye, X.; Fan, D.; Pan, S.; and Xie, Y.\n\n\n \n\n\n\n In International Joint Conference on Artificial Intelligence, IJCAI 2022, 2022. \n \n\n\n\n
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@inproceedings{liu_survey_2022,\n\ttitle = {Survey on {Graph} {Neural} {Network} {Acceleration}: {An} {Algorithmic} {Perspective}},\n\tcopyright = {All rights reserved},\n\tbooktitle = {International {Joint} {Conference} on {Artificial} {Intelligence}, {IJCAI} 2022},\n\tauthor = {Liu, Xin and Yan, Mingyu and Deng, Lei and Li, Guoqi and Ye, Xiaochun and Fan, Dongrui and Pan, Shirui and Xie, Yuan},\n\tyear = {2022},\n}\n\n\n\n
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\n \n\n \n \n \n \n \n Predicting Best-Selling New Products in a Major Promotion Campaign Through Graph Convolutional Networks.\n \n \n \n\n\n \n Li, C.; Jiang, W.; Yang, Y.; Pan, S.; Huang, G.; and Guo, L.\n\n\n \n\n\n\n IEEE Transactions on Neural Networks and Learning Systems (TNNLS). 2022.\n Publisher: IEEE\n\n\n\n
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@article{li_predicting_2022,\n\ttitle = {Predicting {Best}-{Selling} {New} {Products} in a {Major} {Promotion} {Campaign} {Through} {Graph} {Convolutional} {Networks}},\n\tcopyright = {All rights reserved},\n\tjournal = {IEEE Transactions on Neural Networks and Learning Systems (TNNLS)},\n\tauthor = {Li, Chaojie and Jiang, Wensen and Yang, Yin and Pan, Shirui and Huang, Gang and Guo, Lijie},\n\tyear = {2022},\n\tnote = {Publisher: IEEE},\n}\n\n\n\n
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\n \n\n \n \n \n \n \n Projective Ranking-based GNN Evasion Attacks.\n \n \n \n\n\n \n Zhang, H.; Yuan, X.; Zhou, C.; and Pan, S.\n\n\n \n\n\n\n IEEE Transactions on Knowledge and Data Engineering (TKDE). 2022.\n \n\n\n\n
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@article{zhang_projective_2022,\n\ttitle = {Projective {Ranking}-based {GNN} {Evasion} {Attacks}},\n\tcopyright = {All rights reserved},\n\tjournal = {IEEE Transactions on Knowledge and Data Engineering (TKDE)},\n\tauthor = {Zhang, He and Yuan, Xingliang and Zhou, Chuan and Pan, Shirui},\n\tyear = {2022},\n}\n\n\n\n
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\n \n\n \n \n \n \n \n Multivariate Time Series Forecasting with Dynamic Graph Neural ODEs.\n \n \n \n\n\n \n Jin, M.; Zheng, Y.; Li, Y.; Chen, S.; Yang, B.; and Pan, S.\n\n\n \n\n\n\n IEEE Transactions on Knowledge and Data Engineering (TKDE). 2022.\n \n\n\n\n
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@article{jin_multivariate_2022,\n\ttitle = {Multivariate {Time} {Series} {Forecasting} with {Dynamic} {Graph} {Neural} {ODEs}},\n\tcopyright = {All rights reserved},\n\tjournal = {IEEE Transactions on Knowledge and Data Engineering (TKDE)},\n\tauthor = {Jin, Ming and Zheng, Yu and Li, Yuan-Fang and Chen, Siheng and Yang, Bin and Pan, Shirui},\n\tyear = {2022},\n}\n\n\n\n
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\n \n\n \n \n \n \n \n Predicting Human Mobility via Graph Convolutional Dual-attentive Networks.\n \n \n \n\n\n \n Dang, W.; Wang, H.; Pan, S.; Zhang, P.; Zhou, C.; Chen, X.; and Wang, J.\n\n\n \n\n\n\n In ACM International Conference on Web Search and Data Mining (WSDM), pages 192–200, 2022. \n \n\n\n\n
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@inproceedings{dang_predicting_2022,\n\ttitle = {Predicting {Human} {Mobility} via {Graph} {Convolutional} {Dual}-attentive {Networks}},\n\tcopyright = {All rights reserved},\n\tbooktitle = {{ACM} {International} {Conference} on {Web} {Search} and {Data} {Mining} ({WSDM})},\n\tauthor = {Dang, Weizhen and Wang, Haibo and Pan, Shirui and Zhang, Pei and Zhou, Chuan and Chen, Xin and Wang, Jilong},\n\tyear = {2022},\n\tpages = {192--200},\n}\n\n\n\n
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\n \n\n \n \n \n \n \n Dual Space Graph Contrastive Learning.\n \n \n \n\n\n \n Yang, H.; Chen, H.; Pan, S.; Li, L.; Yu, P. S; and Xu, G.\n\n\n \n\n\n\n In The Web Conference (WWW-2022), 2022. \n \n\n\n\n
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@inproceedings{yang_dual_2022,\n\ttitle = {Dual {Space} {Graph} {Contrastive} {Learning}},\n\tcopyright = {All rights reserved},\n\tbooktitle = {The {Web} {Conference} ({WWW}-2022)},\n\tauthor = {Yang, Haoran and Chen, Hongxu and Pan, Shirui and Li, Lin and Yu, Philip S and Xu, Guandong},\n\tyear = {2022},\n}\n\n\n\n
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\n \n\n \n \n \n \n \n Towards Unsupervised Deep Graph Structure Learning.\n \n \n \n\n\n \n Liu, Y.; Zheng, Y.; Zhang, D.; Chen, H.; Peng, H.; and Pan, S.\n\n\n \n\n\n\n In The Web Conference (WWW-2022), 2022. \n \n\n\n\n
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@inproceedings{liu_towards_2022,\n\ttitle = {Towards {Unsupervised} {Deep} {Graph} {Structure} {Learning}},\n\tcopyright = {All rights reserved},\n\tbooktitle = {The {Web} {Conference} ({WWW}-2022)},\n\tauthor = {Liu, Yixin and Zheng, Yu and Zhang, Daokun and Chen, Hongxu and Peng, Hao and Pan, Shirui},\n\tyear = {2022},\n}\n\n\n\n
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\n \n\n \n \n \n \n \n BaLeNAS: Differentiable Architecture Search via the Bayesian Learning Rule.\n \n \n \n\n\n \n Zhang, M.; Hu, J.; Su, S.; Pan, S.; Chang, X.; Yang, B.; and Haffari, G.\n\n\n \n\n\n\n In IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR-22), 2022. \n \n\n\n\n
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@inproceedings{zhang_balenas:_2022,\n\ttitle = {{BaLeNAS}: {Differentiable} {Architecture} {Search} via the {Bayesian} {Learning} {Rule}},\n\tcopyright = {All rights reserved},\n\tbooktitle = {{IEEE}/{CVF} {Conference} on {Computer} {Vision} and {Pattern} {Recognition} ({CVPR}-22)},\n\tauthor = {Zhang, Miao and Hu, Jilin and Su, Steven and Pan, Shirui and Chang, Xiaojun and Yang, Bin and Haffari, Gholamreza},\n\tyear = {2022},\n}\n\n\n\n
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\n \n\n \n \n \n \n \n Multi-Graph Fusion Networks for Urban Region Embedding.\n \n \n \n\n\n \n Wu, S.; Yan, X.; Fan, X.; Pan, S.; Zhu, S.; Zheng, C.; Cheng, M.; and Wang, C.\n\n\n \n\n\n\n In International Joint Conference on Artificial Intelligence, IJCAI 2022, 2022. \n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@inproceedings{wu_multi-graph_2022,\n\ttitle = {Multi-{Graph} {Fusion} {Networks} for {Urban} {Region} {Embedding}},\n\tcopyright = {All rights reserved},\n\tbooktitle = {International {Joint} {Conference} on {Artificial} {Intelligence}, {IJCAI} 2022},\n\tauthor = {Wu, Shangbin and Yan, Xu and Fan, Xiaoliang and Pan, Shirui and Zhu, Shichao and Zheng, Chuanpan and Cheng, Ming and Wang, Cheng},\n\tyear = {2022},\n}\n\n\n\n
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\n \n\n \n \n \n \n \n Positive-unlabeled learning in bioinformatics and computational biology: a brief review.\n \n \n \n\n\n \n Li, F.; Dong, S.; Leier, A.; Han, M.; Guo, X.; Xu, J.; Wang, X.; Pan, S.; Jia, C.; Zhang, Y.; and others\n\n\n \n\n\n\n Briefings in bioinformatics, 23(1): bbab461. 2022.\n Publisher: Oxford University Press\n\n\n\n
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@article{li_positive-unlabeled_2022,\n\ttitle = {Positive-unlabeled learning in bioinformatics and computational biology: a brief review},\n\tvolume = {23},\n\tcopyright = {All rights reserved},\n\tnumber = {1},\n\tjournal = {Briefings in bioinformatics},\n\tauthor = {Li, Fuyi and Dong, Shuangyu and Leier, André and Han, Meiya and Guo, Xudong and Xu, Jing and Wang, Xiaoyu and Pan, Shirui and Jia, Cangzhi and Zhang, Yang and {others}},\n\tyear = {2022},\n\tnote = {Publisher: Oxford University Press},\n\tpages = {bbab461},\n}\n\n\n\n
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\n \n\n \n \n \n \n \n TraverseNet: Unifying Space and Time in Message Passing for Traffic Forecasting.\n \n \n \n\n\n \n Wu, Z.; Zheng, D.; Pan, S.; Gan, Q.; Long, G.; and Karypis, G.\n\n\n \n\n\n\n IEEE Transactions on Neural Networks and Learning Systems (TNNLS). 2022.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@article{wu_traversenet:_2022,\n\ttitle = {{TraverseNet}: {Unifying} {Space} and {Time} in {Message} {Passing} for {Traffic} {Forecasting}},\n\tcopyright = {All rights reserved},\n\tjournal = {IEEE Transactions on Neural Networks and Learning Systems (TNNLS)},\n\tauthor = {Wu, Zonghan and Zheng, Da and Pan, Shirui and Gan, Quan and Long, Guodong and Karypis, George},\n\tyear = {2022},\n}\n\n\n\n
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\n \n\n \n \n \n \n \n Beyond low-pass filtering: Graph convolutional networks with automatic filtering.\n \n \n \n\n\n \n Wu, Z.; Pan, S.; Long, G.; Jiang, J.; and Zhang, C.\n\n\n \n\n\n\n IEEE Transactions on Knowledge and Data Engineering (TKDE). 2022.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@article{wu_beyond_2022,\n\ttitle = {Beyond low-pass filtering: {Graph} convolutional networks with automatic filtering},\n\tcopyright = {All rights reserved},\n\tjournal = {IEEE Transactions on Knowledge and Data Engineering (TKDE)},\n\tauthor = {Wu, Zonghan and Pan, Shirui and Long, Guodong and Jiang, Jing and Zhang, Chengqi},\n\tyear = {2022},\n}\n\n\n\n
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\n \n\n \n \n \n \n \n Graph self-supervised learning: A survey.\n \n \n \n\n\n \n Liu, Y.; Jin, M.; Pan, S.; Zhou, C.; Zheng, Y.; Xia, F.; and Yu, P. S\n\n\n \n\n\n\n IEEE Transactions on Knowledge and Data Engineering (TKDE). 2022.\n \n\n\n\n
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@article{liu_graph_2022,\n\ttitle = {Graph self-supervised learning: {A} survey},\n\tcopyright = {All rights reserved},\n\tjournal = {IEEE Transactions on Knowledge and Data Engineering (TKDE)},\n\tauthor = {Liu, Yixin and Jin, Ming and Pan, Shirui and Zhou, Chuan and Zheng, Yu and Xia, Feng and Yu, Philip S},\n\tyear = {2022},\n}\n\n\n\n
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\n \n\n \n \n \n \n \n Model Extraction Attacks on Graph Neural Networks: Taxonomy and Realization.\n \n \n \n\n\n \n Wu, B.; Yang, X.; Pan, S.; and Yuan, X.\n\n\n \n\n\n\n In ACM ASIA Conference on Computer and Communications Security (ACM ASIACCS 2022), 2022. \n \n\n\n\n
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@inproceedings{wu_model_2022,\n\ttitle = {Model {Extraction} {Attacks} on {Graph} {Neural} {Networks}: {Taxonomy} and {Realization}},\n\tcopyright = {All rights reserved},\n\tbooktitle = {{ACM} {ASIA} {Conference} on {Computer} and {Communications} {Security} ({ACM} {ASIACCS} 2022)},\n\tauthor = {Wu, Bang and Yang, Xiangwen and Pan, Shirui and Yuan, Xingliang},\n\tyear = {2022},\n}\n\n\n\n
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\n \n\n \n \n \n \n \n Learning multi-level weight-centric features for few-shot learning.\n \n \n \n\n\n \n Liang, M.; Huang, S.; Pan, S.; Gong, M.; and Liu, W.\n\n\n \n\n\n\n Pattern Recognition (PR), 128: 108662. 2022.\n \n\n\n\n
\n\n\n\n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n  \n \n 1 download\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@article{liang_learning_2022,\n\ttitle = {Learning multi-level weight-centric features for few-shot learning},\n\tvolume = {128},\n\tcopyright = {All rights reserved},\n\tissn = {0031-3203},\n\tdoi = {https://doi.org/10.1016/j.patcog.2022.108662},\n\tabstract = {Few-shot learning is currently enjoying a considerable resurgence of interest, aided by the recent advance of deep learning. Contemporary approaches based on weight-generation scheme delivers a straightforward and flexible solution to the problem. However, they did not fully consider both the representation power for unseen categories and weight generation capacity in feature learning, making it a significant performance bottleneck. This paper proposes a multi-level weight-centric feature learning to give full play to feature extractor’s dual roles in few-shot learning. Our proposed method consists of two essential techniques: a weight-centric training strategy to improve the features’ prototype-ability and a multi-level feature incorporating a mid- and relation-level information. The former increases the feasibility of constructing a discriminative decision boundary based on a few samples. Simultaneously, the latter helps improve the transferability for characterizing novel classes and preserve classification capability for base classes. We extensively evaluate our approach to low-shot classification benchmarks. Experiments demonstrate our proposed method significantly outperforms its counterparts in both standard and generalized settings and using different network backbones.},\n\tjournal = {Pattern Recognition (PR)},\n\tauthor = {Liang, Mingjiang and Huang, Shaoli and Pan, Shirui and Gong, Mingming and Liu, Wei},\n\tyear = {2022},\n\tpages = {108662},\n}\n\n\n\n
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\n Few-shot learning is currently enjoying a considerable resurgence of interest, aided by the recent advance of deep learning. Contemporary approaches based on weight-generation scheme delivers a straightforward and flexible solution to the problem. However, they did not fully consider both the representation power for unseen categories and weight generation capacity in feature learning, making it a significant performance bottleneck. This paper proposes a multi-level weight-centric feature learning to give full play to feature extractor’s dual roles in few-shot learning. Our proposed method consists of two essential techniques: a weight-centric training strategy to improve the features’ prototype-ability and a multi-level feature incorporating a mid- and relation-level information. The former increases the feasibility of constructing a discriminative decision boundary based on a few samples. Simultaneously, the latter helps improve the transferability for characterizing novel classes and preserve classification capability for base classes. We extensively evaluate our approach to low-shot classification benchmarks. Experiments demonstrate our proposed method significantly outperforms its counterparts in both standard and generalized settings and using different network backbones.\n
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\n \n\n \n \n \n \n \n Deep neighbor-aware embedding for node clustering in attributed graphs.\n \n \n \n\n\n \n Wang, C.; Pan, S.; Yu, C. P.; Hu, R.; Long, G.; and Zhang, C.\n\n\n \n\n\n\n Pattern Recognition (PR), 122: 108230. 2022.\n \n\n\n\n
\n\n\n\n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@article{wang_deep_2022,\n\ttitle = {Deep neighbor-aware embedding for node clustering in attributed graphs},\n\tvolume = {122},\n\tcopyright = {All rights reserved},\n\tissn = {0031-3203},\n\tdoi = {https://doi.org/10.1016/j.patcog.2021.108230},\n\tabstract = {Node clustering aims to partition the vertices in a graph into multiple groups or communities. Existing studies have mostly focused on developing deep learning approaches to learn a latent representation of nodes, based on which simple clustering methods like k-means are applied. These two-step frameworks for node clustering are difficult to manipulate and usually lead to suboptimal performance, mainly because the graph embedding is not goal-directed, i.e., designed for the specific clustering task. In this paper, we propose a clustering-directed deep learning approach, Deep Neighbor-aware Embedded Node Clustering (DNENC for short) for clustering graph data. Our method focuses on attributed graphs to sufficiently explore the two sides of information in graphs. It encodes the topological structure and node content in a graph into a compact representation via a neighbor-aware graph autoencoder, which progressively absorbs information from neighbors via a convolutional or attentional encoder. Multiple neighbor-aware encoders are stacked to build a deep architecture followed by an inner-product decoder for reconstructing the graph structure. Furthermore, soft labels are generated to supervise a self-training process, which iteratively refines the node clustering results. The self-training process is jointly learned and optimized with the graph embedding in a unified framework, to benefit both components mutually. Experimental results compared with state-of-the-art algorithms demonstrate the good performance of our framework.},\n\tjournal = {Pattern Recognition (PR)},\n\tauthor = {Wang, Chun and Pan, Shirui and Yu, Celina P. and Hu, Ruiqi and Long, Guodong and Zhang, Chengqi},\n\tyear = {2022},\n\tpages = {108230},\n}\n\n\n\n
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\n Node clustering aims to partition the vertices in a graph into multiple groups or communities. Existing studies have mostly focused on developing deep learning approaches to learn a latent representation of nodes, based on which simple clustering methods like k-means are applied. These two-step frameworks for node clustering are difficult to manipulate and usually lead to suboptimal performance, mainly because the graph embedding is not goal-directed, i.e., designed for the specific clustering task. In this paper, we propose a clustering-directed deep learning approach, Deep Neighbor-aware Embedded Node Clustering (DNENC for short) for clustering graph data. Our method focuses on attributed graphs to sufficiently explore the two sides of information in graphs. It encodes the topological structure and node content in a graph into a compact representation via a neighbor-aware graph autoencoder, which progressively absorbs information from neighbors via a convolutional or attentional encoder. Multiple neighbor-aware encoders are stacked to build a deep architecture followed by an inner-product decoder for reconstructing the graph structure. Furthermore, soft labels are generated to supervise a self-training process, which iteratively refines the node clustering results. The self-training process is jointly learned and optimized with the graph embedding in a unified framework, to benefit both components mutually. Experimental results compared with state-of-the-art algorithms demonstrate the good performance of our framework.\n
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\n \n\n \n \n \n \n \n Cross-modal Clinical Graph Transformer For Ophthalmic Report Generation.\n \n \n \n\n\n \n Li, M.; Cai, W.; Verspoor, K.; Pan, S.; Li, X.; and Chang, X.\n\n\n \n\n\n\n In IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR-22, New Orleans, Louisiana, US, Jun 19-24, 2022, pages 20624–20633, 2022. \n \n\n\n\n
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@inproceedings{li_cross-modal_2022,\n\ttitle = {Cross-modal {Clinical} {Graph} {Transformer} {For} {Ophthalmic} {Report} {Generation}},\n\tcopyright = {All rights reserved},\n\tdoi = {10.1109/CVPR52688.2022.02000 (CORE Ranked A*)},\n\tbooktitle = {{IEEE}/{CVF} {Conference} on {Computer} {Vision} and {Pattern} {Recognition}, {CVPR}-22, {New} {Orleans}, {Louisiana}, {US}, {Jun} 19-24, 2022},\n\tauthor = {Li, Mingjie and Cai, Wenjia and Verspoor, Karin and Pan, Shirui and Li, Xiaodan and Chang, Xiaojun},\n\tyear = {2022},\n\tpages = {20624--20633},\n}\n\n\n\n
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\n \n\n \n \n \n \n \n A Probabilistic Graphical Model Based on Neural-symbolic Reasoning for Visual Relationship Detection.\n \n \n \n\n\n \n Yu, D.; Yang, B.; Wei, Q.; Li, A.; and Pan, S.\n\n\n \n\n\n\n In IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR-22, New Orleans, Louisiana, US, Jun 19-24, 2022, pages 10599–10608, 2022. \n \n\n\n\n
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@inproceedings{yu_probabilistic_2022,\n\ttitle = {A {Probabilistic} {Graphical} {Model} {Based} on {Neural}-symbolic {Reasoning} for {Visual} {Relationship} {Detection}},\n\tcopyright = {All rights reserved},\n\tdoi = {10.1109/CVPR52688.2022.01035 (CORE Ranked A*)},\n\tbooktitle = {{IEEE}/{CVF} {Conference} on {Computer} {Vision} and {Pattern} {Recognition}, {CVPR}-22, {New} {Orleans}, {Louisiana}, {US}, {Jun} 19-24, 2022},\n\tauthor = {Yu, Dongran and Yang, Bo and Wei, Qianhao and Li, Anchen and Pan, Shirui},\n\tyear = {2022},\n\tpages = {10599--10608},\n}\n\n\n\n
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\n \n\n \n \n \n \n \n BaLeNAS: Differentiable Architecture Search via Bayesian Learning Rule.\n \n \n \n\n\n \n Zhang, M.; Pan, S.; Chang, X.; Su, S.; Hu, J.; Haffari, R.; and Yang, B.\n\n\n \n\n\n\n In IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR-22, New Orleans, Louisiana, US, Jun 19-24, 2022, pages 11861–11870, 2022. \n \n\n\n\n
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@inproceedings{zhang_balenas:_2022,\n\ttitle = {{BaLeNAS}: {Differentiable} {Architecture} {Search} via {Bayesian} {Learning} {Rule}},\n\tcopyright = {All rights reserved},\n\tdoi = {10.1109/CVPR52688.2022.01157 (CORE Ranked A*)},\n\tbooktitle = {{IEEE}/{CVF} {Conference} on {Computer} {Vision} and {Pattern} {Recognition}, {CVPR}-22, {New} {Orleans}, {Louisiana}, {US}, {Jun} 19-24, 2022},\n\tauthor = {Zhang, Miao and Pan, Shirui and Chang, Xiaojun and Su, Steven and Hu, Jilin and Haffari, Reza and Yang, Bin},\n\tyear = {2022},\n\tpages = {11861--11870},\n}\n\n\n\n
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\n \n\n \n \n \n \n \n Graph Self-Supervised Learning: A Survey.\n \n \n \n\n\n \n Liu, Y.; Jin, M.; Pan, S.; Zhou, C.; Zheng, Y.; Xia, F.; and Yu, P. S.\n\n\n \n\n\n\n IEEE Transactions on Knowledge and Data Engineering (TKDE),1–21. 2022.\n \n\n\n\n
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@article{liu_graph_2022,\n\ttitle = {Graph {Self}-{Supervised} {Learning}: {A} {Survey}},\n\tcopyright = {All rights reserved},\n\tdoi = {10.1109/TKDE.2022.3172903 (Impact Factor: 6.977; JCR Ranked Q1; Top Journal in Data Mining)},\n\tjournal = {IEEE Transactions on Knowledge and Data Engineering (TKDE)},\n\tauthor = {Liu, Yixin and Jin, Ming and Pan, Shirui and Zhou, Chuan and Zheng, Yu and Xia, Feng and Yu, Philip S.},\n\tyear = {2022},\n\tpages = {1--21},\n}\n\n\n\n
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\n \n\n \n \n \n \n \n Towards graph self-supervised learning with contrastive adjusted zooming.\n \n \n \n\n\n \n Liu, Y.; Jin, M.; Pan, S.; Zhou, C.; Zheng, Y.; Xia, F.; and Yu, P. S.\n\n\n \n\n\n\n IEEE Transactions on Neural Networks and Learning Systems (TNNLS),1–15. 2022.\n \n\n\n\n
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@article{liu_towards_2022,\n\ttitle = {Towards graph self-supervised learning with contrastive adjusted zooming},\n\tcopyright = {All rights reserved},\n\tdoi = {10.1109/TNNLS.2022.3216630},\n\tjournal = {IEEE Transactions on Neural Networks and Learning Systems (TNNLS)},\n\tauthor = {Liu, Yixin and Jin, Ming and Pan, Shirui and Zhou, Chuan and Zheng, Yu and Xia, Feng and Yu, Philip S.},\n\tyear = {2022},\n\tpages = {1--15},\n}\n\n\n\n
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\n \n\n \n \n \n \n \n Projective Ranking-based GNN Evasion Attacks.\n \n \n \n\n\n \n Zhang, H.; Yuan, X.; Zhou, C.; and Pan, S.\n\n\n \n\n\n\n IEEE Transactions on Knowledge and Data Engineering (TKDE),1–14. 2022.\n \n\n\n\n
\n\n\n\n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n  \n \n 1 download\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@article{zhang_projective_2022,\n\ttitle = {Projective {Ranking}-based {GNN} {Evasion} {Attacks}},\n\tcopyright = {All rights reserved},\n\tdoi = {10.1109/TKDE.2022.3219209 (Impact Factor: 6.977; JCR Ranked Q1; Top Journal in Data Mining)},\n\tjournal = {IEEE Transactions on Knowledge and Data Engineering (TKDE)},\n\tauthor = {Zhang, He and Yuan, Xingliang and Zhou, Chuan and Pan, Shirui},\n\tyear = {2022},\n\tpages = {1--14},\n}\n\n\n\n
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\n \n\n \n \n \n \n \n Multivariate Time Series Forecasting with Dynamic Graph Neural ODEs.\n \n \n \n\n\n \n Jin, M.; Zheng, Y.; Li, Y.; Chen, S.; Yang, B.; and Pan, S.\n\n\n \n\n\n\n IEEE Transactions on Knowledge and Data Engineering (TKDE),1–14. 2022.\n \n\n\n\n
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@article{jin_multivariate_2022,\n\ttitle = {Multivariate {Time} {Series} {Forecasting} with {Dynamic} {Graph} {Neural} {ODEs}},\n\tcopyright = {All rights reserved},\n\tjournal = {IEEE Transactions on Knowledge and Data Engineering (TKDE)},\n\tauthor = {Jin, Ming and Zheng, Yu and Li, Yuan-Fang and Chen, Siheng and Yang, Bin and Pan, Shirui},\n\tyear = {2022},\n\tpages = {1--14},\n}\n\n\n\n
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\n \n\n \n \n \n \n \n Predicting Human Mobility via Graph Convolutional Dual-attentive Networks.\n \n \n \n\n\n \n Dang, W.; Wang, H.; Pan, S.; Zhang, P.; Zhou, C.; Chen, X.; and Wang, J.\n\n\n \n\n\n\n In International Conference on Web Search and Data Mining (WSDM), pages 192–200, 2022. ACM\n \n\n\n\n
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@inproceedings{dang_predicting_2022,\n\ttitle = {Predicting {Human} {Mobility} via {Graph} {Convolutional} {Dual}-attentive {Networks}},\n\tcopyright = {All rights reserved},\n\tdoi = {10.1145/3488560.3498400 (CORE Ranked A*)},\n\tbooktitle = {International {Conference} on {Web} {Search} and {Data} {Mining} ({WSDM})},\n\tpublisher = {ACM},\n\tauthor = {Dang, Weizhen and Wang, Haibo and Pan, Shirui and Zhang, Pei and Zhou, Chuan and Chen, Xin and Wang, Jilong},\n\tyear = {2022},\n\tpages = {192--200},\n}\n\n\n\n
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\n \n\n \n \n \n \n \n Towards Unsupervised Deep Graph Structure Learning.\n \n \n \n\n\n \n Liu, Y.; Zheng, Y.; Zhang, D.; Chen, H.; Peng, H.; and Pan, S.\n\n\n \n\n\n\n In The Web Conference (WWW), Lyon, France 25 – 29 April 2022, pages 1392–1403, 2022. ACM\n \n\n\n\n
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@inproceedings{liu_towards_2022,\n\ttitle = {Towards {Unsupervised} {Deep} {Graph} {Structure} {Learning}},\n\tcopyright = {All rights reserved},\n\tdoi = {10.1145/3485447.3512186 (CORE Ranked A*)},\n\tbooktitle = {The {Web} {Conference} ({WWW}), {Lyon}, {France} 25 – 29 {April} 2022},\n\tpublisher = {ACM},\n\tauthor = {Liu, Yixin and Zheng, Yu and Zhang, Daokun and Chen, Hongxu and Peng, Hao and Pan, Shirui},\n\tyear = {2022},\n\tpages = {1392--1403},\n}\n\n\n\n
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\n \n\n \n \n \n \n \n Towards Spatio-Temporal Aware Traffic Time Series Forecasting.\n \n \n \n\n\n \n Cirstea, R.; Yang, B.; Guo, C.; Kieu, T.; and Pan, S. P.\n\n\n \n\n\n\n In IEEE International Conference on Data Engineering (ICDE-22), (Virtual) Kuala Lumpur, Malaysia, May 9-12, 2022 (CORE Ranked A*), 2022. \n \n\n\n\n
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@inproceedings{cirstea_towards_2022,\n\ttitle = {Towards {Spatio}-{Temporal} {Aware} {Traffic} {Time} {Series} {Forecasting}},\n\tcopyright = {All rights reserved},\n\tbooktitle = {{IEEE} {International} {Conference} on {Data} {Engineering} ({ICDE}-22), ({Virtual}) {Kuala} {Lumpur}, {Malaysia}, {May} 9-12, 2022 ({CORE} {Ranked} {A}*)},\n\tauthor = {Cirstea, Razvan and Yang, Bin and Guo, Chenjuan and Kieu, Tung and Pan, Shirui Pan},\n\tyear = {2022},\n}\n\n\n\n
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\n \n\n \n \n \n \n \n Fire Burns, Swords Cut: Commonsense Inductive Bias for Exploration in Text-based Games.\n \n \n \n\n\n \n Ryu, D.; Shareghi, E.; Fang, M.; Xu, Y.; Pan, S.; and Haffari, R.\n\n\n \n\n\n\n In 60th Annual Meeting of the Association for Computational Linguistics (ACL-2022), Dublin, Ireland, May 22-27, 2022 (CORE Ranked A*), volume 2, pages 515–522, 2022. \n \n\n\n\n
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@inproceedings{ryu_fire_2022,\n\ttitle = {Fire {Burns}, {Swords} {Cut}: {Commonsense} {Inductive} {Bias} for {Exploration} in {Text}-based {Games}},\n\tvolume = {2},\n\tcopyright = {All rights reserved},\n\tbooktitle = {60th {Annual} {Meeting} of the {Association} for {Computational} {Linguistics} ({ACL}-2022), {Dublin}, {Ireland}, {May} 22-27, 2022 ({CORE} {Ranked} {A}*)},\n\tauthor = {Ryu, Dongwon and Shareghi, Ehsan and Fang, Meng and Xu, Yunqiu and Pan, Shirui and Haffari, Reza},\n\tyear = {2022},\n\tpages = {515--522},\n}\n\n\n\n
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\n \n\n \n \n \n \n \n Exploring Relational Semantics for Inductive Knowledge Graph Completion.\n \n \n \n\n\n \n Wang, C.; Zhou, X.; Pan, S.; Dong, L.; Song, Z.; and Sha, Y.\n\n\n \n\n\n\n In Proceedings of the AAAI Conference on Artificial Intelligence (AAAI), Virtual Conference, Feburary 22- March 1, 2022 (CORE Ranked A*), 2022. AAAI Press\n \n\n\n\n
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@inproceedings{wang_exploring_2022,\n\ttitle = {Exploring {Relational} {Semantics} for {Inductive} {Knowledge} {Graph} {Completion}},\n\tcopyright = {All rights reserved},\n\tbooktitle = {Proceedings of the {AAAI} {Conference} on {Artificial} {Intelligence} ({AAAI}), {Virtual} {Conference}, {Feburary} 22- {March} 1, 2022 ({CORE} {Ranked} {A}*)},\n\tpublisher = {AAAI Press},\n\tauthor = {Wang, Changjian and Zhou, Xiaofei and Pan, Shirui and Dong, Linhua and Song, Zeliang and Sha, Ying},\n\tyear = {2022},\n}\n\n\n\n
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\n \n\n \n \n \n \n \n Dual Space Graph Contrastive Learning.\n \n \n \n\n\n \n Yang, H.; Chen, H.; Pan, S.; Li, L.; Yu, P. S; and Xu, G.\n\n\n \n\n\n\n In The Web Conference (WWW), Lyon, France 25 – 29 April 2022, pages 1238–1247, 2022. ACM\n \n\n\n\n
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@inproceedings{yang_dual_2022,\n\ttitle = {Dual {Space} {Graph} {Contrastive} {Learning}},\n\tcopyright = {All rights reserved},\n\tdoi = {10.1145/3485447.3512211 (CORE Ranked A*)},\n\tbooktitle = {The {Web} {Conference} ({WWW}), {Lyon}, {France} 25 – 29 {April} 2022},\n\tpublisher = {ACM},\n\tauthor = {Yang, Haoran and Chen, Hongxu and Pan, Shirui and Li, Lin and Yu, Philip S and Xu, Guandong},\n\tyear = {2022},\n\tpages = {1238--1247},\n}\n\n\n\n
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\n \n\n \n \n \n \n \n A Survey on Knowledge Graphs: Representation, Acquisition, and Applications.\n \n \n \n\n\n \n Ji, S.; Pan, S.; Cambria, E.; Marttinen, P.; and Yu, P. S\n\n\n \n\n\n\n IEEE Transactions on Neural Networks and Learning Systems (TNNLS), 33(2): 494–514. 2022.\n \n\n\n\n
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@article{ji_survey_2022,\n\ttitle = {A {Survey} on {Knowledge} {Graphs}: {Representation}, {Acquisition}, and {Applications}},\n\tvolume = {33},\n\tcopyright = {All rights reserved},\n\tdoi = {10.1109/TNNLS.2021.3070843 (Impact Factor: 10.451; JCR Ranked Q1)},\n\tnumber = {2},\n\tjournal = {IEEE Transactions on Neural Networks and Learning Systems (TNNLS)},\n\tauthor = {Ji, Shaoxiong and Pan, Shirui and Cambria, Erik and Marttinen, Pekka and Yu, Philip S},\n\tyear = {2022},\n\tpages = {494--514},\n}\n\n\n\n
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\n \n\n \n \n \n \n \n Predicting Best-Selling New Products in a Major Promotion Campaign Through Graph Convolutional Networks.\n \n \n \n\n\n \n Li, C.; Jiang, W.; Yang, Y.; Pan, S.; Guo, L.; and Huang, G.\n\n\n \n\n\n\n IEEE Transactions on Neural Networks and Learning Systems (TNNLS),1–14. 2022.\n \n\n\n\n
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@article{li_predicting_2022,\n\ttitle = {Predicting {Best}-{Selling} {New} {Products} in a {Major} {Promotion} {Campaign} {Through} {Graph} {Convolutional} {Networks}},\n\tcopyright = {All rights reserved},\n\tdoi = {10.1109/TNNLS.2022.3155690 (Impact Factor: 10.451; JCR Ranked Q1)},\n\tjournal = {IEEE Transactions on Neural Networks and Learning Systems (TNNLS)},\n\tauthor = {Li, Chaojie and Jiang, Wensen and Yang, Yin and Pan, Shirui and Guo, Lijie and Huang, Gang},\n\tyear = {2022},\n\tpages = {1--14},\n}\n\n\n\n
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\n \n\n \n \n \n \n \n Anomaly Detection on Attributed Networks via Contrastive Self-Supervised Learning.\n \n \n \n\n\n \n Liu, Y.; Li, Z.; Pan, S.; Gong, C.; Zhou, C.; and Karypis, G.\n\n\n \n\n\n\n IEEE Transactions on Neural Networks and Learning Systems (TNNLS), 33(6): 2378–2392. 2022.\n \n\n\n\n
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@article{liu_anomaly_2022,\n\ttitle = {Anomaly {Detection} on {Attributed} {Networks} via {Contrastive} {Self}-{Supervised} {Learning}},\n\tvolume = {33},\n\tcopyright = {All rights reserved},\n\tdoi = {10.1109/TNNLS.2021.3068344 (Impact Factor: 10.451; JCR Ranked Q1)},\n\tnumber = {6},\n\tjournal = {IEEE Transactions on Neural Networks and Learning Systems (TNNLS)},\n\tauthor = {Liu, Yixin and Li, Zhao and Pan, Shirui and Gong, Chen and Zhou, Chuan and Karypis, George},\n\tyear = {2022},\n\tpages = {2378--2392},\n}\n\n\n\n
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\n \n\n \n \n \n \n \n Dual Interactive Graph Convolutional Networks for Hyperspectral Image Classification.\n \n \n \n\n\n \n Wan, S.; Pan, S.; Zhong, P.; Chang, X.; Yang, J.; and Gong, C.\n\n\n \n\n\n\n IEEE Transactions on Geoscience and Remote Sensing (TGRS), 60: 1–14. 2022.\n \n\n\n\n
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@article{wan_dual_2022,\n\ttitle = {Dual {Interactive} {Graph} {Convolutional} {Networks} for {Hyperspectral} {Image} {Classification}},\n\tvolume = {60},\n\tcopyright = {All rights reserved},\n\tdoi = {10.1109/TGRS.2021.3075223 (Impact Factor: 5.6; JCR Ranked Q1)},\n\tjournal = {IEEE Transactions on Geoscience and Remote Sensing (TGRS)},\n\tauthor = {Wan, Sheng and Pan, Shirui and Zhong, Ping and Chang, Xiaojun and Yang, Jian and Gong, Chen},\n\tyear = {2022},\n\tpages = {1--14},\n}\n\n\n\n
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\n \n\n \n \n \n \n \n Attraction and Repulsion: Unsupervised Domain Adaptive Graph Contrastive Learning Network.\n \n \n \n\n\n \n Wu, M.; Pan, S.; and Zhu, X.\n\n\n \n\n\n\n IEEE Transactions on Emerging Topics in Computational Intelligence (TETCI),1. 2022.\n \n\n\n\n
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@article{wu_attraction_2022,\n\ttitle = {Attraction and {Repulsion}: {Unsupervised} {Domain} {Adaptive} {Graph} {Contrastive} {Learning} {Network}},\n\tcopyright = {All rights reserved},\n\tdoi = {10.1109/TETCI.2022.3156044},\n\tjournal = {IEEE Transactions on Emerging Topics in Computational Intelligence (TETCI)},\n\tauthor = {Wu, Man and Pan, Shirui and Zhu, Xingquan},\n\tyear = {2022},\n\tpages = {1},\n}\n\n\n\n
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\n \n\n \n \n \n \n \n TraverseNet: Unifying Space and Time in Message Passing for Traffic Forecasting.\n \n \n \n\n\n \n Wu, Z.; Zheng, D.; Pan, S.; Gan, Q.; Long, G.; and Karypis, G.\n\n\n \n\n\n\n IEEE Transactions on Neural Networks and Learning Systems (TNNLS),1–11. 2022.\n \n\n\n\n
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@article{wu_traversenet:_2022,\n\ttitle = {{TraverseNet}: {Unifying} {Space} and {Time} in {Message} {Passing} for {Traffic} {Forecasting}},\n\tcopyright = {All rights reserved},\n\tdoi = {10.1109/TNNLS.2022.3186103},\n\tjournal = {IEEE Transactions on Neural Networks and Learning Systems (TNNLS)},\n\tauthor = {Wu, Zonghan and Zheng, Da and Pan, Shirui and Gan, Quan and Long, Guodong and Karypis, George},\n\tyear = {2022},\n\tpages = {1--11},\n}\n\n\n\n
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\n \n\n \n \n \n \n \n Discrete embedding for attributed graphs.\n \n \n \n\n\n \n Yang, H.; Chen, L.; Pan, S.; Wang, H.; and Zhang, P.\n\n\n \n\n\n\n Pattern Recognition (PR), 123: 108368. 2022.\n Publisher: Elsevier\n\n\n\n
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@article{yang_discrete_2022,\n\ttitle = {Discrete embedding for attributed graphs},\n\tvolume = {123},\n\tcopyright = {All rights reserved},\n\tdoi = {10.1016/j.patcog.2021.108368 (Impact Factor: 7.74; JCR Ranked Q1)},\n\tjournal = {Pattern Recognition (PR)},\n\tauthor = {Yang, Hong and Chen, Ling and Pan, Shirui and Wang, Haishuai and Zhang, Peng},\n\tyear = {2022},\n\tnote = {Publisher: Elsevier},\n\tpages = {108368},\n}\n\n\n\n
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\n \n\n \n \n \n \n \n Task Scheduling in Three-Dimensional Spatial Crowdsourcing: A Social Welfare Perspective.\n \n \n \n\n\n \n Wang, L.; Yang, D.; Yu, Z.; Xiong, F.; Han, L.; Pan, S.; and Guo, B.\n\n\n \n\n\n\n IEEE Transactions on Mobile Computing (TMC),1–1. 2022.\n \n\n\n\n
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@article{wang_task_2022,\n\ttitle = {Task {Scheduling} in {Three}-{Dimensional} {Spatial} {Crowdsourcing}: {A} {Social} {Welfare} {Perspective}},\n\tcopyright = {All rights reserved},\n\tdoi = {10.1109/TMC.2022.3175305},\n\tjournal = {IEEE Transactions on Mobile Computing (TMC)},\n\tauthor = {Wang, Liang and Yang, Dingqi and Yu, Zhiwen and Xiong, Fei and Han, Lei and Pan, Shirui and Guo, Bin},\n\tyear = {2022},\n\tpages = {1--1},\n}\n\n\n\n
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\n \n\n \n \n \n \n \n Compact Scheduling for Task Graph Oriented Mobile Crowdsourcing.\n \n \n \n\n\n \n Wang, L.; Yu, Z.; Han, Q.; Yang, D.; Pan, S.; Yao, Y.; and Zhang, D.\n\n\n \n\n\n\n IEEE Transactions on Mobile Computing (TMC), 21(7): 2358–2371. 2022.\n \n\n\n\n
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@article{wang_compact_2022,\n\ttitle = {Compact {Scheduling} for {Task} {Graph} {Oriented} {Mobile} {Crowdsourcing}},\n\tvolume = {21},\n\tcopyright = {All rights reserved},\n\tdoi = {10.1109/TMC.2020.3040007},\n\tnumber = {7},\n\tjournal = {IEEE Transactions on Mobile Computing (TMC)},\n\tauthor = {Wang, Liang and Yu, Zhiwen and Han, Qi and Yang, Dingqi and Pan, Shirui and Yao, Yuan and Zhang, Daqing},\n\tyear = {2022},\n\tpages = {2358--2371},\n}\n\n\n\n
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\n \n\n \n \n \n \n \n Beyond low-pass filtering: Graph convolutional networks with automatic filtering.\n \n \n \n\n\n \n Wu, Z.; Pan, S.; Long, G.; Jiang, J.; and Zhang, C.\n\n\n \n\n\n\n IEEE Transactions on Knowledge and Data Engineering (TKDE),1–12. 2022.\n \n\n\n\n
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@article{wu_beyond_2022,\n\ttitle = {Beyond low-pass filtering: {Graph} convolutional networks with automatic filtering},\n\tcopyright = {All rights reserved},\n\tdoi = {10.1109/TKDE.2022.3186016},\n\tjournal = {IEEE Transactions on Knowledge and Data Engineering (TKDE)},\n\tauthor = {Wu, Zonghan and Pan, Shirui and Long, Guodong and Jiang, Jing and Zhang, Chengqi},\n\tyear = {2022},\n\tpages = {1--12},\n}\n\n\n\n
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\n \n\n \n \n \n \n \n One-shot Learning-based Animal Video Segmentation.\n \n \n \n\n\n \n Xue, T.; Qiao, Y.; Kong, H.; Su, D.; Pan, S.; Rafique, K.; and Sukkarieh, S.\n\n\n \n\n\n\n IEEE Transactions on Industrial Informatics (TII), 18(6): 3799–3807. 2022.\n \n\n\n\n
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@article{xue_one-shot_2022,\n\ttitle = {One-shot {Learning}-based {Animal} {Video} {Segmentation}},\n\tvolume = {18},\n\tcopyright = {All rights reserved},\n\tdoi = {10.1109/TII.2021.3117020 (Impact Factor: 10.215; JCR Ranked Q1)},\n\tnumber = {6},\n\tjournal = {IEEE Transactions on Industrial Informatics (TII)},\n\tauthor = {Xue, Tengfei and Qiao, Yongliang and Kong, He and Su, Daobilige and Pan, Shirui and Rafique, Khalid and Sukkarieh, Salah},\n\tyear = {2022},\n\tpages = {3799--3807},\n}\n
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\n  \n 2021\n \n \n (43)\n \n \n
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\n \n\n \n \n \n \n \n Towards graph self-supervised learning with contrastive adjusted zooming.\n \n \n \n\n\n \n Zheng, Y.; Jin, M.; Pan, S.; Li, Y.; Peng, H.; Li, M.; and Li, Z.\n\n\n \n\n\n\n IEEE Transactions on Neural Networks and Learning Systems (TNNLS). 2021.\n \n\n\n\n
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@article{zheng_towards_2021,\n\ttitle = {Towards graph self-supervised learning with contrastive adjusted zooming},\n\tcopyright = {All rights reserved},\n\tjournal = {IEEE Transactions on Neural Networks and Learning Systems (TNNLS)},\n\tauthor = {Zheng, Yizhen and Jin, Ming and Pan, Shirui and Li, Yuan-Fang and Peng, Hao and Li, Ming and Li, Zhao},\n\tyear = {2021},\n}\n\n\n\n
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\n \n\n \n \n \n \n \n Hypergraph Convolutional Network for Group Recommendation.\n \n \n \n\n\n \n Jia, R.; Zhou, X.; Dong, L.; and Pan, S.\n\n\n \n\n\n\n In IEEE International Conference on Data Mining (ICDM), 2021. \n \n\n\n\n
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@inproceedings{jia_hypergraph_2021,\n\ttitle = {Hypergraph {Convolutional} {Network} for {Group} {Recommendation}},\n\tcopyright = {All rights reserved},\n\tbooktitle = {{IEEE} {International} {Conference} on {Data} {Mining} ({ICDM})},\n\tauthor = {Jia, Renqi and Zhou, Xiaofei and Dong, Linhua and Pan, Shirui},\n\tyear = {2021},\n}\n\n\n\n
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\n \n\n \n \n \n \n \n Adapting Membership Inference Attacks to GNN for Graph Classification: Approaches and Implications.\n \n \n \n\n\n \n Wu, B.; Yang, X.; Pan, S.; and Yuan, X.\n\n\n \n\n\n\n In IEEE International Conference on Data Mining (ICDM), 2021. \n \n\n\n\n
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@inproceedings{wu_adapting_2021,\n\ttitle = {Adapting {Membership} {Inference} {Attacks} to {GNN} for {Graph} {Classification}: {Approaches} and {Implications}},\n\tcopyright = {All rights reserved},\n\tbooktitle = {{IEEE} {International} {Conference} on {Data} {Mining} ({ICDM})},\n\tauthor = {Wu, Bang and Yang, Xiangwen and Pan, Shirui and Yuan, Xingliang},\n\tyear = {2021},\n}\n\n\n\n
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\n \n\n \n \n \n \n \n One-shot learning-based animal video segmentation.\n \n \n \n\n\n \n Xue, T.; Qiao, Y.; Kong, H.; Su, D.; Pan, S.; Rafique, K.; and Sukkarieh, S.\n\n\n \n\n\n\n IEEE Transactions on Industrial Informatics, 18(6): 3799–3807. 2021.\n Publisher: IEEE\n\n\n\n
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@article{xue_one-shot_2021,\n\ttitle = {One-shot learning-based animal video segmentation},\n\tvolume = {18},\n\tcopyright = {All rights reserved},\n\tnumber = {6},\n\tjournal = {IEEE Transactions on Industrial Informatics},\n\tauthor = {Xue, Tengfei and Qiao, Yongliang and Kong, He and Su, Daobilige and Pan, Shirui and Rafique, Khalid and Sukkarieh, Salah},\n\tyear = {2021},\n\tnote = {Publisher: IEEE},\n\tpages = {3799--3807},\n}\n\n\n\n
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\n \n\n \n \n \n \n \n ANEMONE: Graph Anomaly Detection with Multi-Scale Contrastive Learning.\n \n \n \n\n\n \n Jin, M.; Liu, Y.; Zheng, Y.; Chi, L.; Li, Y.; and Pan, S.\n\n\n \n\n\n\n In ACM International Conference on Information and Knowledge Management (CIKM’21), 2021. \n \n\n\n\n
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@inproceedings{jin_anemone:_2021,\n\ttitle = {{ANEMONE}: {Graph} {Anomaly} {Detection} with {Multi}-{Scale} {Contrastive} {Learning}},\n\tcopyright = {All rights reserved},\n\tbooktitle = {{ACM} {International} {Conference} on {Information} and {Knowledge} {Management} ({CIKM}’21)},\n\tauthor = {Jin, Ming and Liu, Yixin and Zheng, Yu and Chi, Lianhua and Li, Yuan-Fang and Pan, Shirui},\n\tyear = {2021},\n}\n\n\n\n
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\n \n\n \n \n \n \n \n IGNSCDA: Predicting CircRNA-Disease Associations Based on Improved Graph Convolutional Network and Negative Sampling.\n \n \n \n\n\n \n Lan, W.; Dong, Y.; Chen, Q.; Liu, J.; Wang, J.; Chen, Y. P.; and Pan, S.\n\n\n \n\n\n\n IEEE/ACM Transactions on Computational Biology and Bioinformatics, TCBB. 2021.\n Publisher: IEEE\n\n\n\n
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@article{lan_ignscda:_2021,\n\ttitle = {{IGNSCDA}: {Predicting} {CircRNA}-{Disease} {Associations} {Based} on {Improved} {Graph} {Convolutional} {Network} and {Negative} {Sampling}},\n\tcopyright = {All rights reserved},\n\tjournal = {IEEE/ACM Transactions on Computational Biology and Bioinformatics, TCBB},\n\tauthor = {Lan, Wei and Dong, Yi and Chen, Qingfeng and Liu, Jin and Wang, Jianxin and Chen, Yi-Ping Phoebe and Pan, Shirui},\n\tyear = {2021},\n\tnote = {Publisher: IEEE},\n}\n\n\n\n
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\n \n\n \n \n \n \n \n Learning graph representations with maximal cliques.\n \n \n \n\n\n \n Molaei, S.; Bousejin, N. G.; Zare, H.; Jalili, M.; and Pan, S.\n\n\n \n\n\n\n IEEE Transactions on Neural Networks and Learning Systems, 34(2): 1089–1096. 2021.\n Publisher: IEEE\n\n\n\n
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@article{molaei_learning_2021,\n\ttitle = {Learning graph representations with maximal cliques},\n\tvolume = {34},\n\tcopyright = {All rights reserved},\n\tnumber = {2},\n\tjournal = {IEEE Transactions on Neural Networks and Learning Systems},\n\tauthor = {Molaei, Soheila and Bousejin, Nima Ghanbari and Zare, Hadi and Jalili, Mahdi and Pan, Shirui},\n\tyear = {2021},\n\tnote = {Publisher: IEEE},\n\tpages = {1089--1096},\n}\n\n\n\n
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\n \n\n \n \n \n \n \n Anomaly Detection in Dynamic Graphs via Transformer.\n \n \n \n\n\n \n Liu, Y.; Pan, S.; Wang, Y. G.; Xiong, F.; Wang, L.; and Lee, V.\n\n\n \n\n\n\n IEEE Transactions on Knowledge and Data Engineering (TKDE). 2021.\n \n\n\n\n
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@article{liu_anomaly_2021,\n\ttitle = {Anomaly {Detection} in {Dynamic} {Graphs} via {Transformer}},\n\tcopyright = {All rights reserved},\n\tjournal = {IEEE Transactions on Knowledge and Data Engineering (TKDE)},\n\tauthor = {Liu, Yixin and Pan, Shirui and Wang, Yu Guang and Xiong, Fei and Wang, Liang and Lee, Vincent},\n\tyear = {2021},\n}\n\n\n\n
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\n \n\n \n \n \n \n \n Contrastive Graph Poisson Networks: Semi-Supervised Learning with Extremely Limited Labels.\n \n \n \n\n\n \n Wan, S.; Zhan, Y.; Liu, L.; Yu, B.; Pan, S.; and Gong, C.\n\n\n \n\n\n\n In Advances in Neural Information Processing Systems, NeurIPS-21, 2021. \n \n\n\n\n
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@inproceedings{wan_contrastive_2021,\n\ttitle = {Contrastive {Graph} {Poisson} {Networks}: {Semi}-{Supervised} {Learning} with {Extremely} {Limited} {Labels}},\n\tcopyright = {All rights reserved},\n\tbooktitle = {Advances in {Neural} {Information} {Processing} {Systems}, {NeurIPS}-21},\n\tauthor = {Wan, Sheng and Zhan, Yibing and Liu, Liu and Yu, Baosheng and Pan, Shirui and Gong, Chen},\n\tyear = {2021},\n}\n\n\n\n
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\n \n\n \n \n \n \n \n Leveraging Information Bottleneck for Scientific Document Summarization.\n \n \n \n\n\n \n Ju, J.; Liu, M.; Koh, H. Y.; Jin, Y.; Du, L.; and Pan, S.\n\n\n \n\n\n\n In EMNLP 2021 Findings, 2021. \n \n\n\n\n
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@inproceedings{ju_leveraging_2021,\n\ttitle = {Leveraging {Information} {Bottleneck} for {Scientific} {Document} {Summarization}},\n\tcopyright = {All rights reserved},\n\tbooktitle = {{EMNLP} 2021 {Findings}},\n\tauthor = {Ju, Jiaxin and Liu, Ming and Koh, Huan Yee and Jin, Yuan and Du, Lan and Pan, Shirui},\n\tyear = {2021},\n}\n\n\n\n
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\n \n\n \n \n \n \n \n Projective Ranking: A Transferable Evasion Attack Method on Graph Neural Networks.\n \n \n \n\n\n \n Zhang, H.; Wu, B.; Yang, X.; Zhou, C.; Wang, S.; Yuan, X.; and Pan, S.\n\n\n \n\n\n\n In ACM International Conference on Information and Knowledge Management (CIKM’21), 2021. \n \n\n\n\n
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@inproceedings{zhang_projective_2021,\n\ttitle = {Projective {Ranking}: {A} {Transferable} {Evasion} {Attack} {Method} on {Graph} {Neural} {Networks}},\n\tcopyright = {All rights reserved},\n\tbooktitle = {{ACM} {International} {Conference} on {Information} and {Knowledge} {Management} ({CIKM}’21)},\n\tauthor = {Zhang, He and Wu, Bang and Yang, Xiangwen and Zhou, Chuan and Wang, Shuo and Yuan, Xingliang and Pan, Shirui},\n\tyear = {2021},\n}\n\n\n\n
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\n \n\n \n \n \n \n \n iDARTS: Differentiable Architecture Search with Stochastic Implicit Gradients.\n \n \n \n\n\n \n Zhang, M.; Su, S.; Pan, S.; Chang, X.; Abbasnejad, E.; and Haffari, R.\n\n\n \n\n\n\n In International Conference on Machine Learning, ICML-21, 2021. \n \n\n\n\n
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@inproceedings{zhang_idarts:_2021,\n\ttitle = {{iDARTS}: {Differentiable} {Architecture} {Search} with {Stochastic} {Implicit} {Gradients}},\n\tcopyright = {All rights reserved},\n\tbooktitle = {International {Conference} on {Machine} {Learning}, {ICML}-21},\n\tauthor = {Zhang, Miao and Su, Steven and Pan, Shirui and Chang, Xiaojun and Abbasnejad, Ehsan and Haffari, Reza},\n\tyear = {2021},\n}\n\n\n\n
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\n \n\n \n \n \n \n \n Multi-Scale Contrastive Siamese Networks for Self-Supervised Graph Representation Learning.\n \n \n \n\n\n \n Jin, M.; Zheng, Y.; Li, Y.; Gong, C.; Zhou, C.; and Pan, S.\n\n\n \n\n\n\n In International Joint Conference on Artificial Intelligence, IJCAI-21, 2021. \n \n\n\n\n
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@inproceedings{jin_multi-scale_2021,\n\ttitle = {Multi-{Scale} {Contrastive} {Siamese} {Networks} for {Self}-{Supervised} {Graph} {Representation} {Learning}},\n\tcopyright = {All rights reserved},\n\tbooktitle = {International {Joint} {Conference} on {Artificial} {Intelligence}, {IJCAI}-21},\n\tauthor = {Jin, Ming and Zheng, Yizhen and Li, Yuan-Fang and Gong, Chen and Zhou, Chuan and Pan, Shirui},\n\tyear = {2021},\n}\n\n\n\n
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\n \n\n \n \n \n \n \n Dual Interactive Graph Convolutional Networks for Hyperspectral Image Classification.\n \n \n \n\n\n \n Wan, S.; Pan, S.; Zhong, P.; Chang, X.; Yang, J.; and Gong, C.\n\n\n \n\n\n\n IEEE Transactions on Geoscience and Remote Sensing, TGRS, 60: 5510214. 2021.\n Publisher: IEEE\n\n\n\n
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@article{wan_dual_2021,\n\ttitle = {Dual {Interactive} {Graph} {Convolutional} {Networks} for {Hyperspectral} {Image} {Classification}},\n\tvolume = {60},\n\tcopyright = {All rights reserved},\n\tjournal = {IEEE Transactions on Geoscience and Remote Sensing, TGRS},\n\tauthor = {Wan, Sheng and Pan, Shirui and Zhong, Ping and Chang, Xiaojun and Yang, Jian and Gong, Chen},\n\tyear = {2021},\n\tnote = {Publisher: IEEE},\n\tpages = {5510214},\n}\n\n\n\n
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\n \n\n \n \n \n \n \n Task-adaptive Neural Process for User Cold-Start Recommendation.\n \n \n \n\n\n \n Lin, X.; Wu, J.; Zhou, C.; Pan, S.; Cao, Y.; and Wang, B.\n\n\n \n\n\n\n In The Web Conference (WWW-2021), 2021. \n \n\n\n\n
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@inproceedings{lin_task-adaptive_2021,\n\ttitle = {Task-adaptive {Neural} {Process} for {User} {Cold}-{Start} {Recommendation}},\n\tcopyright = {All rights reserved},\n\tbooktitle = {The {Web} {Conference} ({WWW}-2021)},\n\tauthor = {Lin, Xixun and Wu, Jia and Zhou, Chuan and Pan, Shirui and Cao, Yanan and Wang, Bin},\n\tyear = {2021},\n}\n\n\n\n
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\n \n\n \n \n \n \n \n Learning Graph Neural Networks with Positive and Unlabeled Nodes.\n \n \n \n\n\n \n Wu, M.; Pan, S.; Du, L.; and Zhu, X.\n\n\n \n\n\n\n ACM Transactions on Knowledge Discovery from Data (TKDD). 2021.\n \n\n\n\n
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@article{wu_learning_2021,\n\ttitle = {Learning {Graph} {Neural} {Networks} with {Positive} and {Unlabeled} {Nodes}},\n\tcopyright = {All rights reserved},\n\tjournal = {ACM Transactions on Knowledge Discovery from Data (TKDD)},\n\tauthor = {Wu, Man and Pan, Shirui and Du, Lan and Zhu, Xingquan},\n\tyear = {2021},\n}\n\n\n\n
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\n \n\n \n \n \n \n \n Convolutional neural networks based lung nodule classification: a surrogate-assisted evolutionary algorithm for hyperparameter optimization.\n \n \n \n\n\n \n Zhang, M.; Li, H.; Pan, S.; Lyu, J.; Ling, S.; and Su, S.\n\n\n \n\n\n\n IEEE Transactions on Evolutionary Computation, TEvC. 2021.\n Publisher: IEEE\n\n\n\n
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@article{zhang_convolutional_2021,\n\ttitle = {Convolutional neural networks based lung nodule classification: a surrogate-assisted evolutionary algorithm for hyperparameter optimization},\n\tcopyright = {All rights reserved},\n\tjournal = {IEEE Transactions on Evolutionary Computation, TEvC},\n\tauthor = {Zhang, Miao and Li, Huiqi and Pan, Shirui and Lyu, Juan and Ling, Steve and Su, Steven},\n\tyear = {2021},\n\tnote = {Publisher: IEEE},\n}\n\n\n\n
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\n \n\n \n \n \n \n \n Temporal network embedding for link prediction via VAE joint attention mechanism.\n \n \n \n\n\n \n Jiao, P.; Guo, X.; Jing, X.; He, D.; Wu, H.; Pan, S.; Gong, M.; and Wang, W.\n\n\n \n\n\n\n IEEE Transactions on Neural Networks and Learning Systems, 33(12): 7400–7413. 2021.\n Publisher: IEEE\n\n\n\n
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@article{jiao_temporal_2021,\n\ttitle = {Temporal network embedding for link prediction via {VAE} joint attention mechanism},\n\tvolume = {33},\n\tcopyright = {All rights reserved},\n\tnumber = {12},\n\tjournal = {IEEE Transactions on Neural Networks and Learning Systems},\n\tauthor = {Jiao, Pengfei and Guo, Xuan and Jing, Xin and He, Dongxiao and Wu, Huaming and Pan, Shirui and Gong, Maoguo and Wang, Wenjun},\n\tyear = {2021},\n\tnote = {Publisher: IEEE},\n\tpages = {7400--7413},\n}\n\n\n\n
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\n \n\n \n \n \n \n \n Towards Extracting Graph Neural Network Models via Prediction Queries (Student Abstract).\n \n \n \n\n\n \n Wu, B.; Pan, S.; and Yuan, X.\n\n\n \n\n\n\n In Proceedings of the AAAI Conference on Artificial Intelligence, volume 35, pages 15925–15926, 2021. \n Issue: 18\n\n\n\n
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@inproceedings{wu_towards_2021,\n\ttitle = {Towards {Extracting} {Graph} {Neural} {Network} {Models} via {Prediction} {Queries} ({Student} {Abstract})},\n\tvolume = {35},\n\tcopyright = {All rights reserved},\n\tbooktitle = {Proceedings of the {AAAI} {Conference} on {Artificial} {Intelligence}},\n\tauthor = {Wu, Bang and Pan, Shirui and Yuan, Xingliang},\n\tyear = {2021},\n\tnote = {Issue: 18},\n\tpages = {15925--15926},\n}\n\n\n\n
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\n \n\n \n \n \n \n \n Graph Learning: A Survey.\n \n \n \n\n\n \n Xia, F.; Sun, K.; Yu, S.; Aziz, A.; Wan, L.; Pan, S.; and Liu, H.\n\n\n \n\n\n\n IEEE Transactions on Artificial Intelligence (TAI). 2021.\n \n\n\n\n
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@article{xia_graph_2021,\n\ttitle = {Graph {Learning}: {A} {Survey}},\n\tcopyright = {All rights reserved},\n\tjournal = {IEEE Transactions on Artificial Intelligence (TAI)},\n\tauthor = {Xia, Feng and Sun, Ke and Yu, Shuo and Aziz, Abdul and Wan, Liangtian and Pan, Shirui and Liu, Huan},\n\tyear = {2021},\n}\n\n\n\n
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\n \n\n \n \n \n \n \n Anomaly Detection on Attributed Networks via Contrastive Self-Supervised Learning.\n \n \n \n\n\n \n Liu, Y.; Li, Z.; Pan, S.; Gong, C.; Zhou, C.; and Karypis, G.\n\n\n \n\n\n\n IEEE Transactions on Neural Networks and Learning Systems (TNNLS). 2021.\n \n\n\n\n
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@article{liu_anomaly_2021,\n\ttitle = {Anomaly {Detection} on {Attributed} {Networks} via {Contrastive} {Self}-{Supervised} {Learning}},\n\tcopyright = {All rights reserved},\n\tjournal = {IEEE Transactions on Neural Networks and Learning Systems (TNNLS)},\n\tauthor = {Liu, Yixin and Li, Zhao and Pan, Shirui and Gong, Chen and Zhou, Chuan and Karypis, George},\n\tyear = {2021},\n}\n\n\n\n
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\n \n\n \n \n \n \n \n A survey of community detection approaches: From statistical modeling to deep learning.\n \n \n \n\n\n \n Jin, D.; Yu, Z.; Jiao, P.; Pan, S.; Yu, P. S; and Zhang, W.\n\n\n \n\n\n\n IEEE Transactions on Knowledge and Data Engineering (TKDE). 2021.\n \n\n\n\n
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@article{jin_survey_2021,\n\ttitle = {A survey of community detection approaches: {From} statistical modeling to deep learning},\n\tcopyright = {All rights reserved},\n\tjournal = {IEEE Transactions on Knowledge and Data Engineering (TKDE)},\n\tauthor = {Jin, Di and Yu, Zhizhi and Jiao, Pengfei and Pan, Shirui and Yu, Philip S and Zhang, Weixiong},\n\tyear = {2021},\n}\n\n\n\n
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\n \n\n \n \n \n \n \n A survey on knowledge graphs: Representation, acquisition and applications.\n \n \n \n\n\n \n Ji, S.; Pan, S.; Cambria, E.; Marttinen, P.; and Yu, P. S\n\n\n \n\n\n\n IEEE Transactions on Neural Networks and Learning Systems. 2021.\n \n\n\n\n
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@article{ji_survey_2021,\n\ttitle = {A survey on knowledge graphs: {Representation}, acquisition and applications},\n\tcopyright = {All rights reserved},\n\tjournal = {IEEE Transactions on Neural Networks and Learning Systems},\n\tauthor = {Ji, Shaoxiong and Pan, Shirui and Cambria, Erik and Marttinen, Pekka and Yu, Philip S},\n\tyear = {2021},\n}\n\n\n\n
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\n \n\n \n \n \n \n \n One-Shot Neural Architecture Search: Maximising Diversity to Overcome Catastrophic Forgetting.\n \n \n \n\n\n \n Zhang, M.; Li, H.; Pan, S.; Chang, X.; Zhou, C.; Ge, Z.; and Su, S. W\n\n\n \n\n\n\n IEEE Transactions on Pattern Analysis and Machine Intelligence, TPAMI, 43(9): 2921–2935. 2021.\n Publisher: IEEE Computer Society\n\n\n\n
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@article{zhang_one-shot_2021,\n\ttitle = {One-{Shot} {Neural} {Architecture} {Search}: {Maximising} {Diversity} to {Overcome} {Catastrophic} {Forgetting}},\n\tvolume = {43},\n\tcopyright = {All rights reserved},\n\tnumber = {9},\n\tjournal = {IEEE Transactions on Pattern Analysis and Machine Intelligence, TPAMI},\n\tauthor = {Zhang, Miao and Li, Huiqi and Pan, Shirui and Chang, Xiaojun and Zhou, Chuan and Ge, Zongyuan and Su, Steven W},\n\tyear = {2021},\n\tnote = {Publisher: IEEE Computer Society},\n\tpages = {2921--2935},\n}\n\n\n\n
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\n \n\n \n \n \n \n \n Contrastive and Generative Graph Convolutional Networks for Graph-based Semi-Supervised Learning.\n \n \n \n\n\n \n Wan, S.; Pan, S.; Yang, J.; and Gong, C.\n\n\n \n\n\n\n In AAAI Conference on Artificial Intelligence, AAAI, 2021. \n \n\n\n\n
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@inproceedings{wan_contrastive_2021,\n\ttitle = {Contrastive and {Generative} {Graph} {Convolutional} {Networks} for {Graph}-based {Semi}-{Supervised} {Learning}},\n\tcopyright = {All rights reserved},\n\tbooktitle = {{AAAI} {Conference} on {Artificial} {Intelligence}, {AAAI}},\n\tauthor = {Wan, Sheng and Pan, Shirui and Yang, Jian and Gong, Chen},\n\tyear = {2021},\n}\n\n\n\n
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\n \n\n \n \n \n \n \n Hypergraph Convolutional Network for Group Recommendation.\n \n \n \n\n\n \n Jia, R.; Zhou, X.; Dong, L.; and Pan, S.\n\n\n \n\n\n\n In IEEE International Conference on Data Mining (ICDM), pages 260–269 (CORE Ranked A*), 2021. \n \n\n\n\n
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@inproceedings{jia_hypergraph_2021,\n\ttitle = {Hypergraph {Convolutional} {Network} for {Group} {Recommendation}},\n\tcopyright = {All rights reserved},\n\tbooktitle = {{IEEE} {International} {Conference} on {Data} {Mining} ({ICDM})},\n\tauthor = {Jia, Renqi and Zhou, Xiaofei and Dong, Linhua and Pan, Shirui},\n\tyear = {2021},\n\tpages = {260--269 (CORE Ranked A*)},\n}\n\n\n\n
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\n \n\n \n \n \n \n \n Multi-Scale Contrastive Siamese Networks for Self-Supervised Graph Representation Learning.\n \n \n \n\n\n \n Jin, M.; Zheng, Y.; Li, Y.; Gong, C.; Zhou, C.; and Pan, S.\n\n\n \n\n\n\n In International Joint Conference on Artificial Intelligence, IJCAI, pages 1477–1483 (CORE Ranked A*), 2021. \n _eprint: 2105.05682\n\n\n\n
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@inproceedings{jin_multi-scale_2021,\n\ttitle = {Multi-{Scale} {Contrastive} {Siamese} {Networks} for {Self}-{Supervised} {Graph} {Representation} {Learning}},\n\tcopyright = {All rights reserved},\n\tabstract = {Graph representation learning plays a vital role in processing graph-structured data. However, prior arts on graph representation learning heavily rely on the labeling information. To overcome this problem, inspired by the recent success of graph contrastive learning and Siamese networks in visual representation learning, we propose a novel self-supervised approach in this paper to learn node representations by enhancing Siamese self-distillation with multi-scale contrastive learning. Specifically, we first generate two augmented views from the input graph based on local and global perspectives. Then, we employ two objectives called cross-view and cross-network contrastiveness to maximize the agreement between node representations across different views and networks. To demonstrate the effectiveness of our approach, we perform empirical experiments on five real-world datasets. Our method not only achieves new state-of-the-art results but also surpasses some semi-supervised counterparts by large margins.},\n\tbooktitle = {International {Joint} {Conference} on {Artificial} {Intelligence}, {IJCAI}},\n\tauthor = {Jin, Ming and Zheng, Yizhen and Li, Yuan-Fang and Gong, Chen and Zhou, Chuan and Pan, Shirui},\n\tyear = {2021},\n\tnote = {\\_eprint: 2105.05682},\n\tpages = {1477--1483 (CORE Ranked A*)},\n}\n\n\n\n
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\n Graph representation learning plays a vital role in processing graph-structured data. However, prior arts on graph representation learning heavily rely on the labeling information. To overcome this problem, inspired by the recent success of graph contrastive learning and Siamese networks in visual representation learning, we propose a novel self-supervised approach in this paper to learn node representations by enhancing Siamese self-distillation with multi-scale contrastive learning. Specifically, we first generate two augmented views from the input graph based on local and global perspectives. Then, we employ two objectives called cross-view and cross-network contrastiveness to maximize the agreement between node representations across different views and networks. To demonstrate the effectiveness of our approach, we perform empirical experiments on five real-world datasets. Our method not only achieves new state-of-the-art results but also surpasses some semi-supervised counterparts by large margins.\n
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\n \n\n \n \n \n \n \n Contrastive and Generative Graph Convolutional Networks for Graph-based Semi-Supervised Learning.\n \n \n \n\n\n \n Wan, S.; Pan, S.; Yang, J.; and Gong, C.\n\n\n \n\n\n\n In Thirty-Fifth \\AAAI\\ Conference on Artificial Intelligence, \\AAAI\\ 2021, pages 10049–10057 (CORE Ranked A*), 2021. \\AAAI\\ Press\n \n\n\n\n
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@inproceedings{wan_contrastive_2021,\n\ttitle = {Contrastive and {Generative} {Graph} {Convolutional} {Networks} for {Graph}-based {Semi}-{Supervised} {Learning}},\n\tcopyright = {All rights reserved},\n\tbooktitle = {Thirty-{Fifth} \\{{AAAI}\\} {Conference} on {Artificial} {Intelligence}, \\{{AAAI}\\} 2021},\n\tpublisher = {\\{AAAI\\} Press},\n\tauthor = {Wan, Sheng and Pan, Shirui and Yang, Jian and Gong, Chen},\n\tyear = {2021},\n\tpages = {10049--10057 (CORE Ranked A*)},\n}\n\n\n\n
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\n \n\n \n \n \n \n \n Task-adaptive Neural Process for User Cold-Start Recommendation.\n \n \n \n\n\n \n Lin, X.; Wu, J.; Zhou, C.; Pan, S.; Cao, Y.; and Wang, B.\n\n\n \n\n\n\n In Leskovec, J.; Grobelnik, M.; Najork, M.; Tang, J.; and Zia, L., editor(s), The Web Conference (WWW), Virtual Event / Ljubljana, Slovenia, April 19-23, 2021, pages 1306–1316, 2021. \\ACM\\ / \\IW3C2\\\n \n\n\n\n
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@inproceedings{lin_task-adaptive_2021,\n\ttitle = {Task-adaptive {Neural} {Process} for {User} {Cold}-{Start} {Recommendation}},\n\tcopyright = {All rights reserved},\n\tdoi = {10.1145/3442381.3449908 (CORE Ranked A*)},\n\tbooktitle = {The {Web} {Conference} ({WWW}), {Virtual} {Event} / {Ljubljana}, {Slovenia}, {April} 19-23, 2021},\n\tpublisher = {\\{ACM\\} / \\{IW3C2\\}},\n\tauthor = {Lin, Xixun and Wu, Jia and Zhou, Chuan and Pan, Shirui and Cao, Yanan and Wang, Bin},\n\teditor = {Leskovec, Jure and Grobelnik, Marko and Najork, Marc and Tang, Jie and Zia, Leila},\n\tyear = {2021},\n\tpages = {1306--1316},\n}\n\n\n\n
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\n \n\n \n \n \n \n \n iDARTS: Differentiable Architecture Search with Stochastic Implicit Gradients.\n \n \n \n\n\n \n Zhang, M.; Su, S.; Pan, S.; Chang, X.; Abbasnejad, E.; and Haffari, R.\n\n\n \n\n\n\n In International Conference on Machine Learning (ICML), pages 12557–12566 (CORE Ranked A*), 2021. \n _eprint: 2106.10784\n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@inproceedings{zhang_idarts:_2021,\n\ttitle = {{iDARTS}: {Differentiable} {Architecture} {Search} with {Stochastic} {Implicit} {Gradients}},\n\tcopyright = {All rights reserved},\n\tabstract = {\\${\\textbackslash}backslash\\$textit\\{Differentiable ARchiTecture Search\\} (DARTS) has recently become the mainstream of neural architecture search (NAS) due to its efficiency and simplicity. With a gradient-based bi-level optimization, DARTS alternately optimizes the inner model weights and the outer architecture parameter in a weight-sharing supernet. A key challenge to the scalability and quality of the learned architectures is the need for differentiating through the inner-loop optimisation. While much has been discussed about several potentially fatal factors in DARTS, the architecture gradient, a.k.a. hypergradient, has received less attention. In this paper, we tackle the hypergradient computation in DARTS based on the implicit function theorem, making it only depends on the obtained solution to the inner-loop optimization and agnostic to the optimization path. To further reduce the computational requirements, we formulate a stochastic hypergradient approximation for differentiable NAS, and theoretically show that the architecture optimization with the proposed method, named iDARTS, is expected to converge to a stationary point. Comprehensive experiments on two NAS benchmark search spaces and the common NAS search space verify the effectiveness of our proposed method. It leads to architectures outperforming, with large margins, those learned by the baseline methods.},\n\tbooktitle = {International {Conference} on {Machine} {Learning} ({ICML})},\n\tauthor = {Zhang, Miao and Su, Steven and Pan, Shirui and Chang, Xiaojun and Abbasnejad, Ehsan and Haffari, Reza},\n\tyear = {2021},\n\tnote = {\\_eprint: 2106.10784},\n\tpages = {12557--12566 (CORE Ranked A*)},\n}\n\n\n\n
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\n ${\\}backslash$textit\\Differentiable ARchiTecture Search\\ (DARTS) has recently become the mainstream of neural architecture search (NAS) due to its efficiency and simplicity. With a gradient-based bi-level optimization, DARTS alternately optimizes the inner model weights and the outer architecture parameter in a weight-sharing supernet. A key challenge to the scalability and quality of the learned architectures is the need for differentiating through the inner-loop optimisation. While much has been discussed about several potentially fatal factors in DARTS, the architecture gradient, a.k.a. hypergradient, has received less attention. In this paper, we tackle the hypergradient computation in DARTS based on the implicit function theorem, making it only depends on the obtained solution to the inner-loop optimization and agnostic to the optimization path. To further reduce the computational requirements, we formulate a stochastic hypergradient approximation for differentiable NAS, and theoretically show that the architecture optimization with the proposed method, named iDARTS, is expected to converge to a stationary point. Comprehensive experiments on two NAS benchmark search spaces and the common NAS search space verify the effectiveness of our proposed method. It leads to architectures outperforming, with large margins, those learned by the baseline methods.\n
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\n \n\n \n \n \n \n \n Identify Topic Relations in Scientific Literature Using Topic Modeling.\n \n \n \n\n\n \n Chen, H.; Wang, X.; Pan, S.; and Xiong, F.\n\n\n \n\n\n\n IEEE Transactions on Engineering Management (TEM), 68(5): 1232–1244. 2021.\n \n\n\n\n
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@article{chen_identify_2021,\n\ttitle = {Identify {Topic} {Relations} in {Scientific} {Literature} {Using} {Topic} {Modeling}},\n\tvolume = {68},\n\tcopyright = {All rights reserved},\n\tdoi = {10.1109/TEM.2019.2903115 (Impact Factor: 6.146)},\n\tnumber = {5},\n\tjournal = {IEEE Transactions on Engineering Management (TEM)},\n\tauthor = {Chen, Hongshu and Wang, Ximeng and Pan, Shirui and Xiong, Fei},\n\tyear = {2021},\n\tpages = {1232--1244},\n}\n\n\n\n
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\n \n\n \n \n \n \n \n Suicidal Ideation Detection: A Review of Machine Learning Methods and Applications.\n \n \n \n\n\n \n Ji, S.; Pan, S.; Li, X.; Cambria, E.; Long, G.; and Huang, Z.\n\n\n \n\n\n\n IEEE Transactions on Computational Social Systems (TCSS), 8(1): 214–226. 2021.\n \n\n\n\n
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@article{ji_suicidal_2021,\n\ttitle = {Suicidal {Ideation} {Detection}: {A} {Review} of {Machine} {Learning} {Methods} and {Applications}},\n\tvolume = {8},\n\tcopyright = {All rights reserved},\n\tdoi = {10.1109/TCSS.2020.3021467},\n\tnumber = {1},\n\tjournal = {IEEE Transactions on Computational Social Systems (TCSS)},\n\tauthor = {Ji, Shaoxiong and Pan, Shirui and Li, Xue and Cambria, Erik and Long, Guodong and Huang, Zi},\n\tyear = {2021},\n\tpages = {214--226},\n}\n\n\n\n
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\n \n\n \n \n \n \n \n Contrastive Graph Poisson Networks: Semi-Supervised Learning with Extremely Limited Labels.\n \n \n \n\n\n \n Wan, S.; Zhan, Y.; Liu, L.; Yu, B.; Pan, S.; and Gong, C.\n\n\n \n\n\n\n In Thirty-fifth Conference on Neural Information Processing Systems (NeurIPS-21), Virtual Conference, Dec 6 - Dec 14, 2021 (CORE Ranked A*; Top Conference in Machine Learning), 2021. \n \n\n\n\n
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@inproceedings{wan_contrastive_2021,\n\ttitle = {Contrastive {Graph} {Poisson} {Networks}: {Semi}-{Supervised} {Learning} with {Extremely} {Limited} {Labels}},\n\tcopyright = {All rights reserved},\n\tbooktitle = {Thirty-fifth {Conference} on {Neural} {Information} {Processing} {Systems} ({NeurIPS}-21), {Virtual} {Conference}, {Dec} 6 - {Dec} 14, 2021 ({CORE} {Ranked} {A}*; {Top} {Conference} in {Machine} {Learning})},\n\tauthor = {Wan, Sheng and Zhan, Yibing and Liu, Liu and Yu, Baosheng and Pan, Shirui and Gong, Chen},\n\tyear = {2021},\n}\n\n\n\n
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\n \n\n \n \n \n \n \n Temporal Network Embedding for Link Prediction via VAE Joint Attention Mechanism.\n \n \n \n\n\n \n Jiao, P.; Guo, X.; Jing, X.; He, D.; Wu, H.; Pan, S.; Gong, M.; and Wang, W.\n\n\n \n\n\n\n IEEE Transactions on Neural Networks and Learning Systems (TNNLS),1–14. 2021.\n \n\n\n\n
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@article{jiao_temporal_2021,\n\ttitle = {Temporal {Network} {Embedding} for {Link} {Prediction} via {VAE} {Joint} {Attention} {Mechanism}},\n\tcopyright = {All rights reserved},\n\tdoi = {10.1109/TNNLS.2021.3084957 (Impact Factor: 10.451; JCR Ranked Q1)},\n\tjournal = {IEEE Transactions on Neural Networks and Learning Systems (TNNLS)},\n\tauthor = {Jiao, Pengfei and Guo, Xuan and Jing, Xin and He, Dongxiao and Wu, Huaming and Pan, Shirui and Gong, Maoguo and Wang, Wenjun},\n\tyear = {2021},\n\tpages = {1--14},\n}\n\n\n\n
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\n \n\n \n \n \n \n \n IGNSCDA: Predicting CircRNA-Disease Associations Based on Improved Graph Convolutional Network and Negative Sampling.\n \n \n \n\n\n \n Lan, W.; Dong, Y.; Chen, Q.; Liu, J.; Wang, J.; Chen, Y. P.; and Pan, S.\n\n\n \n\n\n\n IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB),1. 2021.\n \n\n\n\n
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@article{lan_ignscda:_2021,\n\ttitle = {{IGNSCDA}: {Predicting} {CircRNA}-{Disease} {Associations} {Based} on {Improved} {Graph} {Convolutional} {Network} and {Negative} {Sampling}},\n\tcopyright = {All rights reserved},\n\tdoi = {10.1109/TCBB.2021.3111607 (Impact Factor: 3.71)},\n\tjournal = {IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)},\n\tauthor = {Lan, Wei and Dong, Yi and Chen, Qingfeng and Liu, Jin and Wang, Jianxin and Chen, Yi-Ping Phoebe and Pan, Shirui},\n\tyear = {2021},\n\tpages = {1},\n}\n\n\n\n
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\n \n\n \n \n \n \n \n Anomaly Detection in Dynamic Graphs via Transformer.\n \n \n \n\n\n \n Liu, Y.; Pan, S.; Wang, Y. G.; Xiong, F.; Wang, L.; Chen, Q.; and Lee, V. C S\n\n\n \n\n\n\n IEEE Transactions on Knowledge and Data Engineering (TKDE),1–14. 2021.\n \n\n\n\n
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@article{liu_anomaly_2021,\n\ttitle = {Anomaly {Detection} in {Dynamic} {Graphs} via {Transformer}},\n\tcopyright = {All rights reserved},\n\tdoi = {10.1109/TKDE.2021.3124061 (Impact Factor: 6.977; JCR Ranked Q1; Top Journal in Data Mining)},\n\tjournal = {IEEE Transactions on Knowledge and Data Engineering (TKDE)},\n\tauthor = {Liu, Yixin and Pan, Shirui and Wang, Yu Guang and Xiong, Fei and Wang, Liang and Chen, Qingfeng and Lee, Vincent C S},\n\tyear = {2021},\n\tpages = {1--14},\n}\n\n\n\n
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\n \n\n \n \n \n \n \n Hyperspectral Image Classification with Context-aware Dynamic Graph Convolutional Networks.\n \n \n \n\n\n \n Wan, S.; Zhong, P.; Pan, S.; Yang, J.; Li, G.; and Gong, C.\n\n\n \n\n\n\n IEEE Transactions on Geoscience and Remote Sensing (TGRS), 59(1): 597–612. 2021.\n Publisher: IEEE\n\n\n\n
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@article{wan_hyperspectral_2021,\n\ttitle = {Hyperspectral {Image} {Classification} with {Context}-aware {Dynamic} {Graph} {Convolutional} {Networks}},\n\tvolume = {59},\n\tcopyright = {All rights reserved},\n\tdoi = {10.1109/TGRS.2020.2994205 (Impact Factor: 5.6; JCR Ranked Q1)},\n\tnumber = {1},\n\tjournal = {IEEE Transactions on Geoscience and Remote Sensing (TGRS)},\n\tauthor = {Wan, Sheng and Zhong, Ping and Pan, Shirui and Yang, Jian and Li, Guangyu and Gong, Chen},\n\tyear = {2021},\n\tnote = {Publisher: IEEE},\n\tpages = {597--612},\n}\n\n\n\n
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\n \n\n \n \n \n \n \n Influence Spread in Geo-Social Networks: A Multiobjective Optimization Perspective.\n \n \n \n\n\n \n Wang, L.; Yu, Z.; Xiong, F.; Yang, D.; Pan, S.; and Yan, Z.\n\n\n \n\n\n\n IEEE Transactions on Cybernetics (TCYB), 51(5): 2663–2675. 2021.\n \n\n\n\n
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@article{wang_influence_2021,\n\ttitle = {Influence {Spread} in {Geo}-{Social} {Networks}: {A} {Multiobjective} {Optimization} {Perspective}},\n\tvolume = {51},\n\tcopyright = {All rights reserved},\n\tdoi = {doi.org/10.1109/TCYB.2019.2906078  (Impact Factor: 11.448; JCR Ranked Q1)},\n\tnumber = {5},\n\tjournal = {IEEE Transactions on Cybernetics (TCYB)},\n\tauthor = {Wang, Liang and Yu, Zhiwen and Xiong, Fei and Yang, Dingqi and Pan, Shirui and Yan, Zheng},\n\tyear = {2021},\n\tpages = {2663--2675},\n}\n\n\n\n
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\n \n\n \n \n \n \n \n Learning Graph Neural Networks with Positive and Unlabeled Nodes.\n \n \n \n\n\n \n Wu, M.; Pan, S.; Du, L.; and Zhu, X.\n\n\n \n\n\n\n ACM Transactions on Knowledge Discovery from Data (TKDD), 15(6): 101:1–101:25. 2021.\n \n\n\n\n
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@article{wu_learning_2021,\n\ttitle = {Learning {Graph} {Neural} {Networks} with {Positive} and {Unlabeled} {Nodes}},\n\tvolume = {15},\n\tcopyright = {All rights reserved},\n\tdoi = {10.1145/3450316 (Impact Factor: 2.01)},\n\tnumber = {6},\n\tjournal = {ACM Transactions on Knowledge Discovery from Data (TKDD)},\n\tauthor = {Wu, Man and Pan, Shirui and Du, Lan and Zhu, Xingquan},\n\tyear = {2021},\n\tpages = {101:1--101:25},\n}\n\n\n\n
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\n \n\n \n \n \n \n \n A Comprehensive Survey on Graph Neural Networks.\n \n \n \n\n\n \n Wu, Z.; Pan, S.; Chen, F.; Long, G.; Zhang, C.; and Yu, P. S.\n\n\n \n\n\n\n IEEE Transactions on Neural Networks and Learning Systems (TNNLS), 32(1): 4–24. 2021.\n Publisher: IEEE\n\n\n\n
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@article{wu_comprehensive_2021,\n\ttitle = {A {Comprehensive} {Survey} on {Graph} {Neural} {Networks}},\n\tvolume = {32},\n\tcopyright = {All rights reserved},\n\tdoi = {10.1109/TNNLS.2020.2978386 (Impact Factor: 10.451; JCR Ranked Q1; Citations: 4000)},\n\tnumber = {1},\n\tjournal = {IEEE Transactions on Neural Networks and Learning Systems (TNNLS)},\n\tauthor = {Wu, Zonghan and Pan, Shirui and Chen, Fengwen and Long, Guodong and Zhang, Chengqi and Yu, Philip S.},\n\tyear = {2021},\n\tnote = {Publisher: IEEE},\n\tpages = {4--24},\n}\n\n\n\n
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\n \n\n \n \n \n \n \n Graph Learning: A Survey.\n \n \n \n\n\n \n Xia, F.; Sun, K.; Yu, S.; Aziz, A.; Wan, L.; Pan, S.; and Liu, H.\n\n\n \n\n\n\n IEEE Transactions on Artificial Intelligence (TAI), 2(2): 109–127. 2021.\n \n\n\n\n
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@article{xia_graph_2021,\n\ttitle = {Graph {Learning}: {A} {Survey}},\n\tvolume = {2},\n\tcopyright = {All rights reserved},\n\tdoi = {10.1109/TAI.2021.3076021},\n\tnumber = {2},\n\tjournal = {IEEE Transactions on Artificial Intelligence (TAI)},\n\tauthor = {Xia, Feng and Sun, Ke and Yu, Shuo and Aziz, Abdul and Wan, Liangtian and Pan, Shirui and Liu, Huan},\n\tyear = {2021},\n\tpages = {109--127},\n}\n\n\n\n
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\n \n\n \n \n \n \n \n Convolutional Neural Networks based Lung Nodule Classification: A Surrogate-Assisted Evolutionary Algorithm for Hyperparameter Optimization.\n \n \n \n\n\n \n Zhang, M.; Li, H.; Pan, S.; Lyu, J.; Ling, S.; and Su, S.\n\n\n \n\n\n\n IEEE Transactions on Evolutionary Computation (TEvC), 25(5): 869–882. 2021.\n \n\n\n\n
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@article{zhang_convolutional_2021,\n\ttitle = {Convolutional {Neural} {Networks} based {Lung} {Nodule} {Classification}: {A} {Surrogate}-{Assisted} {Evolutionary} {Algorithm} for {Hyperparameter} {Optimization}},\n\tvolume = {25},\n\tcopyright = {All rights reserved},\n\tdoi = {10.1109/TEVC.2021.3060833 (Impact Factor: 11.554; JCR Ranked Q1)},\n\tnumber = {5},\n\tjournal = {IEEE Transactions on Evolutionary Computation (TEvC)},\n\tauthor = {Zhang, Miao and Li, Huiqi and Pan, Shirui and Lyu, Juan and Ling, Steve and Su, Steven},\n\tyear = {2021},\n\tpages = {869--882},\n}\n\n\n\n
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\n \n\n \n \n \n \n \n One-Shot Neural Architecture Search: Maximising Diversity to Overcome Catastrophic Forgetting.\n \n \n \n\n\n \n Zhang, M.; Li, H.; Pan, S.; Chang, X.; Zhou, C.; Ge, Z.; and W. Su, S.\n\n\n \n\n\n\n IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 43(9): 2921–2935. 2021.\n \n\n\n\n
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@article{zhang_one-shot_2021,\n\ttitle = {One-{Shot} {Neural} {Architecture} {Search}: {Maximising} {Diversity} to {Overcome} {Catastrophic} {Forgetting}},\n\tvolume = {43},\n\tcopyright = {All rights reserved},\n\tdoi = {10.1109/TPAMI.2020.3035351 (Impact Factor: 16.389; JCR Ranked Q1; Top Journal in AI)},\n\tnumber = {9},\n\tjournal = {IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI)},\n\tauthor = {Zhang, Miao and Li, Huiqi and Pan, Shirui and Chang, Xiaojun and Zhou, Chuan and Ge, Zongyuan and W. Su, Steven},\n\tyear = {2021},\n\tpages = {2921--2935},\n}\n\n\n\n
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\n  \n 2020\n \n \n (42)\n \n \n
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\n \n\n \n \n \n \n \n Cross-graph: robust and unsupervised embedding for attributed graphs with corrupted structure.\n \n \n \n\n\n \n Wang, C.; Han, B.; Pan, S.; Jiang, J.; Niu, G.; and Long, G.\n\n\n \n\n\n\n In 2020 IEEE International Conference on Data Mining (ICDM), pages 571–580, 2020. IEEE\n \n\n\n\n
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@inproceedings{wang_cross-graph:_2020,\n\ttitle = {Cross-graph: robust and unsupervised embedding for attributed graphs with corrupted structure},\n\tcopyright = {All rights reserved},\n\tbooktitle = {2020 {IEEE} {International} {Conference} on {Data} {Mining} ({ICDM})},\n\tpublisher = {IEEE},\n\tauthor = {Wang, Chun and Han, Bo and Pan, Shirui and Jiang, Jing and Niu, Gang and Long, Guodong},\n\tyear = {2020},\n\tpages = {571--580},\n}\n\n\n\n
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\n \n\n \n \n \n \n \n Compact scheduling for task graph oriented mobile crowdsourcing.\n \n \n \n\n\n \n Wang, L.; Yu, Z.; Han, Q.; Yang, D.; Pan, S.; Yao, Y.; and Zhang, D.\n\n\n \n\n\n\n IEEE Transactions on Mobile Computing, 21(7): 2358–2371. 2020.\n Publisher: IEEE\n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@article{wang_compact_2020,\n\ttitle = {Compact scheduling for task graph oriented mobile crowdsourcing},\n\tvolume = {21},\n\tcopyright = {All rights reserved},\n\tnumber = {7},\n\tjournal = {IEEE Transactions on Mobile Computing},\n\tauthor = {Wang, Liang and Yu, Zhiwen and Han, Qi and Yang, Dingqi and Pan, Shirui and Yao, Yuan and Zhang, Daqing},\n\tyear = {2020},\n\tnote = {Publisher: IEEE},\n\tpages = {2358--2371},\n}\n\n\n\n
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\n \n\n \n \n \n \n \n OpenWGL: Open-World Graph Learning.\n \n \n \n\n\n \n Wu, M.; Pan, S.; and Zhu, X.\n\n\n \n\n\n\n In IEEE International Conference on Data Mining, ICDM-20, November 17-20, 2020, Sorrento, Italy, 2020. \n \n\n\n\n
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@inproceedings{wu_openwgl:_2020,\n\ttitle = {{OpenWGL}: {Open}-{World} {Graph} {Learning}},\n\tcopyright = {All rights reserved},\n\tbooktitle = {{IEEE} {International} {Conference} on {Data} {Mining}, {ICDM}-20, {November} 17-20, 2020, {Sorrento}, {Italy}},\n\tauthor = {Wu, Man and Pan, Shirui and Zhu, Xingquan},\n\tyear = {2020},\n}\n\n\n\n
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\n \n\n \n \n \n \n \n Graph Stochastic Neural Networks for Semi-supervised Learning.\n \n \n \n\n\n \n Wang, H.; Zhou, C.; Chen, X.; Wu, J.; Pan, S.; and Wang, J.\n\n\n \n\n\n\n In Advances in Neural Information Processing Systems, NeurIPS-20, 2020. \n \n\n\n\n
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@inproceedings{wang_graph_2020,\n\ttitle = {Graph {Stochastic} {Neural} {Networks} for {Semi}-supervised {Learning}},\n\tcopyright = {All rights reserved},\n\tbooktitle = {Advances in {Neural} {Information} {Processing} {Systems}, {NeurIPS}-20},\n\tauthor = {Wang, Haibo and Zhou, Chuan and Chen, Xin and Wu, Jia and Pan, Shirui and Wang, Jilong},\n\tyear = {2020},\n}\n\n\n\n
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\n \n\n \n \n \n \n \n Differentiable Neural Architecture Search in Equivalent Space with Exploration Enhancement.\n \n \n \n\n\n \n Zhang, M.; Li, H.; Pan, S.; Chang, X.; Ge, Z.; and Su, S.\n\n\n \n\n\n\n In Advances in Neural Information Processing Systems, NeurIPS-20, 2020. \n \n\n\n\n
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@inproceedings{zhang_differentiable_2020,\n\ttitle = {Differentiable {Neural} {Architecture} {Search} in {Equivalent} {Space} with {Exploration} {Enhancement}},\n\tcopyright = {All rights reserved},\n\tbooktitle = {Advances in {Neural} {Information} {Processing} {Systems}, {NeurIPS}-20},\n\tauthor = {Zhang, Miao and Li, Huiqi and Pan, Shirui and Chang, Xiaojun and Ge, Zongyuan and Su, Steven},\n\tyear = {2020},\n}\n\n\n\n
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\n \n\n \n \n \n \n \n Graph Geometry Interaction Learning.\n \n \n \n\n\n \n Zhu, S.; Pan, S.; Zhou, C.; Wu, J.; Cao, Y.; and Wang, B.\n\n\n \n\n\n\n In Advances in Neural Information Processing Systems, NeurIPS-20, 2020. \n \n\n\n\n
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@inproceedings{zhu_graph_2020,\n\ttitle = {Graph {Geometry} {Interaction} {Learning}},\n\tcopyright = {All rights reserved},\n\tbooktitle = {Advances in {Neural} {Information} {Processing} {Systems}, {NeurIPS}-20},\n\tauthor = {Zhu, Shichao and Pan, Shirui and Zhou, Chuan and Wu, Jia and Cao, Yanan and Wang, Bin},\n\tyear = {2020},\n}\n\n\n\n
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\n \n\n \n \n \n \n \n Grounding Visual Concepts for Zero-Shot Event Detection and Event Captioning.\n \n \n \n\n\n \n Li, Z.; Chang, X.; Yao, L.; Pan, S.; Zongyuan, G.; and Zhang, H.\n\n\n \n\n\n\n In ACM SIGKDD Conference on Knowledge Discovery and Data Mining, KDD-20, pages 297–305, 2020. \n \n\n\n\n
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@inproceedings{li_grounding_2020,\n\ttitle = {Grounding {Visual} {Concepts} for {Zero}-{Shot} {Event} {Detection} and {Event} {Captioning}},\n\tcopyright = {All rights reserved},\n\tbooktitle = {{ACM} {SIGKDD} {Conference} on {Knowledge} {Discovery} and {Data} {Mining}, {KDD}-20},\n\tauthor = {Li, Zhihui and Chang, Xiaojun and Yao, Lina and Pan, Shirui and Zongyuan, Ge and Zhang, Huaxiang},\n\tyear = {2020},\n\tpages = {297--305},\n}\n\n\n\n
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\n \n\n \n \n \n \n \n Connecting the Dots: Multivariate Time Series Forecasting with Graph Neural Networks.\n \n \n \n\n\n \n Wu, Z.; Pan, S.; Long, G.; Jiang, J.; Chang, X.; and Zhang, C.\n\n\n \n\n\n\n In ACM SIGKDD Conference on Knowledge Discovery and Data Mining, KDD-20, 2020. \n \n\n\n\n
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@inproceedings{wu_connecting_2020,\n\ttitle = {Connecting the {Dots}: {Multivariate} {Time} {Series} {Forecasting} with {Graph} {Neural} {Networks}},\n\tcopyright = {All rights reserved},\n\tbooktitle = {{ACM} {SIGKDD} {Conference} on {Knowledge} {Discovery} and {Data} {Mining}, {KDD}-20},\n\tauthor = {Wu, Zonghan and Pan, Shirui and Long, Guodong and Jiang, Jing and Chang, Xiaojun and Zhang, Chengqi},\n\tyear = {2020},\n}\n\n\n\n
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\n \n\n \n \n \n \n \n A Relation-Specific Attention Network for Joint Entity and Relation Extraction.\n \n \n \n\n\n \n Yuan, Y.; Zhou, X.; Pan, S.; Zhu, Q.; Song, Z.; and Guo, L.\n\n\n \n\n\n\n In International Joint Conference on Artificial Intelligence, IJCAI-20, 2020. \n \n\n\n\n
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@inproceedings{yuan_relation-specific_2020,\n\ttitle = {A {Relation}-{Specific} {Attention} {Network} for {Joint} {Entity} and {Relation} {Extraction}},\n\tcopyright = {All rights reserved},\n\tbooktitle = {International {Joint} {Conference} on {Artificial} {Intelligence}, {IJCAI}-20},\n\tauthor = {Yuan, Yue and Zhou, Xiaofei and Pan, Shirui and Zhu, Qiannan and Song, Zeliang and Guo, Li},\n\tyear = {2020},\n}\n\n\n\n
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\n \n\n \n \n \n \n \n One-Shot Neural Architecture Search via Novelty Driven Sampling.\n \n \n \n\n\n \n Zhang, M.; Li, H.; Pan, S.; Liu, T.; and Su, S.\n\n\n \n\n\n\n In International Joint Conference on Artificial Intelligence, IJCAI-20, 2020. \n \n\n\n\n
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@inproceedings{zhang_one-shot_2020,\n\ttitle = {One-{Shot} {Neural} {Architecture} {Search} via {Novelty} {Driven} {Sampling}},\n\tcopyright = {All rights reserved},\n\tbooktitle = {International {Joint} {Conference} on {Artificial} {Intelligence}, {IJCAI}-20},\n\tauthor = {Zhang, Miao and Li, Huiqi and Pan, Shirui and Liu, Taoping and Su, Steven},\n\tyear = {2020},\n}\n\n\n\n
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\n \n\n \n \n \n \n \n Going Deep: Graph Convolutional Ladder-Shape Networks.\n \n \n \n\n\n \n Hu, R.; Pan, S.; Long, G.; Lu, Q.; Zhu, L.; and Jiang, J.\n\n\n \n\n\n\n In AAAI Conference on Artificial Intelligence, AAAI-20, 2020. \n \n\n\n\n
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@inproceedings{hu_going_2020,\n\ttitle = {Going {Deep}: {Graph} {Convolutional} {Ladder}-{Shape} {Networks}},\n\tcopyright = {All rights reserved},\n\tbooktitle = {{AAAI} {Conference} on {Artificial} {Intelligence}, {AAAI}-20},\n\tauthor = {Hu, Ruiqi and Pan, Shirui and Long, Guodong and Lu, Qinghua and Zhu, Liming and Jiang, Jing},\n\tyear = {2020},\n}\n\n\n\n
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\n \n\n \n \n \n \n \n A comprehensive survey on graph neural networks.\n \n \n \n\n\n \n Wu, Z.; Pan, S.; Chen, F.; Long, G.; Zhang, C.; and Yu, P. S\n\n\n \n\n\n\n IEEE Transactions on Neural Networks and Learning Systems, 32(1): 4–24. 2020.\n Publisher: IEEE\n\n\n\n
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@article{wu_comprehensive_2020,\n\ttitle = {A comprehensive survey on graph neural networks},\n\tvolume = {32},\n\tcopyright = {All rights reserved},\n\tnumber = {1},\n\tjournal = {IEEE Transactions on Neural Networks and Learning Systems},\n\tauthor = {Wu, Zonghan and Pan, Shirui and Chen, Fengwen and Long, Guodong and Zhang, Chengqi and Yu, Philip S},\n\tyear = {2020},\n\tnote = {Publisher: IEEE},\n\tpages = {4--24},\n}\n\n\n\n
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\n \n\n \n \n \n \n \n Medical code assignment with gated convolution and note-code interaction.\n \n \n \n\n\n \n Ji, S.; Pan, S.; and Marttinen, P.\n\n\n \n\n\n\n In ACL Findings, 2020. \n \n\n\n\n
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@inproceedings{ji_medical_2020,\n\ttitle = {Medical code assignment with gated convolution and note-code interaction},\n\tcopyright = {All rights reserved},\n\tbooktitle = {{ACL} {Findings}},\n\tauthor = {Ji, Shaoxiong and Pan, Shirui and Marttinen, Pekka},\n\tyear = {2020},\n}\n\n\n\n
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\n \n\n \n \n \n \n \n Reasoning Like Human: Hierarchical Reinforcement Learning for Knowledge Graph Reasoning.\n \n \n \n\n\n \n Wan, G.; Pan, S.; Gong, C.; Zhou, C.; and Haffari, G.\n\n\n \n\n\n\n In International Joint Conference on Artificial Intelligence, IJCAI-20, 2020. \n \n\n\n\n
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@inproceedings{wan_reasoning_2020,\n\ttitle = {Reasoning {Like} {Human}: {Hierarchical} {Reinforcement} {Learning} for {Knowledge} {Graph} {Reasoning}},\n\tcopyright = {All rights reserved},\n\tbooktitle = {International {Joint} {Conference} on {Artificial} {Intelligence}, {IJCAI}-20},\n\tauthor = {Wan, Guojia and Pan, Shirui and Gong, Chen and Zhou, Chuan and Haffari, Gholamreza},\n\tyear = {2020},\n}\n\n\n\n
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\n \n\n \n \n \n \n \n Reinforcement Learning based Meta-path Discovery in Large-scale Heterogeneous Information Networks.\n \n \n \n\n\n \n Wan, G.; Du, B.; Pan, S.; and Haffari, G.\n\n\n \n\n\n\n In AAAI Conference on Artificial Intelligence, AAAI-20, 2020. \n \n\n\n\n
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@inproceedings{wan_reinforcement_2020,\n\ttitle = {Reinforcement {Learning} based {Meta}-path {Discovery} in {Large}-scale {Heterogeneous} {Information} {Networks}},\n\tcopyright = {All rights reserved},\n\tbooktitle = {{AAAI} {Conference} on {Artificial} {Intelligence}, {AAAI}-20},\n\tauthor = {Wan, Guojia and Du, Bo and Pan, Shirui and Haffari, Gholamreza},\n\tyear = {2020},\n}\n\n\n\n
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\n \n\n \n \n \n \n \n Overcoming Multi-Model Forgetting in One-Shot NAS with Diversity Maximization.\n \n \n \n\n\n \n Zhang, M.; Li, H.; Pan, S.; Chang, X.; and Su, S.\n\n\n \n\n\n\n In IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR-20, 2020. \n \n\n\n\n
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@inproceedings{zhang_overcoming_2020,\n\ttitle = {Overcoming {Multi}-{Model} {Forgetting} in {One}-{Shot} {NAS} with {Diversity} {Maximization}},\n\tcopyright = {All rights reserved},\n\tbooktitle = {{IEEE}/{CVF} {Conference} on {Computer} {Vision} and {Pattern} {Recognition}, {CVPR}-20},\n\tauthor = {Zhang, Miao and Li, Huiqi and Pan, Shirui and Chang, Xiaojun and Su, Steven},\n\tyear = {2020},\n}\n\n\n\n
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\n \n\n \n \n \n \n \n Unsupervised Domain Adaptive Graph Convolutional Networks.\n \n \n \n\n\n \n Wu, M.; Pan, S.; Zhou, C.; Chang, X.; and Zhu, X.\n\n\n \n\n\n\n In The Web Conference, WWW-20, 2020. \n \n\n\n\n
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@inproceedings{wu_unsupervised_2020,\n\ttitle = {Unsupervised {Domain} {Adaptive} {Graph} {Convolutional} {Networks}},\n\tcopyright = {All rights reserved},\n\tbooktitle = {The {Web} {Conference}, {WWW}-20},\n\tauthor = {Wu, Man and Pan, Shirui and Zhou, Chuan and Chang, Xiaojun and Zhu, Xingquan},\n\tyear = {2020},\n}\n\n\n\n
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\n \n\n \n \n \n \n \n GSSNN: Graph Smoothing Splines Neural Networks.\n \n \n \n\n\n \n Zhu, S.; Zhou, L.; Pan, S.; Zhou, C.; Yan, G.; and Wang, B.\n\n\n \n\n\n\n In AAAI Conference on Artificial Intelligence, AAAI-20, 2020. \n \n\n\n\n
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@inproceedings{zhu_gssnn:_2020,\n\ttitle = {{GSSNN}: {Graph} {Smoothing} {Splines} {Neural} {Networks}},\n\tcopyright = {All rights reserved},\n\tbooktitle = {{AAAI} {Conference} on {Artificial} {Intelligence}, {AAAI}-20},\n\tauthor = {Zhu, Shichao and Zhou, Lewei and Pan, Shirui and Zhou, Chuan and Yan, Guiying and Wang, Bin},\n\tyear = {2020},\n}\n\n\n\n
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\n \n\n \n \n \n \n \n Hyperspectral Image Classification With Context-Aware Dynamic Graph Convolutional Network.\n \n \n \n\n\n \n Wan, S.; Gong, C.; Zhong, P.; Pan, S.; Li, G.; and Yang, J.\n\n\n \n\n\n\n IEEE Transactions on Geoscience and Remote Sensing, TGRS. 2020.\n \n\n\n\n
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@article{wan_hyperspectral_2020,\n\ttitle = {Hyperspectral {Image} {Classification} {With} {Context}-{Aware} {Dynamic} {Graph} {Convolutional} {Network}},\n\tcopyright = {All rights reserved},\n\tjournal = {IEEE Transactions on Geoscience and Remote Sensing, TGRS},\n\tauthor = {Wan, Sheng and Gong, Chen and Zhong, Ping and Pan, Shirui and Li, Guangyu and Yang, Jian},\n\tyear = {2020},\n}\n\n\n\n
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\n \n\n \n \n \n \n \n Learning graph embedding with adversarial training methods.\n \n \n \n\n\n \n Pan, S.; Hu, R.; Fung, S.; Long, G.; Jiang, J.; and Zhang, C.\n\n\n \n\n\n\n IEEE Transactions on Cybernetics (TCYB). 2020.\n \n\n\n\n
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@article{pan_learning_2020,\n\ttitle = {Learning graph embedding with adversarial training methods},\n\tcopyright = {All rights reserved},\n\tjournal = {IEEE Transactions on Cybernetics (TCYB)},\n\tauthor = {Pan, Shirui and Hu, Ruiqi and Fung, Sai-fu and Long, Guodong and Jiang, Jing and Zhang, Chengqi},\n\tyear = {2020},\n}\n\n\n\n
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\n \n\n \n \n \n \n \n Going Deep: Graph Convolutional Ladder-Shape Networks.\n \n \n \n\n\n \n Hu, R.; Pan, S.; Long, G.; Lu, Q.; Zhu, L.; and Jiang, J.\n\n\n \n\n\n\n In Proceedings of the Thirty-Fourth AAAI Conference on Artificial Intelligence, AAAI-20, New York, New York, USA, February 7-12, 2020, pages 2838–2845 (CORE Ranked A*), 2020. \n \n\n\n\n
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@inproceedings{hu_going_2020,\n\ttitle = {Going {Deep}: {Graph} {Convolutional} {Ladder}-{Shape} {Networks}},\n\tcopyright = {All rights reserved},\n\tbooktitle = {Proceedings of the {Thirty}-{Fourth} {AAAI} {Conference} on {Artificial} {Intelligence}, {AAAI}-20, {New} {York}, {New} {York}, {USA}, {February} 7-12, 2020},\n\tauthor = {Hu, Ruiqi and Pan, Shirui and Long, Guodong and Lu, Qinghua and Zhu, Liming and Jiang, Jing},\n\tyear = {2020},\n\tpages = {2838--2845 (CORE Ranked A*)},\n}\n\n\n\n
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\n \n\n \n \n \n \n \n Grounding Visual Concepts for Multimedia Event Detection and Multimedia Event Captioning in Zero-shot Setting.\n \n \n \n\n\n \n Li, Z.; Chang, X.; Yao, L.; Pan, S.; Ge, Z.; and Zhang, H.\n\n\n \n\n\n\n In ACM SIGKDD Conference on Knowledge Discovery and Data Mining, KDD-20, August 23–27, 2020, Virtual Event, CA, USA, pages 297–305 (CORE Ranked A*), 2020. \n \n\n\n\n
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@inproceedings{li_grounding_2020,\n\ttitle = {Grounding {Visual} {Concepts} for {Multimedia} {Event} {Detection} and {Multimedia} {Event} {Captioning} in {Zero}-shot {Setting}},\n\tcopyright = {All rights reserved},\n\tbooktitle = {{ACM} {SIGKDD} {Conference} on {Knowledge} {Discovery} and {Data} {Mining}, {KDD}-20, {August} 23–27, 2020, {Virtual} {Event}, {CA}, {USA}},\n\tauthor = {Li, Zhihui and Chang, Xiaojun and Yao, Lina and Pan, Shirui and Ge, Zongyuan and Zhang, Huaxiang},\n\tyear = {2020},\n\tpages = {297--305 (CORE Ranked A*)},\n}\n\n\n\n
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\n \n\n \n \n \n \n \n Graph Stochastic Neural Networks for Semi-supervised Learning.\n \n \n \n\n\n \n Wang, H.; Zhou, C.; Chen, X.; Wu, J.; Pan, S.; and Wang, J.\n\n\n \n\n\n\n In Thirty-fourth Conference on Neural Information Processing Systems, NeurIPS-20, December 6-12, 2020, Virtual Conference, pages 19839–19848 (CORE Ranked A*; Top Conference in Machine Learning), 2020. \n \n\n\n\n
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@inproceedings{wang_graph_2020,\n\ttitle = {Graph {Stochastic} {Neural} {Networks} for {Semi}-supervised {Learning}},\n\tcopyright = {All rights reserved},\n\tbooktitle = {Thirty-fourth {Conference} on {Neural} {Information} {Processing} {Systems}, {NeurIPS}-20, {December} 6-12, 2020, {Virtual} {Conference}},\n\tauthor = {Wang, Haibo and Zhou, Chuan and Chen, Xin and Wu, Jia and Pan, Shirui and Wang, Jilong},\n\tyear = {2020},\n\tpages = {19839--19848 (CORE Ranked A*; Top Conference in Machine Learning)},\n}\n\n\n\n
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\n \n\n \n \n \n \n \n Reinforcement Learning based Meta-path Discovery in Large-scale Heterogeneous Information Networks.\n \n \n \n\n\n \n Wan, G.; Du, B.; Pan, S.; and Haffari, G.\n\n\n \n\n\n\n In Proceedings of the Thirty-Fourth AAAI Conference on Artificial Intelligence, AAAI-20, New York, New York, USA, February 7-12, 2020, pages 6094–6101 (CORE Ranked A*), 2020. \n \n\n\n\n
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@inproceedings{wan_reinforcement_2020,\n\ttitle = {Reinforcement {Learning} based {Meta}-path {Discovery} in {Large}-scale {Heterogeneous} {Information} {Networks}},\n\tcopyright = {All rights reserved},\n\tbooktitle = {Proceedings of the {Thirty}-{Fourth} {AAAI} {Conference} on {Artificial} {Intelligence}, {AAAI}-20, {New} {York}, {New} {York}, {USA}, {February} 7-12, 2020},\n\tauthor = {Wan, Guojia and Du, Bo and Pan, Shirui and Haffari, Gholamreza},\n\tyear = {2020},\n\tpages = {6094--6101 (CORE Ranked A*)},\n}\n\n\n\n
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\n \n\n \n \n \n \n \n Reasoning Like Human: Hierarchical Reinforcement Learning for Knowledge Graph Reasoning.\n \n \n \n\n\n \n Wan, G.; Pan, S.; Gong, C.; Zhou, C.; and Haffari, G.\n\n\n \n\n\n\n In International Joint Conference on Artificial Intelligence, IJCAI-20, Yokohama, Japan, January, 2021, pages 1926–1932 (CORE Ranked A*), 2020. \n \n\n\n\n
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@inproceedings{wan_reasoning_2020,\n\ttitle = {Reasoning {Like} {Human}: {Hierarchical} {Reinforcement} {Learning} for {Knowledge} {Graph} {Reasoning}},\n\tcopyright = {All rights reserved},\n\tbooktitle = {International {Joint} {Conference} on {Artificial} {Intelligence}, {IJCAI}-20, {Yokohama}, {Japan}, {January}, 2021},\n\tauthor = {Wan, Guojia and Pan, Shirui and Gong, Chen and Zhou, Chuan and Haffari, Gholamreza},\n\tyear = {2020},\n\tpages = {1926--1932 (CORE Ranked A*)},\n}\n\n\n\n
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\n \n\n \n \n \n \n \n Cross-Graph: Robust and Unsupervised Embedding for Attributed Graphs with Corrupted Structure.\n \n \n \n\n\n \n Wang, C.; Han, B.; Pan, S.; Jiang, J.; Niu, G.; and Long, G.\n\n\n \n\n\n\n In IEEE International Conference on Data Mining, ICDM-20, November 17-20, 2020, Sorrento, Italy, pages 571–580 (CORE Ranked A*), 2020. \n \n\n\n\n
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@inproceedings{wang_cross-graph:_2020,\n\ttitle = {Cross-{Graph}: {Robust} and {Unsupervised} {Embedding} for {Attributed} {Graphs} with {Corrupted} {Structure}},\n\tcopyright = {All rights reserved},\n\tbooktitle = {{IEEE} {International} {Conference} on {Data} {Mining}, {ICDM}-20, {November} 17-20, 2020, {Sorrento}, {Italy}},\n\tauthor = {Wang, Chun and Han, Bo and Pan, Shirui and Jiang, Jing and Niu, Gang and Long, Guodong},\n\tyear = {2020},\n\tpages = {571--580 (CORE Ranked A*)},\n}\n\n\n\n
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\n \n\n \n \n \n \n \n Unsupervised Domain Adaptive Graph Convolutional Networks.\n \n \n \n\n\n \n Wu, M.; Pan, S.; Zhou, C.; Chang, X.; and Zhu, X.\n\n\n \n\n\n\n In The Web Conference (WWW), WWW-20, Taipei, Taiwan, April 20-24, 2020, pages 1457–1467, 2020. \n \n\n\n\n
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@inproceedings{wu_unsupervised_2020,\n\ttitle = {Unsupervised {Domain} {Adaptive} {Graph} {Convolutional} {Networks}},\n\tcopyright = {All rights reserved},\n\tdoi = {10.1145/3366423.3380219 (CORE Ranked A*)},\n\tbooktitle = {The {Web} {Conference} ({WWW}), {WWW}-20, {Taipei}, {Taiwan}, {April} 20-24, 2020},\n\tauthor = {Wu, Man and Pan, Shirui and Zhou, Chuan and Chang, Xiaojun and Zhu, Xingquan},\n\tyear = {2020},\n\tpages = {1457--1467},\n}\n\n\n\n
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\n \n\n \n \n \n \n \n Overcoming Multi-Model Forgetting in One-Shot NAS with Diversity Maximization.\n \n \n \n\n\n \n Zhang, M.; Li, H.; Pan, S.; Chang, X.; and Su, S.\n\n\n \n\n\n\n In IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR-20, pages 7809–7818 (CORE Ranked A*), 2020. \n \n\n\n\n
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@inproceedings{zhang_overcoming_2020,\n\ttitle = {Overcoming {Multi}-{Model} {Forgetting} in {One}-{Shot} {NAS} with {Diversity} {Maximization}},\n\tcopyright = {All rights reserved},\n\tbooktitle = {{IEEE}/{CVF} {Conference} on {Computer} {Vision} and {Pattern} {Recognition}, {CVPR}-20},\n\tauthor = {Zhang, Miao and Li, Huiqi and Pan, Shirui and Chang, Xiaojun and Su, Steven},\n\tyear = {2020},\n\tpages = {7809--7818 (CORE Ranked A*)},\n}\n\n\n\n
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\n \n\n \n \n \n \n \n Graph Geometry Interaction Learning.\n \n \n \n\n\n \n Zhu, S.; Pan, S.; Zhou, C.; Wu, J.; Cao, Y.; and Wang, B.\n\n\n \n\n\n\n In Thirty-fourth Conference on Neural Information Processing Systems, NeurIPS-20, December 6-12, 2020, Virtual Conference, pages 7548–7558 (CORE Ranked A*; Top Conference in Machine Learning), 2020. \n \n\n\n\n
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@inproceedings{zhu_graph_2020,\n\ttitle = {Graph {Geometry} {Interaction} {Learning}},\n\tcopyright = {All rights reserved},\n\tbooktitle = {Thirty-fourth {Conference} on {Neural} {Information} {Processing} {Systems}, {NeurIPS}-20, {December} 6-12, 2020, {Virtual} {Conference}},\n\tauthor = {Zhu, Shichao and Pan, Shirui and Zhou, Chuan and Wu, Jia and Cao, Yanan and Wang, Bin},\n\tyear = {2020},\n\tpages = {7548--7558 (CORE Ranked A*; Top Conference in Machine Learning)},\n}\n\n\n\n
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\n \n\n \n \n \n \n \n GSSNN: Graph Smoothing Splines Neural Networks.\n \n \n \n\n\n \n Zhu, S.; Zhou, L.; Pan, S.; Zhou, C.; Yan, G.; and Wang, B.\n\n\n \n\n\n\n In Proceedings of the Thirty-Fourth AAAI Conference on Artificial Intelligence, AAAI-20, New York, New York, USA, February 7-12, 2020, pages 7007–7014 (CORE Ranked A*), 2020. \n \n\n\n\n
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@inproceedings{zhu_gssnn:_2020,\n\ttitle = {{GSSNN}: {Graph} {Smoothing} {Splines} {Neural} {Networks}},\n\tcopyright = {All rights reserved},\n\tbooktitle = {Proceedings of the {Thirty}-{Fourth} {AAAI} {Conference} on {Artificial} {Intelligence}, {AAAI}-20, {New} {York}, {New} {York}, {USA}, {February} 7-12, 2020},\n\tauthor = {Zhu, Shichao and Zhou, Lewei and Pan, Shirui and Zhou, Chuan and Yan, Guiying and Wang, Bin},\n\tyear = {2020},\n\tpages = {7007--7014 (CORE Ranked A*)},\n}\n\n\n\n
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\n \n\n \n \n \n \n \n Distributed Feature Selection for Big Data using Fuzzy Rough Sets.\n \n \n \n\n\n \n Kong, L.; Qu, W.; Yu, J.; Zuo, H.; Chen, G.; Xiong, F.; Pan, S.; Lin, S.; and Qiu, M.\n\n\n \n\n\n\n IEEE Transactions on Fuzzy Systems (TFS), 28(5): 846–857. 2020.\n Publisher: IEEE\n\n\n\n
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@article{kong_distributed_2020,\n\ttitle = {Distributed {Feature} {Selection} for {Big} {Data} using {Fuzzy} {Rough} {Sets}},\n\tvolume = {28},\n\tcopyright = {All rights reserved},\n\tdoi = {doi.org/10.1109/TFUZZ.2019.2955894 (Impact Factor: 12.029; JCR Ranked Q1)},\n\tnumber = {5},\n\tjournal = {IEEE Transactions on Fuzzy Systems (TFS)},\n\tauthor = {Kong, Linghe and Qu, Wenhao and Yu, Jiadi and Zuo, Hua and Chen, Guihai and Xiong, Fei and Pan, Shirui and Lin, Siyu and Qiu, Meikang},\n\tyear = {2020},\n\tnote = {Publisher: IEEE},\n\tpages = {846--857},\n}\n\n\n\n
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\n \n\n \n \n \n \n \n OpenWGL: Open-World Graph Learning.\n \n \n \n\n\n \n Wu, M.; Pan, S.; and Zhu, X.\n\n\n \n\n\n\n In IEEE International Conference on Data Mining, ICDM-20, November 17-20, 2020, Sorrento, Italy, pages 681–690 (CORE Ranked A*; Best Student Paper Award), 2020. \n \n\n\n\n
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@inproceedings{wu_openwgl:_2020,\n\ttitle = {{OpenWGL}: {Open}-{World} {Graph} {Learning}},\n\tcopyright = {All rights reserved},\n\tbooktitle = {{IEEE} {International} {Conference} on {Data} {Mining}, {ICDM}-20, {November} 17-20, 2020, {Sorrento}, {Italy}},\n\tauthor = {Wu, Man and Pan, Shirui and Zhu, Xingquan},\n\tyear = {2020},\n\tpages = {681--690 (CORE Ranked A*; Best Student Paper Award)},\n}\n\n\n\n
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\n \n\n \n \n \n \n \n Connecting the Dots: Multivariate Time Series Forecasting with Graph Neural Networks.\n \n \n \n\n\n \n Wu, Z.; Pan, S.; Long, G.; Jiang, J.; Chang, X.; and Zhang, C.\n\n\n \n\n\n\n In ACM SIGKDD Conference on Knowledge Discovery and Data Mining, KDD-20, August 23–27, 2020, Virtual Event, CA, USA, pages 753–763 (CORE Ranked A*; Top Conference in Data Mining), 2020. ACM\n \n\n\n\n
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@inproceedings{wu_connecting_2020,\n\ttitle = {Connecting the {Dots}: {Multivariate} {Time} {Series} {Forecasting} with {Graph} {Neural} {Networks}},\n\tcopyright = {All rights reserved},\n\tbooktitle = {{ACM} {SIGKDD} {Conference} on {Knowledge} {Discovery} and {Data} {Mining}, {KDD}-20, {August} 23–27, 2020, {Virtual} {Event}, {CA}, {USA}},\n\tpublisher = {ACM},\n\tauthor = {Wu, Zonghan and Pan, Shirui and Long, Guodong and Jiang, Jing and Chang, Xiaojun and Zhang, Chengqi},\n\tyear = {2020},\n\tpages = {753--763 (CORE Ranked A*; Top Conference in Data Mining)},\n}\n\n\n\n
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\n \n\n \n \n \n \n \n A Relation-Specific Attention Network for Joint Entity and Relation Extraction.\n \n \n \n\n\n \n Yuan, Y.; Zhou, X.; Pan, S.; Zhu, Q.; Song, Z.; and Guo, L.\n\n\n \n\n\n\n In International Joint Conference on Artificial Intelligence, IJCAI-20, Yokohama, Japan, January, 2021, pages 4054–4060 (CORE Ranked A*), 2020. \n \n\n\n\n
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@inproceedings{yuan_relation-specific_2020,\n\ttitle = {A {Relation}-{Specific} {Attention} {Network} for {Joint} {Entity} and {Relation} {Extraction}},\n\tcopyright = {All rights reserved},\n\tbooktitle = {International {Joint} {Conference} on {Artificial} {Intelligence}, {IJCAI}-20, {Yokohama}, {Japan}, {January}, 2021},\n\tauthor = {Yuan, Yue and Zhou, Xiaofei and Pan, Shirui and Zhu, Qiannan and Song, Zeliang and Guo, Li},\n\tyear = {2020},\n\tpages = {4054--4060 (CORE Ranked A*)},\n}\n\n\n\n
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\n \n\n \n \n \n \n \n Differentiable Neural Architecture Search in Equivalent Space with Exploration Enhancement.\n \n \n \n\n\n \n Zhang, M.; Li, H.; Pan, S.; Chang, X.; Ge, Z.; and Su, S.\n\n\n \n\n\n\n In Thirty-fourth Conference on Neural Information Processing Systems, NeurIPS-20, December 6-12, 2020, Virtual Conference, pages 13341–13351 (CORE Ranked A*; Top Conference in Machine Learning), 2020. \n \n\n\n\n
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@inproceedings{zhang_differentiable_2020,\n\ttitle = {Differentiable {Neural} {Architecture} {Search} in {Equivalent} {Space} with {Exploration} {Enhancement}},\n\tcopyright = {All rights reserved},\n\tbooktitle = {Thirty-fourth {Conference} on {Neural} {Information} {Processing} {Systems}, {NeurIPS}-20, {December} 6-12, 2020, {Virtual} {Conference}},\n\tauthor = {Zhang, Miao and Li, Huiqi and Pan, Shirui and Chang, Xiaojun and Ge, Zongyuan and Su, Steven},\n\tyear = {2020},\n\tpages = {13341--13351 (CORE Ranked A*; Top Conference in Machine Learning)},\n}\n\n\n\n
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\n \n\n \n \n \n \n \n One-Shot Neural Architecture Search via Novelty Driven Sampling.\n \n \n \n\n\n \n Zhang, M.; Li, H.; Pan, S.; Liu, T.; and Su, S.\n\n\n \n\n\n\n In International Joint Conference on Artificial Intelligence, IJCAI-20, Yokohama, Japan, January, 2021, pages 3188–3194 (CORE Ranked A*), 2020. \n \n\n\n\n
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@inproceedings{zhang_one-shot_2020,\n\ttitle = {One-{Shot} {Neural} {Architecture} {Search} via {Novelty} {Driven} {Sampling}},\n\tcopyright = {All rights reserved},\n\tbooktitle = {International {Joint} {Conference} on {Artificial} {Intelligence}, {IJCAI}-20, {Yokohama}, {Japan}, {January}, 2021},\n\tauthor = {Zhang, Miao and Li, Huiqi and Pan, Shirui and Liu, Taoping and Su, Steven},\n\tyear = {2020},\n\tpages = {3188--3194 (CORE Ranked A*)},\n}\n\n\n\n
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\n \n\n \n \n \n \n \n Learning graph embedding with adversarial training methods.\n \n \n \n\n\n \n Pan, S.; Hu, R.; Fung, S.; Long, G.; Jiang, J.; and Zhang, C.\n\n\n \n\n\n\n IEEE transactions on cybernetics (TCYB), 50(6): 2475–2487. 2020.\n Publisher: IEEE\n\n\n\n
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@article{pan_learning_2020,\n\ttitle = {Learning graph embedding with adversarial training methods},\n\tvolume = {50},\n\tcopyright = {All rights reserved},\n\tdoi = {doi.org/10.1109/TCYB.2019.2932096  (Impact Factor: 11.448; JCR Ranked Q1)},\n\tnumber = {6},\n\tjournal = {IEEE transactions on cybernetics (TCYB)},\n\tauthor = {Pan, Shirui and Hu, Ruiqi and Fung, Sai-fu and Long, Guodong and Jiang, Jing and Zhang, Chengqi},\n\tyear = {2020},\n\tnote = {Publisher: IEEE},\n\tpages = {2475--2487},\n}\n\n\n\n
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\n \n\n \n \n \n \n \n Exploiting Implicit Influence From Information Propagation for Social Recommendation.\n \n \n \n\n\n \n Xiong, F.; Shen, W.; Chen, H.; Pan, S.; Wang, X.; and Yan, Z.\n\n\n \n\n\n\n IEEE transactions on cybernetics (TCYB), 50(10): 4186–4199. 2020.\n Publisher: IEEE\n\n\n\n
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@article{xiong_exploiting_2020,\n\ttitle = {Exploiting {Implicit} {Influence} {From} {Information} {Propagation} for {Social} {Recommendation}},\n\tvolume = {50},\n\tcopyright = {All rights reserved},\n\tdoi = {doi.org/10.1109/TCYB.2019.2939390  (Impact Factor: 11.448; JCR Ranked Q1)},\n\tnumber = {10},\n\tjournal = {IEEE transactions on cybernetics (TCYB)},\n\tauthor = {Xiong, Fei and Shen, Weihan and Chen, Hongshu and Pan, Shirui and Wang, Ximeng and Yan, Zheng},\n\tyear = {2020},\n\tnote = {Publisher: IEEE},\n\tpages = {4186--4199},\n}\n\n\n\n
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\n \n\n \n \n \n \n \n Social recommendation with evolutionary opinion dynamics.\n \n \n \n\n\n \n Xiong, F.; Wang, X.; Pan, S.; Yang, H.; Wang, H.; and Zhang, C.\n\n\n \n\n\n\n IEEE Transactions on Systems, Man, and Cybernetics: Systems (TSMC), 50(10): 3804–3816. 2020.\n \n\n\n\n
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@article{xiong_social_2020,\n\ttitle = {Social recommendation with evolutionary opinion dynamics},\n\tvolume = {50},\n\tcopyright = {All rights reserved},\n\tissn = {2168-2216},\n\tdoi = {10.1109/TSMC.2018.2854000 (Impact Factor: 13.451; JCR Ranked Q1)},\n\tlanguage = {English},\n\tnumber = {10},\n\tjournal = {IEEE Transactions on Systems, Man, and Cybernetics: Systems (TSMC)},\n\tauthor = {Xiong, Fei and Wang, Ximeng and Pan, Shirui and Yang, Hong and Wang, Haishuai and Zhang, Chengqi},\n\tyear = {2020},\n\tpages = {3804--3816},\n}\n\n\n\n
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\n \n\n \n \n \n \n \n Familial Clustering For Weakly-labeled Android Malware Using Hybrid Representation Learning.\n \n \n \n\n\n \n Zhang, Y.; Sui, Y.; Pan, S.; Zheng, Z.; Ning, B.; Tsang, I.; and Zhou, W.\n\n\n \n\n\n\n IEEE Transactions on Information Forensics and Security (TIFS), 15: 3401–3414. 2020.\n Publisher: IEEE\n\n\n\n
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@article{zhang_familial_2020,\n\ttitle = {Familial {Clustering} {For} {Weakly}-labeled {Android} {Malware} {Using} {Hybrid} {Representation} {Learning}},\n\tvolume = {15},\n\tcopyright = {All rights reserved},\n\tdoi = {doi.org/10.1109/TIFS.2019.2947861 (Impact Factor: 7.178; JCR Ranked Q1)},\n\tjournal = {IEEE Transactions on Information Forensics and Security (TIFS)},\n\tauthor = {Zhang, Yanxin and Sui, Yulei and Pan, Shirui and Zheng, Zheng and Ning, Baodi and Tsang, Ivor and Zhou, Wanlei},\n\tyear = {2020},\n\tnote = {Publisher: IEEE},\n\tpages = {3401--3414},\n}\n\n\n\n
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\n \n\n \n \n \n \n \n Adaptive knowledge subgraph ensemble for robust and trustworthy knowledge graph completion.\n \n \n \n\n\n \n Wan, G.; Du, B.; Pan, S.; and Wu, J.\n\n\n \n\n\n\n World Wide Web (WWW), 23(1): 471–490. 2020.\n Publisher: Springer\n\n\n\n
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@article{wan_adaptive_2020,\n\ttitle = {Adaptive knowledge subgraph ensemble for robust and trustworthy knowledge graph completion},\n\tvolume = {23},\n\tcopyright = {All rights reserved},\n\tdoi = {doi.org/10.1007/s11280-019-00711-y (Impact Factor: 2.716; JCR Ranked Q2)},\n\tnumber = {1},\n\tjournal = {World Wide Web (WWW)},\n\tauthor = {Wan, Guojia and Du, Bo and Pan, Shirui and Wu, Jia},\n\tyear = {2020},\n\tnote = {Publisher: Springer},\n\tpages = {471--490},\n}\n\n\n\n
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\n \n\n \n \n \n \n \n Clustering social audiences in business information networks.\n \n \n \n\n\n \n Zheng, Y.; Hu, R.; Fung, S.; Yu, C.; Long, G.; Guo, T.; and Pan, S.\n\n\n \n\n\n\n Pattern Recognition (PR), 100: 107126. 2020.\n \n\n\n\n
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@article{zheng_clustering_2020,\n\ttitle = {Clustering social audiences in business information networks},\n\tvolume = {100},\n\tcopyright = {All rights reserved},\n\tissn = {0031-3203},\n\tdoi = {doi.org/10.1016/j.patcog.2019.107126 (Impact Factor: 7.74; JCR Ranked Q1)},\n\tjournal = {Pattern Recognition (PR)},\n\tauthor = {Zheng, Yu and Hu, Ruiqi and Fung, Sai-fu and Yu, Celina and Long, Guodong and Guo, Ting and Pan, Shirui},\n\tyear = {2020},\n\tpages = {107126},\n}\n\n\n\n
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\n  \n 2019\n \n \n (23)\n \n \n
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\n \n\n \n \n \n \n \n An explainable deep fusion network for affect recognition using physiological signals.\n \n \n \n\n\n \n Lin, J.; Pan, S.; Lee, C. S.; and Oviatt, S.\n\n\n \n\n\n\n In Proceedings of the 28th ACM International Conference on Information and Knowledge Management, pages 2069–2072, 2019. \n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@inproceedings{lin_explainable_2019,\n\ttitle = {An explainable deep fusion network for affect recognition using physiological signals},\n\tcopyright = {All rights reserved},\n\tbooktitle = {Proceedings of the 28th {ACM} {International} {Conference} on {Information} and {Knowledge} {Management}},\n\tauthor = {Lin, Jionghao and Pan, Shirui and Lee, Cheng Siong and Oviatt, Sharon},\n\tyear = {2019},\n\tpages = {2069--2072},\n}\n\n\n\n
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\n \n\n \n \n \n \n \n Distributed feature selection for big data using fuzzy rough sets.\n \n \n \n\n\n \n Kong, L.; Qu, W.; Yu, J.; Zuo, H.; Chen, G.; Xiong, F.; Pan, S.; Lin, S.; and Qiu, M.\n\n\n \n\n\n\n IEEE Transactions on Fuzzy Systems, 28(5): 846–857. 2019.\n Publisher: IEEE\n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@article{kong_distributed_2019,\n\ttitle = {Distributed feature selection for big data using fuzzy rough sets},\n\tvolume = {28},\n\tcopyright = {All rights reserved},\n\tnumber = {5},\n\tjournal = {IEEE Transactions on Fuzzy Systems},\n\tauthor = {Kong, Linghe and Qu, Wenhao and Yu, Jiadi and Zuo, Hua and Chen, Guihai and Xiong, Fei and Pan, Shirui and Lin, Siyu and Qiu, Meikang},\n\tyear = {2019},\n\tnote = {Publisher: IEEE},\n\tpages = {846--857},\n}\n\n\n\n
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\n \n\n \n \n \n \n \n Suicidal ideation detection: A review of machine learning methods and applications.\n \n \n \n\n\n \n Ji, S.; Pan, S.; Li, X.; Cambria, E.; Long, G.; and Huang, Z.\n\n\n \n\n\n\n IEEE Transactions on Computational Social Systems. 2019.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@article{ji_suicidal_2019,\n\ttitle = {Suicidal ideation detection: {A} review of machine learning methods and applications},\n\tcopyright = {All rights reserved},\n\tjournal = {IEEE Transactions on Computational Social Systems},\n\tauthor = {Ji, Shaoxiong and Pan, Shirui and Li, Xue and Cambria, Erik and Long, Guodong and Huang, Zi},\n\tyear = {2019},\n}\n\n\n\n
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\n \n\n \n \n \n \n \n Familial Clustering For Weakly-labeled Android Malware Using Hybrid Representation Learning.\n \n \n \n\n\n \n Zhang, Y.; Sui, Y.; Pan, S.; Zheng, Z.; Ning, B.; Tsang, I.; and Zhou, W.\n\n\n \n\n\n\n IEEE Transactions on Information Forensics & Security (TIFS). 2019.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@article{zhang_familial_2019,\n\ttitle = {Familial {Clustering} {For} {Weakly}-labeled {Android} {Malware} {Using} {Hybrid} {Representation} {Learning}},\n\tcopyright = {All rights reserved},\n\tjournal = {IEEE Transactions on Information Forensics \\& Security (TIFS)},\n\tauthor = {Zhang, Yanxin and Sui, Yulei and Pan, Shirui and Zheng, Zheng and Ning, Baodi and Tsang, Ivor and Zhou, Wanlei},\n\tyear = {2019},\n}\n\n\n\n
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\n \n\n \n \n \n \n \n Relation Structure-Aware Heterogeneous Graph Neural Network.\n \n \n \n\n\n \n Zhu, S.; Zhou, C.; Pan, S.; Zhu, X.; and Wang, B.\n\n\n \n\n\n\n In IEEE International Conference on Data Mining, ICDM, 2019. \n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@inproceedings{zhu_relation_2019,\n\ttitle = {Relation {Structure}-{Aware} {Heterogeneous} {Graph} {Neural} {Network}},\n\tcopyright = {All rights reserved},\n\tbooktitle = {{IEEE} {International} {Conference} on {Data} {Mining}, {ICDM}},\n\tauthor = {Zhu, Shichao and Zhou, Chuan and Pan, Shirui and Zhu, Xingquan and Wang, Bin},\n\tyear = {2019},\n}\n\n\n\n
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\n \n\n \n \n \n \n \n Exploiting implicit influence from information propagation for social recommendation.\n \n \n \n\n\n \n Xiong, F.; Shen, W.; Chen, H.; Pan, S.; Wang, X.; and Yan, Z.\n\n\n \n\n\n\n IEEE Transactions on Cybernetics, 50(10): 4186–4199. 2019.\n Publisher: IEEE\n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@article{xiong_exploiting_2019,\n\ttitle = {Exploiting implicit influence from information propagation for social recommendation},\n\tvolume = {50},\n\tcopyright = {All rights reserved},\n\tnumber = {10},\n\tjournal = {IEEE Transactions on Cybernetics},\n\tauthor = {Xiong, Fei and Shen, Weihan and Chen, Hongshu and Pan, Shirui and Wang, Ximeng and Yan, Zheng},\n\tyear = {2019},\n\tnote = {Publisher: IEEE},\n\tpages = {4186--4199},\n}\n\n\n\n
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\n \n\n \n \n \n \n \n Graph WaveNet for Deep Spatial-Temporal Graph Modeling.\n \n \n \n\n\n \n Wu, Z.; Pan, S.; Long, G.; Jiang, J.; and Zhang, C.\n\n\n \n\n\n\n In International Joint Conference on Artificial Intelligence, IJCAI-19, 2019. \n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@inproceedings{wu_graph_2019,\n\ttitle = {Graph {WaveNet} for {Deep} {Spatial}-{Temporal} {Graph} {Modeling}},\n\tcopyright = {All rights reserved},\n\tbooktitle = {International {Joint} {Conference} on {Artificial} {Intelligence}, {IJCAI}-19},\n\tauthor = {Wu, Zonghan and Pan, Shirui and Long, Guodong and Jiang, Jing and Zhang, Chengqi},\n\tyear = {2019},\n}\n\n\n\n
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\n \n\n \n \n \n \n \n Influence spread in geo-social networks: a multiobjective optimization perspective.\n \n \n \n\n\n \n Wang, L.; Yu, Z.; Xiong, F.; Yang, D.; Pan, S.; and Yan, Z.\n\n\n \n\n\n\n IEEE Transactions on Cybernetics, 51(5): 2663–2675. 2019.\n Publisher: IEEE\n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@article{wang_influence_2019,\n\ttitle = {Influence spread in geo-social networks: a multiobjective optimization perspective},\n\tvolume = {51},\n\tcopyright = {All rights reserved},\n\tnumber = {5},\n\tjournal = {IEEE Transactions on Cybernetics},\n\tauthor = {Wang, Liang and Yu, Zhiwen and Xiong, Fei and Yang, Dingqi and Pan, Shirui and Yan, Zheng},\n\tyear = {2019},\n\tnote = {Publisher: IEEE},\n\tpages = {2663--2675},\n}\n\n\n\n
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\n \n\n \n \n \n \n \n Domain-Adversarial Graph Neural Networks for Text Classification.\n \n \n \n\n\n \n Wu, M.; Pan, S.; Zhu, X.; Zhou, C.; and Pan, L.\n\n\n \n\n\n\n In IEEE International Conference on Data Mining, ICDM, 2019. \n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@inproceedings{wu_domain-adversarial_2019,\n\ttitle = {Domain-{Adversarial} {Graph} {Neural} {Networks} for {Text} {Classification}},\n\tcopyright = {All rights reserved},\n\tbooktitle = {{IEEE} {International} {Conference} on {Data} {Mining}, {ICDM}},\n\tauthor = {Wu, Man and Pan, Shirui and Zhu, Xingquan and Zhou, Chuan and Pan, Lei},\n\tyear = {2019},\n}\n\n\n\n
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\n \n\n \n \n \n \n \n Long-short Distance Aggregation Networks for Positive Unlabeled Graph Learning.\n \n \n \n\n\n \n Wu, M.; Pan, S.; Du, L.; Tsang, I. W; Zhu, X.; and Du, B.\n\n\n \n\n\n\n In CIKM-2019, 2019. \n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@inproceedings{wu_long-short_2019,\n\ttitle = {Long-short {Distance} {Aggregation} {Networks} for {Positive} {Unlabeled} {Graph} {Learning}},\n\tcopyright = {All rights reserved},\n\tbooktitle = {{CIKM}-2019},\n\tauthor = {Wu, Man and Pan, Shirui and Du, Lan and Tsang, Ivor W and Zhu, Xingquan and Du, Bo},\n\tyear = {2019},\n}\n\n\n\n
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\n \n\n \n \n \n \n \n Attributed Graph Clustering: A Deep Attentional Embedding Approach.\n \n \n \n\n\n \n Wang, C.; Pan, S.; Hu, R.; Long, G.; Jiang, J.; and Zhang, C.\n\n\n \n\n\n\n In International Joint Conference on Artificial Intelligence, IJCAI-19, 2019. \n \n\n\n\n
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@inproceedings{wang_attributed_2019,\n\ttitle = {Attributed {Graph} {Clustering}: {A} {Deep} {Attentional} {Embedding} {Approach}},\n\tcopyright = {All rights reserved},\n\tbooktitle = {International {Joint} {Conference} on {Artificial} {Intelligence}, {IJCAI}-19},\n\tauthor = {Wang, Chun and Pan, Shirui and Hu, Ruiqi and Long, Guodong and Jiang, Jing and Zhang, Chengqi},\n\tyear = {2019},\n}\n\n\n\n
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\n \n\n \n \n \n \n \n Low-Bit Quantization for Attributed Network Representation Learning.\n \n \n \n\n\n \n Yang, H.; Pan, S.; Chen, L.; Zhou, C.; and Zhang, P.\n\n\n \n\n\n\n In International Joint Conference on Artificial Intelligence, IJCAI, 2019. \n \n\n\n\n
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@inproceedings{yang_low-bit_2019,\n\ttitle = {Low-{Bit} {Quantization} for {Attributed} {Network} {Representation} {Learning}},\n\tcopyright = {All rights reserved},\n\tbooktitle = {International {Joint} {Conference} on {Artificial} {Intelligence}, {IJCAI}},\n\tauthor = {Yang, Hong and Pan, Shirui and Chen, Ling and Zhou, Chuan and Zhang, Peng},\n\tyear = {2019},\n}\n\n\n\n
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\n \n\n \n \n \n \n \n Identify topic relations in scientific literature using topic modeling.\n \n \n \n\n\n \n Chen, H.; Wang, X.; Pan, S.; and Xiong, F.\n\n\n \n\n\n\n IEEE Transactions on Engineering Management, 68(5): 1232–1244. 2019.\n Publisher: IEEE\n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@article{chen_identify_2019,\n\ttitle = {Identify topic relations in scientific literature using topic modeling},\n\tvolume = {68},\n\tcopyright = {All rights reserved},\n\tnumber = {5},\n\tjournal = {IEEE Transactions on Engineering Management},\n\tauthor = {Chen, Hongshu and Wang, Ximeng and Pan, Shirui and Xiong, Fei},\n\tyear = {2019},\n\tnote = {Publisher: IEEE},\n\tpages = {1232--1244},\n}\n\n\n\n
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\n \n\n \n \n \n \n \n Label Embedding with Partial Heterogeneous Contexts.\n \n \n \n\n\n \n Shi, Y.; Xu, D.; Pan, Y.; Tsang, I. W; and Pan, S.\n\n\n \n\n\n\n In AAAI Conference on Artificial Intelligence, AAAI-19, 2019. \n \n\n\n\n
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@inproceedings{shi_label_2019,\n\ttitle = {Label {Embedding} with {Partial} {Heterogeneous} {Contexts}},\n\tcopyright = {All rights reserved},\n\tbooktitle = {{AAAI} {Conference} on {Artificial} {Intelligence}, {AAAI}-19},\n\tauthor = {Shi, Yaxin and Xu, Donna and Pan, Yuangang and Tsang, Ivor W and Pan, Shirui},\n\tyear = {2019},\n}\n\n\n\n
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\n \n\n \n \n \n \n \n \n Cost-Sensitive Parallel Learning Framework for Insurance Intelligence Operation.\n \n \n \n \n\n\n \n Jiang, X.; Pan, S.; Long, G.; Xiong, F.; Jiang, J.; and Zhang, C.\n\n\n \n\n\n\n IEEE Transactions Industrial Electronics (TIE), 66(12): 9713–9723. 2019.\n \n\n\n\n
\n\n\n\n \n \n \"Cost-SensitivePaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@article{jiang_cost-sensitive_2019,\n\ttitle = {Cost-{Sensitive} {Parallel} {Learning} {Framework} for {Insurance} {Intelligence} {Operation}},\n\tvolume = {66},\n\tcopyright = {All rights reserved},\n\turl = {https://doi.org/10.1109/TIE.2018.2873526},\n\tdoi = {10.1109/TIE.2018.2873526 (Impact Factor: 8.236; JCR Ranked Q1)},\n\tnumber = {12},\n\tjournal = {IEEE Transactions Industrial Electronics (TIE)},\n\tauthor = {Jiang, Xinxin and Pan, Shirui and Long, Guodong and Xiong, Fei and Jiang, Jing and Zhang, Chengqi},\n\tyear = {2019},\n\tpages = {9713--9723},\n}\n\n\n\n
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\n \n\n \n \n \n \n \n \n Attributed Graph Clustering: A Deep Attentional Embedding Approach.\n \n \n \n \n\n\n \n Wang, C.; Pan, S.; Hu, R.; Long, G.; Jiang, J.; and Zhang, C.\n\n\n \n\n\n\n In Kraus, S., editor(s), Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence, IJCAI 2019, Macao, China, August 10-16, 2019, pages 3670–3676, 2019. ijcai.org\n \n\n\n\n
\n\n\n\n \n \n \"AttributedPaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@inproceedings{wang_attributed_2019,\n\ttitle = {Attributed {Graph} {Clustering}: {A} {Deep} {Attentional} {Embedding} {Approach}},\n\tcopyright = {All rights reserved},\n\turl = {https://doi.org/10.24963/ijcai.2019/509},\n\tdoi = {10.24963/ijcai.2019/509 (CORE Ranked A*)},\n\tbooktitle = {Proceedings of the {Twenty}-{Eighth} {International} {Joint} {Conference} on {Artificial} {Intelligence}, {IJCAI} 2019, {Macao}, {China}, {August} 10-16, 2019},\n\tpublisher = {ijcai.org},\n\tauthor = {Wang, Chun and Pan, Shirui and Hu, Ruiqi and Long, Guodong and Jiang, Jing and Zhang, Chengqi},\n\teditor = {Kraus, Sarit},\n\tyear = {2019},\n\tpages = {3670--3676},\n}\n\n\n\n
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\n \n\n \n \n \n \n \n \n Label Embedding with Partial Heterogeneous Contexts.\n \n \n \n \n\n\n \n Shi, Y.; Xu, D.; Pan, Y.; Tsang, I. W.; and Pan, S.\n\n\n \n\n\n\n In The Thirty-Third AAAI Conference on Artificial Intelligence, AAAI 2019, Honolulu, Hawaii, USA, January 27 - February 1, 2019, pages 4926–4933, 2019. AAAI Press\n \n\n\n\n
\n\n\n\n \n \n \"LabelPaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@inproceedings{shi_label_2019,\n\ttitle = {Label {Embedding} with {Partial} {Heterogeneous} {Contexts}},\n\tcopyright = {All rights reserved},\n\turl = {https://doi.org/10.1609/aaai.v33i01.33014926},\n\tdoi = {10.1609/aaai.v33i01.33014926 (CORE Ranked A*)},\n\tbooktitle = {The {Thirty}-{Third} {AAAI} {Conference} on {Artificial} {Intelligence}, {AAAI} 2019, {Honolulu}, {Hawaii}, {USA}, {January} 27 - {February} 1, 2019},\n\tpublisher = {AAAI Press},\n\tauthor = {Shi, Yaxin and Xu, Donna and Pan, Yuangang and Tsang, Ivor W. and Pan, Shirui},\n\tyear = {2019},\n\tpages = {4926--4933},\n}\n\n\n\n
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\n \n\n \n \n \n \n \n Domain-Adversarial Graph Neural Networks for Text Classification.\n \n \n \n\n\n \n Wu, M.; Pan, S.; Zhu, X.; Zhou, C.; and Pan, L.\n\n\n \n\n\n\n In IEEE International Conference on Data Mining (ICDM), pages 648–657, 2019. \n \n\n\n\n
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@inproceedings{wu_domain-adversarial_2019,\n\ttitle = {Domain-{Adversarial} {Graph} {Neural} {Networks} for {Text} {Classification}},\n\tcopyright = {All rights reserved},\n\tdoi = {10.1109/ICDM.2019.00075 (CORE Ranked A*)},\n\tbooktitle = {{IEEE} {International} {Conference} on {Data} {Mining} ({ICDM})},\n\tauthor = {Wu, Man and Pan, Shirui and Zhu, Xingquan and Zhou, Chuan and Pan, Lei},\n\tyear = {2019},\n\tpages = {648--657},\n}\n\n\n\n
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\n \n\n \n \n \n \n \n \n Graph WaveNet for Deep Spatial-Temporal Graph Modeling.\n \n \n \n \n\n\n \n Wu, Z.; Pan, S.; Long, G.; Jiang, J.; and Zhang, C.\n\n\n \n\n\n\n In Kraus, S., editor(s), Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence, IJCAI 2019, Macao, China, August 10-16, 2019, pages 1907–1913, 2019. ijcai.org\n \n\n\n\n
\n\n\n\n \n \n \"GraphPaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@inproceedings{wu_graph_2019,\n\ttitle = {Graph {WaveNet} for {Deep} {Spatial}-{Temporal} {Graph} {Modeling}},\n\tcopyright = {All rights reserved},\n\turl = {https://doi.org/10.24963/ijcai.2019/264},\n\tdoi = {10.24963/ijcai.2019/264 (CORE Ranked A*)},\n\tbooktitle = {Proceedings of the {Twenty}-{Eighth} {International} {Joint} {Conference} on {Artificial} {Intelligence}, {IJCAI} 2019, {Macao}, {China}, {August} 10-16, 2019},\n\tpublisher = {ijcai.org},\n\tauthor = {Wu, Zonghan and Pan, Shirui and Long, Guodong and Jiang, Jing and Zhang, Chengqi},\n\teditor = {Kraus, Sarit},\n\tyear = {2019},\n\tpages = {1907--1913},\n}\n\n\n\n
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\n \n\n \n \n \n \n \n \n Low-Bit Quantization for Attributed Network Representation Learning.\n \n \n \n \n\n\n \n Yang, H.; Pan, S.; Chen, L.; Zhou, C.; and Zhang, P.\n\n\n \n\n\n\n In Kraus, S., editor(s), Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence, IJCAI 2019, Macao, China, August 10-16, 2019, pages 4047–4053, 2019. ijcai.org\n \n\n\n\n
\n\n\n\n \n \n \"Low-BitPaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@inproceedings{yang_low-bit_2019,\n\ttitle = {Low-{Bit} {Quantization} for {Attributed} {Network} {Representation} {Learning}},\n\tcopyright = {All rights reserved},\n\turl = {https://doi.org/10.24963/ijcai.2019/562},\n\tdoi = {10.24963/ijcai.2019/562 (CORE Ranked A*)},\n\tbooktitle = {Proceedings of the {Twenty}-{Eighth} {International} {Joint} {Conference} on {Artificial} {Intelligence}, {IJCAI} 2019, {Macao}, {China}, {August} 10-16, 2019},\n\tpublisher = {ijcai.org},\n\tauthor = {Yang, Hong and Pan, Shirui and Chen, Ling and Zhou, Chuan and Zhang, Peng},\n\teditor = {Kraus, Sarit},\n\tyear = {2019},\n\tpages = {4047--4053},\n}\n\n\n\n
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\n \n\n \n \n \n \n \n Relation Structure-Aware Heterogeneous Graph Neural Network.\n \n \n \n\n\n \n Zhu, S.; Zhou, C.; Pan, S.; Zhu, X.; and Wang, B.\n\n\n \n\n\n\n In IEEE International Conference on Data Mining (ICDM), pages 1534–1539, 2019. \n \n\n\n\n
\n\n\n\n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@inproceedings{zhu_relation_2019,\n\ttitle = {Relation {Structure}-{Aware} {Heterogeneous} {Graph} {Neural} {Network}},\n\tcopyright = {All rights reserved},\n\tdoi = {10.1109/ICDM.2019.00203 (CORE Ranked A*)},\n\tbooktitle = {{IEEE} {International} {Conference} on {Data} {Mining} ({ICDM})},\n\tauthor = {Zhu, Shichao and Zhou, Chuan and Pan, Shirui and Zhu, Xingquan and Wang, Bin},\n\tyear = {2019},\n\tpages = {1534--1539},\n}\n\n\n\n
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\n \n\n \n \n \n \n \n \n CFOND: Consensus Factorization for Co-Clustering Networked Data.\n \n \n \n \n\n\n \n Guo, T.; Pan, S.; Zhu, X.; and Zhang, C.\n\n\n \n\n\n\n IEEE Transactions on Knowledge and Data Engineering (TKDE), 31(4): 706–719. 2019.\n \n\n\n\n
\n\n\n\n \n \n \"CFOND:Paper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@article{guo_cfond:_2019,\n\ttitle = {{CFOND}: {Consensus} {Factorization} for {Co}-{Clustering} {Networked} {Data}},\n\tvolume = {31},\n\tcopyright = {All rights reserved},\n\turl = {https://doi.org/10.1109/TKDE.2018.2846555},\n\tdoi = {10.1109/TKDE.2018.2846555 (Impact Factor: 6.977; JCR Ranked Q1; Top Journal in Data Mining)},\n\tnumber = {4},\n\tjournal = {IEEE Transactions on Knowledge and Data Engineering (TKDE)},\n\tauthor = {Guo, Ting and Pan, Shirui and Zhu, Xingquan and Zhang, Chengqi},\n\tyear = {2019},\n\tpages = {706--719},\n}\n\n\n\n
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\n \n\n \n \n \n \n \n \n Time series feature learning with labeled and unlabeled data.\n \n \n \n \n\n\n \n Wang, H.; Zhang, Q.; Wu, J.; Pan, S.; and Chen, Y.\n\n\n \n\n\n\n Pattern Recognition (PR), 89: 55–66. 2019.\n \n\n\n\n
\n\n\n\n \n \n \"TimePaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n  \n \n 1 download\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@article{wang_time_2019,\n\ttitle = {Time series feature learning with labeled and unlabeled data},\n\tvolume = {89},\n\tcopyright = {All rights reserved},\n\turl = {https://doi.org/10.1016/j.patcog.2018.12.026},\n\tdoi = {10.1016/j.patcog.2018.12.026 (Impact Factor: 7.74; JCR Ranked Q1)},\n\tjournal = {Pattern Recognition (PR)},\n\tauthor = {Wang, Haishuai and Zhang, Qin and Wu, Jia and Pan, Shirui and Chen, Yixin},\n\tyear = {2019},\n\tpages = {55--66},\n}\n\n\n\n
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\n  \n 2018\n \n \n (20)\n \n \n
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\n \n\n \n \n \n \n \n Binarized Attributed Network Embedding.\n \n \n \n\n\n \n Yang, H.; Pan, S.; Zhang, P.; Chen, L.; Lian, D.; and Zhang, C.\n\n\n \n\n\n\n In IEEE International Conference on Data Mining, ICDM, 2018. \n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@inproceedings{yang_binarized_2018,\n\ttitle = {Binarized {Attributed} {Network} {Embedding}},\n\tcopyright = {All rights reserved},\n\tbooktitle = {{IEEE} {International} {Conference} on {Data} {Mining}, {ICDM}},\n\tauthor = {Yang, Hong and Pan, Shirui and Zhang, Peng and Chen, Ling and Lian, Defu and Zhang, Chengqi},\n\tyear = {2018},\n}\n\n\n\n
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\n \n\n \n \n \n \n \n Cost-sensitive parallel learning framework for insurance intelligence operation.\n \n \n \n\n\n \n Jiang, X.; Pan, S.; Long, G.; Xiong, F.; Jiang, J.; and Zhang, C.\n\n\n \n\n\n\n IEEE Transactions on Industrial Electronics, 66(12): 9713–9723. 2018.\n Publisher: IEEE\n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@article{jiang_cost-sensitive_2018,\n\ttitle = {Cost-sensitive parallel learning framework for insurance intelligence operation},\n\tvolume = {66},\n\tcopyright = {All rights reserved},\n\tnumber = {12},\n\tjournal = {IEEE Transactions on Industrial Electronics},\n\tauthor = {Jiang, Xinxin and Pan, Shirui and Long, Guodong and Xiong, Fei and Jiang, Jing and Zhang, Chengqi},\n\tyear = {2018},\n\tnote = {Publisher: IEEE},\n\tpages = {9713--9723},\n}\n\n\n\n
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\n \n\n \n \n \n \n \n Social recommendation with evolutionary opinion dynamics.\n \n \n \n\n\n \n Xiong, F.; Wang, X.; Pan, S.; Yang, H.; Wang, H.; and Zhang, C.\n\n\n \n\n\n\n IEEE Transactions on Systems, Man, and Cybernetics: Systems, 50(10): 3804–3816. 2018.\n Publisher: IEEE\n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@article{xiong_social_2018,\n\ttitle = {Social recommendation with evolutionary opinion dynamics},\n\tvolume = {50},\n\tcopyright = {All rights reserved},\n\tnumber = {10},\n\tjournal = {IEEE Transactions on Systems, Man, and Cybernetics: Systems},\n\tauthor = {Xiong, Fei and Wang, Ximeng and Pan, Shirui and Yang, Hong and Wang, Haishuai and Zhang, Chengqi},\n\tyear = {2018},\n\tnote = {Publisher: IEEE},\n\tpages = {3804--3816},\n}\n\n\n\n
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\n \n\n \n \n \n \n \n Adversarially Regularized Graph Autoencoder for Graph Embedding.\n \n \n \n\n\n \n Pan, S.; Hu, R.; Long, G.; Jiang, J.; Yao, L.; and Zhang, C.\n\n\n \n\n\n\n In International Joint Conference on Artificial Intelligence, IJCAI-18, pages 2609–2615, 2018. \n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@inproceedings{pan_adversarially_2018,\n\ttitle = {Adversarially {Regularized} {Graph} {Autoencoder} for {Graph} {Embedding}},\n\tcopyright = {All rights reserved},\n\tbooktitle = {International {Joint} {Conference} on {Artificial} {Intelligence}, {IJCAI}-18},\n\tauthor = {Pan, Shirui and Hu, Ruiqi and Long, Guodong and Jiang, Jing and Yao, Lina and Zhang, Chengqi},\n\tyear = {2018},\n\tpages = {2609--2615},\n}\n\n\n\n
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\n \n\n \n \n \n \n \n Active discriminative network representation learning.\n \n \n \n\n\n \n Gao, L.; Yang, H.; Zhou, C.; Wu, J.; Pan, S.; and Hu, Y.\n\n\n \n\n\n\n In International Joint Conference on Artificial Intelligence, IJCAI-18, pages 2142–2148, 2018. AAAI Press\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@inproceedings{gao_active_2018,\n\ttitle = {Active discriminative network representation learning},\n\tcopyright = {All rights reserved},\n\tbooktitle = {International {Joint} {Conference} on {Artificial} {Intelligence}, {IJCAI}-18},\n\tpublisher = {AAAI Press},\n\tauthor = {Gao, Li and Yang, Hong and Zhou, Chuan and Wu, Jia and Pan, Shirui and Hu, Yue},\n\tyear = {2018},\n\tpages = {2142--2148},\n}\n\n\n\n
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\n \n\n \n \n \n \n \n Discrete network embedding.\n \n \n \n\n\n \n Shen, X.; Pan, S.; Liu, W.; Ong, Y.; and Sun, Q.\n\n\n \n\n\n\n In International Joint Conference on Artificial Intelligence, IJCAI-18, pages 3549–3555, 2018. \n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@inproceedings{shen_discrete_2018,\n\ttitle = {Discrete network embedding},\n\tcopyright = {All rights reserved},\n\tbooktitle = {International {Joint} {Conference} on {Artificial} {Intelligence}, {IJCAI}-18},\n\tauthor = {Shen, Xiaobo and Pan, Shirui and Liu, Weiwei and Ong, Yew-Soon and Sun, Quan-Sen},\n\tyear = {2018},\n\tpages = {3549--3555},\n}\n\n\n\n
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\n \n\n \n \n \n \n \n CFOND: Consensus factorization for co-clustering networked data.\n \n \n \n\n\n \n Guo, T.; Pan, S.; Zhu, X.; and Zhang, C.\n\n\n \n\n\n\n IEEE Transactions on Knowledge and Data Engineering, 31(4): 706–719. 2018.\n Publisher: IEEE\n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@article{guo_cfond:_2018,\n\ttitle = {{CFOND}: {Consensus} factorization for co-clustering networked data},\n\tvolume = {31},\n\tcopyright = {All rights reserved},\n\tnumber = {4},\n\tjournal = {IEEE Transactions on Knowledge and Data Engineering},\n\tauthor = {Guo, Ting and Pan, Shirui and Zhu, Xingquan and Zhang, Chengqi},\n\tyear = {2018},\n\tnote = {Publisher: IEEE},\n\tpages = {706--719},\n}\n\n\n\n
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\n \n\n \n \n \n \n \n A three-layered mutually reinforced model for personalized citation recommendation.\n \n \n \n\n\n \n Cai, X.; Han, J.; Li, W.; Zhang, R.; Pan, S.; and Yang, L.\n\n\n \n\n\n\n IEEE transactions on neural networks and learning systems, 29(12): 6026–6037. 2018.\n Publisher: IEEE\n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@article{cai_three-layered_2018,\n\ttitle = {A three-layered mutually reinforced model for personalized citation recommendation},\n\tvolume = {29},\n\tcopyright = {All rights reserved},\n\tnumber = {12},\n\tjournal = {IEEE transactions on neural networks and learning systems},\n\tauthor = {Cai, Xiaoyan and Han, Junwei and Li, Wenjie and Zhang, Renxian and Pan, Shirui and Yang, Libin},\n\tyear = {2018},\n\tnote = {Publisher: IEEE},\n\tpages = {6026--6037},\n}\n\n\n\n
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\n \n\n \n \n \n \n \n Multi-instance learning with discriminative bag mapping.\n \n \n \n\n\n \n Wu, J.; Pan, S.; Zhu, X.; Zhang, C.; and Wu, X.\n\n\n \n\n\n\n IEEE Transactions on Knowledge and Data Engineering, 30(6): 1065–1080. 2018.\n Publisher: IEEE\n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@article{wu_multi-instance_2018,\n\ttitle = {Multi-instance learning with discriminative bag mapping},\n\tvolume = {30},\n\tcopyright = {All rights reserved},\n\tnumber = {6},\n\tjournal = {IEEE Transactions on Knowledge and Data Engineering},\n\tauthor = {Wu, Jia and Pan, Shirui and Zhu, Xingquan and Zhang, Chengqi and Wu, Xindong},\n\tyear = {2018},\n\tnote = {Publisher: IEEE},\n\tpages = {1065--1080},\n}\n\n\n\n
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\n \n\n \n \n \n \n \n Hashing for Adaptive Real-Time Graph Stream Classification With Concept Drifts.\n \n \n \n\n\n \n Chi, L.; Li, B.; Zhu, X.; Pan, S.; and Chen, L.\n\n\n \n\n\n\n IEEE transactions on cybernetics, 48(5): 1591–1604. 2018.\n Publisher: IEEE\n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@article{chi_hashing_2018,\n\ttitle = {Hashing for {Adaptive} {Real}-{Time} {Graph} {Stream} {Classification} {With} {Concept} {Drifts}},\n\tvolume = {48},\n\tcopyright = {All rights reserved},\n\tnumber = {5},\n\tjournal = {IEEE transactions on cybernetics},\n\tauthor = {Chi, Lianhua and Li, Bin and Zhu, Xingquan and Pan, Shirui and Chen, Ling},\n\tyear = {2018},\n\tnote = {Publisher: IEEE},\n\tpages = {1591--1604},\n}\n\n\n\n
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\n \n\n \n \n \n \n \n DiSAN: Directional self-attention network for rnn/cnn-free language understanding.\n \n \n \n\n\n \n Shen, T.; Zhou, T.; Long, G.; Jiang, J.; Pan, S.; and Zhang, C.\n\n\n \n\n\n\n In AAAI Conference on Artificial Intelligence, AAAI-18, 2018. \n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@inproceedings{shen_disan:_2018,\n\ttitle = {{DiSAN}: {Directional} self-attention network for rnn/cnn-free language understanding},\n\tcopyright = {All rights reserved},\n\tbooktitle = {{AAAI} {Conference} on {Artificial} {Intelligence}, {AAAI}-18},\n\tauthor = {Shen, Tao and Zhou, Tianyi and Long, Guodong and Jiang, Jing and Pan, Shirui and Zhang, Chengqi},\n\tyear = {2018},\n}\n\n\n\n
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\n \n\n \n \n \n \n \n \n Adversarially Regularized Graph Autoencoder for Graph Embedding.\n \n \n \n \n\n\n \n Pan, S.; Hu, R.; Long, G.; Jiang, J.; Yao, L.; and Zhang, C.\n\n\n \n\n\n\n In Lang, J., editor(s), Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence, IJCAI 2018, July 13-19, 2018, Stockholm, Sweden, pages 2609–2615, 2018. ijcai.org\n \n\n\n\n
\n\n\n\n \n \n \"AdversariallyPaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@inproceedings{pan_adversarially_2018,\n\ttitle = {Adversarially {Regularized} {Graph} {Autoencoder} for {Graph} {Embedding}},\n\tcopyright = {All rights reserved},\n\turl = {https://doi.org/10.24963/ijcai.2018/362},\n\tdoi = {10.24963/ijcai.2018/362 (CORE Ranked A*)},\n\tbooktitle = {Proceedings of the {Twenty}-{Seventh} {International} {Joint} {Conference} on {Artificial} {Intelligence}, {IJCAI} 2018, {July} 13-19, 2018, {Stockholm}, {Sweden}},\n\tpublisher = {ijcai.org},\n\tauthor = {Pan, Shirui and Hu, Ruiqi and Long, Guodong and Jiang, Jing and Yao, Lina and Zhang, Chengqi},\n\teditor = {Lang, Jérôme},\n\tyear = {2018},\n\tpages = {2609--2615},\n}\n\n\n\n
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\n \n\n \n \n \n \n \n \n Discrete Network Embedding.\n \n \n \n \n\n\n \n Shen, X.; Pan, S.; Liu, W.; Ong, Y.; and Sun, Q.\n\n\n \n\n\n\n In Lang, J., editor(s), Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence, IJCAI 2018, July 13-19, 2018, Stockholm, Sweden, pages 3549–3555, 2018. ijcai.org\n \n\n\n\n
\n\n\n\n \n \n \"DiscretePaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@inproceedings{shen_discrete_2018,\n\ttitle = {Discrete {Network} {Embedding}},\n\tcopyright = {All rights reserved},\n\turl = {https://doi.org/10.24963/ijcai.2018/493},\n\tdoi = {10.24963/ijcai.2018/493 (CORE Ranked A*)},\n\tbooktitle = {Proceedings of the {Twenty}-{Seventh} {International} {Joint} {Conference} on {Artificial} {Intelligence}, {IJCAI} 2018, {July} 13-19, 2018, {Stockholm}, {Sweden}},\n\tpublisher = {ijcai.org},\n\tauthor = {Shen, Xiaobo and Pan, Shirui and Liu, Weiwei and Ong, Yew-Soon and Sun, Quan-Sen},\n\teditor = {Lang, Jérôme},\n\tyear = {2018},\n\tpages = {3549--3555},\n}\n\n\n\n
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\n \n\n \n \n \n \n \n \n Active Discriminative Network Representation Learning.\n \n \n \n \n\n\n \n Gao, L.; Yang, H.; Zhou, C.; Wu, J.; Pan, S.; and Hu, Y.\n\n\n \n\n\n\n In Lang, J., editor(s), Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence, IJCAI 2018, July 13-19, 2018, Stockholm, Sweden, pages 2142–2148, 2018. ijcai.org\n \n\n\n\n
\n\n\n\n \n \n \"ActivePaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@inproceedings{gao_active_2018,\n\ttitle = {Active {Discriminative} {Network} {Representation} {Learning}},\n\tcopyright = {All rights reserved},\n\turl = {https://doi.org/10.24963/ijcai.2018/296},\n\tdoi = {10.24963/ijcai.2018/296 (CORE Ranked A*)},\n\tbooktitle = {Proceedings of the {Twenty}-{Seventh} {International} {Joint} {Conference} on {Artificial} {Intelligence}, {IJCAI} 2018, {July} 13-19, 2018, {Stockholm}, {Sweden}},\n\tpublisher = {ijcai.org},\n\tauthor = {Gao, Li and Yang, Hong and Zhou, Chuan and Wu, Jia and Pan, Shirui and Hu, Yue},\n\teditor = {Lang, Jérôme},\n\tyear = {2018},\n\tpages = {2142--2148},\n}\n\n\n\n
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\n \n\n \n \n \n \n \n \n DiSAN: Directional Self-Attention Network for RNN/CNN-Free Language Understanding.\n \n \n \n \n\n\n \n Shen, T.; Zhou, T.; Long, G.; Jiang, J.; Pan, S.; and Zhang, C.\n\n\n \n\n\n\n In McIlraith, S. A.; and Weinberger, K. Q., editor(s), Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence, (AAAI-18), New Orleans, Louisiana, USA, February 2-7, 2018, pages 5446–5455 (CORE Ranked A*), 2018. AAAI Press\n \n\n\n\n
\n\n\n\n \n \n \"DiSAN:Paper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@inproceedings{shen_disan:_2018,\n\ttitle = {{DiSAN}: {Directional} {Self}-{Attention} {Network} for {RNN}/{CNN}-{Free} {Language} {Understanding}},\n\tcopyright = {All rights reserved},\n\turl = {https://www.aaai.org/ocs/index.php/AAAI/AAAI18/paper/view/16126},\n\tbooktitle = {Proceedings of the {Thirty}-{Second} {AAAI} {Conference} on {Artificial} {Intelligence}, ({AAAI}-18), {New} {Orleans}, {Louisiana}, {USA}, {February} 2-7, 2018},\n\tpublisher = {AAAI Press},\n\tauthor = {Shen, Tao and Zhou, Tianyi and Long, Guodong and Jiang, Jing and Pan, Shirui and Zhang, Chengqi},\n\teditor = {McIlraith, Sheila A. and Weinberger, Kilian Q.},\n\tyear = {2018},\n\tpages = {5446--5455 (CORE Ranked A*)},\n}\n\n\n\n
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\n \n\n \n \n \n \n \n \n Binarized attributed network embedding.\n \n \n \n \n\n\n \n Yang, H.; Pan, S.; Zhang, P.; Chen, L.; Lian, D.; and Zhang, C.\n\n\n \n\n\n\n In IEEE International Conference on Data Mining, ICDM 2018, Singapore, November 17-20, 2018, pages 1476–1481, 2018. IEEE Computer Society\n \n\n\n\n
\n\n\n\n \n \n \"BinarizedPaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@inproceedings{yang_binarized_2018,\n\ttitle = {Binarized attributed network embedding},\n\tcopyright = {All rights reserved},\n\turl = {https://doi.org/10.1109/ICDM.2018.8626170},\n\tdoi = {10.1109/ICDM.2018.8626170 (CORE Ranked A*)},\n\tbooktitle = {{IEEE} {International} {Conference} on {Data} {Mining}, {ICDM} 2018, {Singapore}, {November} 17-20, 2018},\n\tpublisher = {IEEE Computer Society},\n\tauthor = {Yang, Hong and Pan, Shirui and Zhang, Peng and Chen, Ling and Lian, Defu and Zhang, Chengqi},\n\tyear = {2018},\n\tpages = {1476--1481},\n}\n\n\n\n
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\n \n\n \n \n \n \n \n A Three-Layered Mutually Reinforced Model for Personalized Citation Recommendation.\n \n \n \n\n\n \n Cai, X.; Han, J.; Li, W.; Zhang, R.; Pan, S.; and Yang, L.\n\n\n \n\n\n\n IEEE Transactions on Neural Networks and Learning Systems (TNNLS), 29(12): 6026–6037. 2018.\n \n\n\n\n
\n\n\n\n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@article{cai_three-layered_2018,\n\ttitle = {A {Three}-{Layered} {Mutually} {Reinforced} {Model} for {Personalized} {Citation} {Recommendation}},\n\tvolume = {29},\n\tcopyright = {All rights reserved},\n\tdoi = {10.1109/TNNLS.2018.2817245 (Impact Factor: 10.451; JCR Ranked Q1)},\n\tnumber = {12},\n\tjournal = {IEEE Transactions on Neural Networks and Learning Systems (TNNLS)},\n\tauthor = {Cai, Xiaoyan and Han, Junwei and Li, Wenjie and Zhang, Renxian and Pan, Shirui and Yang, Libin},\n\tyear = {2018},\n\tpages = {6026--6037},\n}\n\n\n\n
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\n \n\n \n \n \n \n \n \n Multi-Instance Learning with Discriminative Bag Mapping.\n \n \n \n \n\n\n \n Wu, J.; Pan, S.; Zhu, X.; Zhang, C.; and Wu, X.\n\n\n \n\n\n\n IEEE Transactions on Knowledge and Data Engineering (TKDE), 30(6): 1065–1080. 2018.\n \n\n\n\n
\n\n\n\n \n \n \"Multi-InstancePaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@article{wu_multi-instance_2018,\n\ttitle = {Multi-{Instance} {Learning} with {Discriminative} {Bag} {Mapping}},\n\tvolume = {30},\n\tcopyright = {All rights reserved},\n\turl = {https://doi.org/10.1109/TKDE.2017.2788430},\n\tdoi = {10.1109/TKDE.2017.2788430 (Impact Factor: 6.977; JCR Ranked Q1; Top Journal in Data Mining)},\n\tnumber = {6},\n\tjournal = {IEEE Transactions on Knowledge and Data Engineering (TKDE)},\n\tauthor = {Wu, Jia and Pan, Shirui and Zhu, Xingquan and Zhang, Chengqi and Wu, Xindong},\n\tyear = {2018},\n\tpages = {1065--1080},\n}\n\n\n\n
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\n \n\n \n \n \n \n \n Multiple Structure-View Learning for Graph Classification.\n \n \n \n\n\n \n Wu, J.; Pan, S.; Zhu, X.; Zhang, C.; and Yu, P. S.\n\n\n \n\n\n\n IEEE Transactions on Neural Networks and Learning Systems (TNNLS), 29(7): 3236–3251. 2018.\n \n\n\n\n
\n\n\n\n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@article{wu_multiple_2018,\n\ttitle = {Multiple {Structure}-{View} {Learning} for {Graph} {Classification}},\n\tvolume = {29},\n\tcopyright = {All rights reserved},\n\tdoi = {10.1109/TNNLS.2017.2703832 (Impact Factor: 10.451; JCR Ranked Q1)},\n\tnumber = {7},\n\tjournal = {IEEE Transactions on Neural Networks and Learning Systems (TNNLS)},\n\tauthor = {Wu, Jia and Pan, Shirui and Zhu, Xingquan and Zhang, Chengqi and Yu, Philip S.},\n\tyear = {2018},\n\tpages = {3236--3251},\n}\n\n\n\n
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\n \n\n \n \n \n \n \n \n Hashing for Adaptive Real-Time Graph Stream Classification With Concept Drifts.\n \n \n \n \n\n\n \n Chi, L.; Li, B.; Zhu, X.; Pan, S.; and Chen, L.\n\n\n \n\n\n\n IEEE transactions on cybernetics (TCYB), 48(5): 1591–1604. 2018.\n \n\n\n\n
\n\n\n\n \n \n \"HashingPaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@article{chi_hashing_2018,\n\ttitle = {Hashing for {Adaptive} {Real}-{Time} {Graph} {Stream} {Classification} {With} {Concept} {Drifts}},\n\tvolume = {48},\n\tcopyright = {All rights reserved},\n\turl = {https://doi.org/10.1109/TCYB.2017.2708979},\n\tdoi = {10.1109/TCYB.2017.2708979 (Impact Factor: 11.448; JCR Ranked Q1)},\n\tnumber = {5},\n\tjournal = {IEEE transactions on cybernetics (TCYB)},\n\tauthor = {Chi, Lianhua and Li, Bin and Zhu, Xingquan and Pan, Shirui and Chen, Ling},\n\tyear = {2018},\n\tpages = {1591--1604},\n}\n\n\n\n
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\n  \n 2017\n \n \n (8)\n \n \n
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\n \n\n \n \n \n \n \n MGAE: Marginalized graph autoencoder for graph clustering.\n \n \n \n\n\n \n Wang, C.; Pan, S.; Long, G.; Zhu, X.; and Jiang, J.\n\n\n \n\n\n\n In CIKM 2017 - Proceedings of the 2017 ACM on Conference on Information and Knowledge Management, pages 889–898, 2017. ACM\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@inproceedings{wang_mgae:_2017,\n\ttitle = {{MGAE}: {Marginalized} graph autoencoder for graph clustering},\n\tcopyright = {All rights reserved},\n\tbooktitle = {{CIKM} 2017 - {Proceedings} of the 2017 {ACM} on {Conference} on {Information} and {Knowledge} {Management}},\n\tpublisher = {ACM},\n\tauthor = {Wang, Chun and Pan, Shirui and Long, Guodong and Zhu, Xingquan and Jiang, Jing},\n\tyear = {2017},\n\tpages = {889--898},\n}\n\n\n\n
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\n \n\n \n \n \n \n \n Positive and Unlabeled Multi-Graph Learning.\n \n \n \n\n\n \n Wu, J.; Pan, S.; Zhu, X.; Zhang, C.; and Wu, X.\n\n\n \n\n\n\n IEEE Transactions on Cybernetics. 2017.\n Publisher: IEEE\n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@article{wu_positive_2017,\n\ttitle = {Positive and {Unlabeled} {Multi}-{Graph} {Learning}},\n\tcopyright = {All rights reserved},\n\tjournal = {IEEE Transactions on Cybernetics},\n\tauthor = {Wu, Jia and Pan, Shirui and Zhu, Xingquan and Zhang, Chengqi and Wu, Xindong},\n\tyear = {2017},\n\tnote = {Publisher: IEEE},\n}\n\n\n\n
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\n \n\n \n \n \n \n \n Task Sensitive Feature Exploration and Learning for Multitask Graph Classification.\n \n \n \n\n\n \n Pan, S.; Wu, J.; Zhu, X.; Long, G.; and Zhang, C.\n\n\n \n\n\n\n IEEE Transactions on Cybernetics. 2017.\n Publisher: IEEE\n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@article{pan_task_2017,\n\ttitle = {Task {Sensitive} {Feature} {Exploration} and {Learning} for {Multitask} {Graph} {Classification}},\n\tcopyright = {All rights reserved},\n\tjournal = {IEEE Transactions on Cybernetics},\n\tauthor = {Pan, Shirui and Wu, Jia and Zhu, Xingquan and Long, Guodong and Zhang, Chengqi},\n\tyear = {2017},\n\tnote = {Publisher: IEEE},\n}\n\n\n\n
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\n \n\n \n \n \n \n \n Graph Ladder Networks for Network Classification.\n \n \n \n\n\n \n Hu, R.; Pan, S.; Jiang, J.; and Long, G.\n\n\n \n\n\n\n In CIKM 2017, pages 2103–2106, 2017. \n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@inproceedings{hu_graph_2017,\n\ttitle = {Graph {Ladder} {Networks} for {Network} {Classification}},\n\tcopyright = {All rights reserved},\n\tbooktitle = {{CIKM} 2017},\n\tauthor = {Hu, Ruiqi and Pan, Shirui and Jiang, Jing and Long, Guodong},\n\tyear = {2017},\n\tpages = {2103--2106},\n}\n\n\n\n
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\n \n\n \n \n \n \n \n Multiple structure-view learning for graph classification.\n \n \n \n\n\n \n Wu, J.; Pan, S.; Zhu, X.; Zhang, C.; and Philip, S Y.\n\n\n \n\n\n\n IEEE Transactions on Neural Networks and Learning Systems (TNNLS), 29(7): 3236–3251. 2017.\n Publisher: IEEE\n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@article{wu_multiple_2017,\n\ttitle = {Multiple structure-view learning for graph classification},\n\tvolume = {29},\n\tcopyright = {All rights reserved},\n\tnumber = {7},\n\tjournal = {IEEE Transactions on Neural Networks and Learning Systems (TNNLS)},\n\tauthor = {Wu, Jia and Pan, Shirui and Zhu, Xingquan and Zhang, Chengqi and Philip, S Yu},\n\tyear = {2017},\n\tnote = {Publisher: IEEE},\n\tpages = {3236--3251},\n}\n\n\n\n
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\n \n\n \n \n \n \n \n Positive and Unlabeled Multi-Graph Learning.\n \n \n \n\n\n \n Wu, J.; Pan, S.; Zhu, X.; Zhang, C.; and Wu, X.\n\n\n \n\n\n\n IEEE transactions on cybernetics (TCYB), 47(4): 818–829. 2017.\n \n\n\n\n
\n\n\n\n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@article{wu_positive_2017,\n\ttitle = {Positive and {Unlabeled} {Multi}-{Graph} {Learning}},\n\tvolume = {47},\n\tcopyright = {All rights reserved},\n\tdoi = {10.1109/TCYB.2016.2527239 (Impact Factor: 11.448; JCR Ranked Q1)},\n\tnumber = {4},\n\tjournal = {IEEE transactions on cybernetics (TCYB)},\n\tauthor = {Wu, Jia and Pan, Shirui and Zhu, Xingquan and Zhang, Chengqi and Wu, Xindong},\n\tyear = {2017},\n\tpages = {818--829},\n}\n\n\n\n
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\n \n\n \n \n \n \n \n \n Exploiting Attribute Correlations: A Novel Trace Lasso-Based Weakly Supervised Dictionary Learning Method.\n \n \n \n \n\n\n \n Wu, L.; Wang, Y.; and Pan, S.\n\n\n \n\n\n\n IEEE transactions on cybernetics (TCYB), 47(12): 4497–4508. 2017.\n \n\n\n\n
\n\n\n\n \n \n \"ExploitingPaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@article{wu_exploiting_2017,\n\ttitle = {Exploiting {Attribute} {Correlations}: {A} {Novel} {Trace} {Lasso}-{Based} {Weakly} {Supervised} {Dictionary} {Learning} {Method}},\n\tvolume = {47},\n\tcopyright = {All rights reserved},\n\turl = {https://doi.org/10.1109/TCYB.2016.2612686},\n\tdoi = {10.1109/TCYB.2016.2612686 (Impact Factor: 11.448; JCR Ranked Q1)},\n\tnumber = {12},\n\tjournal = {IEEE transactions on cybernetics (TCYB)},\n\tauthor = {Wu, Lin and Wang, Yang and Pan, Shirui},\n\tyear = {2017},\n\tpages = {4497--4508},\n}\n\n\n\n
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\n \n\n \n \n \n \n \n Task Sensitive Feature Exploration and Learning for Multitask Graph Classification.\n \n \n \n\n\n \n Pan, S.; Wu, J.; Zhu, X.; Long, G.; and Zhang, C.\n\n\n \n\n\n\n IEEE transactions on cybernetics (TCYB), 47(3): 744–758. 2017.\n \n\n\n\n
\n\n\n\n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@article{pan_task_2017,\n\ttitle = {Task {Sensitive} {Feature} {Exploration} and {Learning} for {Multitask} {Graph} {Classification}},\n\tvolume = {47},\n\tcopyright = {All rights reserved},\n\tdoi = {10.1109/TCYB.2016.2526058 (Impact Factor: 11.448; JCR Ranked Q1)},\n\tnumber = {3},\n\tjournal = {IEEE transactions on cybernetics (TCYB)},\n\tauthor = {Pan, Shirui and Wu, Jia and Zhu, Xingquan and Long, Guodong and Zhang, Chengqi},\n\tyear = {2017},\n\tpages = {744--758},\n}\n\n\n\n
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\n  \n 2016\n \n \n (9)\n \n \n
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\n \n\n \n \n \n \n \n Iterative views agreement: An iterative low-rank based structured optimization method to multi-view spectral clustering.\n \n \n \n\n\n \n Wang, Y.; Zhang, W.; Wu, L.; Lin, X.; Fang, M.; and Pan, S.\n\n\n \n\n\n\n In International Joint Conference on Artificial Intelligence, IJCAI-16, 2016. \n \n\n\n\n
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@inproceedings{wang_iterative_2016,\n\ttitle = {Iterative views agreement: {An} iterative low-rank based structured optimization method to multi-view spectral clustering},\n\tcopyright = {All rights reserved},\n\tbooktitle = {International {Joint} {Conference} on {Artificial} {Intelligence}, {IJCAI}-16},\n\tauthor = {Wang, Yang and Zhang, Wenjie and Wu, Lin and Lin, Xuemin and Fang, Meng and Pan, Shirui},\n\tyear = {2016},\n}\n\n\n\n
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\n \n\n \n \n \n \n \n Exploiting Attribute Correlations: A Novel Trace Lasso based Weakly Supervised Dictionary Learning Method.\n \n \n \n\n\n \n Wu, L.; Wang, Y.; and Pan, S.\n\n\n \n\n\n\n IEEE Transactions on Cybernetics. 2016.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@article{wu_exploiting_2016,\n\ttitle = {Exploiting {Attribute} {Correlations}: {A} {Novel} {Trace} {Lasso} based {Weakly} {Supervised} {Dictionary} {Learning} {Method}},\n\tcopyright = {All rights reserved},\n\tjournal = {IEEE Transactions on Cybernetics},\n\tauthor = {Wu, Lin and Wang, Yang and Pan, Shirui},\n\tyear = {2016},\n}\n\n\n\n
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\n \n\n \n \n \n \n \n Tri-Party Deep Network Representation.\n \n \n \n\n\n \n Pan, S.; Wu, J.; Zhu, X.; Zhang, C.; and Wang, Y.\n\n\n \n\n\n\n In International Joint Conference on Artificial Intelligence, IJCAI-16, pages 1895–1901, 2016. \n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@inproceedings{pan_tri-party_2016,\n\ttitle = {Tri-{Party} {Deep} {Network} {Representation}},\n\tcopyright = {All rights reserved},\n\tbooktitle = {International {Joint} {Conference} on {Artificial} {Intelligence}, {IJCAI}-16},\n\tauthor = {Pan, Shirui and Wu, Jia and Zhu, Xingquan and Zhang, Chengqi and Wang, Yang},\n\tyear = {2016},\n\tpages = {1895--1901},\n}\n\n\n\n
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\n \n\n \n \n \n \n \n Joint Structure Feature Exploration and Regularization for Multi-Task Graph Classification.\n \n \n \n\n\n \n Pan, S.; Wu, J.; Zhu, X.; Zhang, C.; and Yu, P.\n\n\n \n\n\n\n IEEE Transactions on Knowledge and Data Engineering. 2016.\n Publisher: IEEE\n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@article{pan_joint_2016,\n\ttitle = {Joint {Structure} {Feature} {Exploration} and {Regularization} for {Multi}-{Task} {Graph} {Classification}},\n\tcopyright = {All rights reserved},\n\tjournal = {IEEE Transactions on Knowledge and Data Engineering},\n\tauthor = {Pan, Shirui and Wu, Jia and Zhu, Xingquan and Zhang, Chengqi and Yu, Philip},\n\tyear = {2016},\n\tnote = {Publisher: IEEE},\n}\n\n\n\n
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\n \n\n \n \n \n \n \n Tri-Party Deep Network Representation.\n \n \n \n\n\n \n Pan, S.; Wu, J.; Zhu, X.; Zhang, C.; and Wang, Y.\n\n\n \n\n\n\n In Kambhampati, S., editor(s), Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence, IJCAI 2016, New York, NY, USA, 9-15 July 2016, pages 1895–1901 (CORE Ranked A*), 2016. IJCAI/AAAI Press\n \n\n\n\n
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@inproceedings{pan_tri-party_2016,\n\ttitle = {Tri-{Party} {Deep} {Network} {Representation}},\n\tcopyright = {All rights reserved},\n\tbooktitle = {Proceedings of the {Twenty}-{Fifth} {International} {Joint} {Conference} on {Artificial} {Intelligence}, {IJCAI} 2016, {New} {York}, {NY}, {USA}, 9-15 {July} 2016},\n\tpublisher = {IJCAI/AAAI Press},\n\tauthor = {Pan, Shirui and Wu, Jia and Zhu, Xingquan and Zhang, Chengqi and Wang, Yang},\n\teditor = {Kambhampati, Subbarao},\n\tyear = {2016},\n\tpages = {1895--1901 (CORE Ranked A*)},\n}\n\n\n\n
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\n \n\n \n \n \n \n \n Iterative Views Agreement: An Iterative Low-Rank Based Structured Optimization Method to Multi-View Spectral Clustering.\n \n \n \n\n\n \n Wang, Y.; Zhang, W.; Wu, L.; Lin, X.; Fang, M.; and Pan, S.\n\n\n \n\n\n\n In Kambhampati, S., editor(s), Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence, IJCAI 2016, New York, NY, USA, 9-15 July 2016, pages 2153–2159 (CORE Ranked A*), 2016. IJCAI/AAAI Press\n \n\n\n\n
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@inproceedings{wang_iterative_2016,\n\ttitle = {Iterative {Views} {Agreement}: {An} {Iterative} {Low}-{Rank} {Based} {Structured} {Optimization} {Method} to {Multi}-{View} {Spectral} {Clustering}},\n\tcopyright = {All rights reserved},\n\tbooktitle = {Proceedings of the {Twenty}-{Fifth} {International} {Joint} {Conference} on {Artificial} {Intelligence}, {IJCAI} 2016, {New} {York}, {NY}, {USA}, 9-15 {July} 2016},\n\tpublisher = {IJCAI/AAAI Press},\n\tauthor = {Wang, Yang and Zhang, Wenjie and Wu, Lin and Lin, Xuemin and Fang, Meng and Pan, Shirui},\n\teditor = {Kambhampati, Subbarao},\n\tyear = {2016},\n\tpages = {2153--2159 (CORE Ranked A*)},\n}\n\n\n\n
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\n \n\n \n \n \n \n \n \n Direct Discriminative Bag Mapping for Multi-Instance Learning.\n \n \n \n \n\n\n \n Wu, J.; Pan, S.; Zhang, P.; and Zhu, X.\n\n\n \n\n\n\n In Schuurmans, D.; and Wellman, M. P., editor(s), Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence, February 12-17, 2016, Phoenix, Arizona, USA, pages 4274–4275 (CORE Ranked A*), 2016. AAAI Press\n \n\n\n\n
\n\n\n\n \n \n \"DirectPaper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@inproceedings{wu_direct_2016,\n\ttitle = {Direct {Discriminative} {Bag} {Mapping} for {Multi}-{Instance} {Learning}},\n\tcopyright = {All rights reserved},\n\turl = {http://www.aaai.org/ocs/index.php/AAAI/AAAI16/paper/view/11781},\n\tbooktitle = {Proceedings of the {Thirtieth} {AAAI} {Conference} on {Artificial} {Intelligence}, {February} 12-17, 2016, {Phoenix}, {Arizona}, {USA}},\n\tpublisher = {AAAI Press},\n\tauthor = {Wu, Jia and Pan, Shirui and Zhang, Peng and Zhu, Xingquan},\n\teditor = {Schuurmans, Dale and Wellman, Michael P.},\n\tyear = {2016},\n\tpages = {4274--4275 (CORE Ranked A*)},\n}\n\n\n\n
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\n \n\n \n \n \n \n \n Joint Structure Feature Exploration and Regularization for Multi-Task Graph Classification.\n \n \n \n\n\n \n Pan, S.; Wu, J.; Zhu, X.; Zhang, C.; and Yu, P. S.\n\n\n \n\n\n\n IEEE Transactions on Knowledge and Data Engineering (TKDE), 28(3): 715–728. 2016.\n \n\n\n\n
\n\n\n\n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@article{pan_joint_2016,\n\ttitle = {Joint {Structure} {Feature} {Exploration} and {Regularization} for {Multi}-{Task} {Graph} {Classification}},\n\tvolume = {28},\n\tcopyright = {All rights reserved},\n\tdoi = {10.1109/TKDE.2015.2492567 (Impact Factor: 6.977; JCR Ranked Q1; Top Journal in Data Mining)},\n\tnumber = {3},\n\tjournal = {IEEE Transactions on Knowledge and Data Engineering (TKDE)},\n\tauthor = {Pan, Shirui and Wu, Jia and Zhu, Xingquan and Zhang, Chengqi and Yu, Philip S.},\n\tyear = {2016},\n\tpages = {715--728},\n}\n\n\n\n
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\n \n\n \n \n \n \n \n SODE: Self-Adaptive One-Dependence Estimators for classification.\n \n \n \n\n\n \n Wu, J.; Pan, S.; Zhu, X.; Zhang, P.; and Zhang, C.\n\n\n \n\n\n\n Pattern Recognition (PR), 51: 358–377. 2016.\n \n\n\n\n
\n\n\n\n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@article{wu_sode:_2016,\n\ttitle = {{SODE}: {Self}-{Adaptive} {One}-{Dependence} {Estimators} for classification},\n\tvolume = {51},\n\tcopyright = {All rights reserved},\n\tdoi = {10.1016/j.patcog.2015.08.023 (Impact Factor: 7.74; JCR Ranked Q1)},\n\tjournal = {Pattern Recognition (PR)},\n\tauthor = {Wu, Jia and Pan, Shirui and Zhu, Xingquan and Zhang, Peng and Zhang, Chengqi},\n\tyear = {2016},\n\tpages = {358--377},\n}\n\n\n\n
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\n  \n 2015\n \n \n (7)\n \n \n
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\n \n\n \n \n \n \n \n CogBoost: Boosting for Fast Cost-sensitive Graph Classification.\n \n \n \n\n\n \n Pan, S.; Wu, J.; and Zhu, X.\n\n\n \n\n\n\n IEEE Transactions on Knowledge and Data Engineering, 27(11): 2933–2946. 2015.\n Publisher: IEEE\n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@article{pan_cogboost:_2015,\n\ttitle = {{CogBoost}: {Boosting} for {Fast} {Cost}-sensitive {Graph} {Classification}},\n\tvolume = {27},\n\tcopyright = {All rights reserved},\n\tnumber = {11},\n\tjournal = {IEEE Transactions on Knowledge and Data Engineering},\n\tauthor = {Pan, Shirui and Wu, Jia and Zhu, Xingquan},\n\tyear = {2015},\n\tnote = {Publisher: IEEE},\n\tpages = {2933--2946},\n}\n\n\n\n
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\n \n\n \n \n \n \n \n Multi-graph-view learning for complicated object classification.\n \n \n \n\n\n \n Wu, J.; Pan, S.; Zhu, X.; Cai, Z.; and Zhang, C.\n\n\n \n\n\n\n In International Joint Conference on Artificial Intelligence, IJCAI, 2015. \n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@inproceedings{wu_multi-graph-view_2015,\n\ttitle = {Multi-graph-view learning for complicated object classification},\n\tcopyright = {All rights reserved},\n\tbooktitle = {International {Joint} {Conference} on {Artificial} {Intelligence}, {IJCAI}},\n\tauthor = {Wu, Jia and Pan, Shirui and Zhu, Xingquan and Cai, Zhihua and Zhang, Chengqi},\n\tyear = {2015},\n}\n\n\n\n
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\n \n\n \n \n \n \n \n Multi-Graph-View Learning for Complicated Object Classification.\n \n \n \n\n\n \n Wu, J.; Pan, S.; Zhu, X.; Cai, Z.; and Zhang, C.\n\n\n \n\n\n\n In Yang, Q.; and Wooldridge, M. J., editor(s), Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence, IJCAI 2015, Buenos Aires, Argentina, July 25-31, 2015, pages 3953–3959 (CORE Ranked A*), 2015. AAAI Press\n \n\n\n\n
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\n 
@inproceedings{wu_multi-graph-view_2015,\n\ttitle = {Multi-{Graph}-{View} {Learning} for {Complicated} {Object} {Classification}},\n\tcopyright = {All rights reserved},\n\tbooktitle = {Proceedings of the {Twenty}-{Fourth} {International} {Joint} {Conference} on {Artificial} {Intelligence}, {IJCAI} 2015, {Buenos} {Aires}, {Argentina}, {July} 25-31, 2015},\n\tpublisher = {AAAI Press},\n\tauthor = {Wu, Jia and Pan, Shirui and Zhu, Xingquan and Cai, Zhihua and Zhang, Chengqi},\n\teditor = {Yang, Qiang and Wooldridge, Michael J.},\n\tyear = {2015},\n\tpages = {3953--3959 (CORE Ranked A*)},\n}\n\n\n\n
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\n \n\n \n \n \n \n \n Finding the best not the most: regularized loss minimization subgraph selection for graph classification.\n \n \n \n\n\n \n Pan, S.; Wu, J.; Zhu, X.; Long, G.; and Zhang, C.\n\n\n \n\n\n\n Pattern Recognition (PR), 48(11): 3783–3796. 2015.\n \n\n\n\n
\n\n\n\n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@article{pan_finding_2015,\n\ttitle = {Finding the best not the most: regularized loss minimization subgraph selection for graph classification},\n\tvolume = {48},\n\tcopyright = {All rights reserved},\n\tdoi = {10.1016/j.patcog.2015.05.019 (Impact Factor: 7.74; JCR Ranked Q1)},\n\tnumber = {11},\n\tjournal = {Pattern Recognition (PR)},\n\tauthor = {Pan, Shirui and Wu, Jia and Zhu, Xingquan and Long, Guodong and Zhang, Chengqi},\n\tyear = {2015},\n\tpages = {3783--3796},\n}\n\n\n\n
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\n \n\n \n \n \n \n \n Graph Ensemble Boosting for Imbalanced Noisy Graph Stream Classification.\n \n \n \n\n\n \n Pan, S.; Wu, J.; Zhu, X.; and Zhang, C.\n\n\n \n\n\n\n IEEE transactions on cybernetics (TCYB), 45(5): 940–954. 2015.\n \n\n\n\n
\n\n\n\n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@article{pan_graph_2015,\n\ttitle = {Graph {Ensemble} {Boosting} for {Imbalanced} {Noisy} {Graph} {Stream} {Classification}},\n\tvolume = {45},\n\tcopyright = {All rights reserved},\n\tdoi = {10.1109/TCYB.2014.2341031 (Impact Factor: 11.448; JCR Ranked Q1)},\n\tnumber = {5},\n\tjournal = {IEEE transactions on cybernetics (TCYB)},\n\tauthor = {Pan, Shirui and Wu, Jia and Zhu, Xingquan and Zhang, Chengqi},\n\tyear = {2015},\n\tpages = {940--954},\n}\n\n\n\n
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\n \n\n \n \n \n \n \n CogBoost: Boosting for Fast Cost-Sensitive Graph Classification.\n \n \n \n\n\n \n Pan, S.; Wu, J.; and Zhu, X.\n\n\n \n\n\n\n IEEE Transactions on Knowledge and Data Engineering (TKDE), 27(11): 2933–2946. 2015.\n \n\n\n\n
\n\n\n\n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@article{pan_cogboost:_2015,\n\ttitle = {{CogBoost}: {Boosting} for {Fast} {Cost}-{Sensitive} {Graph} {Classification}},\n\tvolume = {27},\n\tcopyright = {All rights reserved},\n\tdoi = {10.1109/TKDE.2015.2391115 (Impact Factor: 6.977; JCR Ranked Q1; Top Journal in Data Mining)},\n\tnumber = {11},\n\tjournal = {IEEE Transactions on Knowledge and Data Engineering (TKDE)},\n\tauthor = {Pan, Shirui and Wu, Jia and Zhu, Xingquan},\n\tyear = {2015},\n\tpages = {2933--2946},\n}\n\n\n\n
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\n \n\n \n \n \n \n \n Boosting for Multi-Graph Classification.\n \n \n \n\n\n \n Wu, J.; Pan, S.; Zhu, X.; and Cai, Z.\n\n\n \n\n\n\n IEEE transactions on cybernetics (TCYB), 45(3): 430–443. 2015.\n \n\n\n\n
\n\n\n\n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@article{wu_boosting_2015,\n\ttitle = {Boosting for {Multi}-{Graph} {Classification}},\n\tvolume = {45},\n\tcopyright = {All rights reserved},\n\tdoi = {10.1109/TCYB.2014.2327111 (Impact Factor: 11.448; JCR Ranked Q1)},\n\tnumber = {3},\n\tjournal = {IEEE transactions on cybernetics (TCYB)},\n\tauthor = {Wu, Jia and Pan, Shirui and Zhu, Xingquan and Cai, Zhihua},\n\tyear = {2015},\n\tpages = {430--443},\n}\n\n\n\n
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\n  \n 2014\n \n \n (6)\n \n \n
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\n \n\n \n \n \n \n \n Multi-graph learning with positive and unlabeled bags.\n \n \n \n\n\n \n Wu, J.; Hong, Z.; Pan, S.; Zhu, X.; Zhang, C.; and Cai, Z.\n\n\n \n\n\n\n In Proceedings of the 2014 SIAM international conference on data mining (SDM), pages 217–225, 2014. Society for Industrial and Applied Mathematics\n \n\n\n\n
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\n 
@inproceedings{wu_multi-graph_2014,\n\ttitle = {Multi-graph learning with positive and unlabeled bags},\n\tcopyright = {All rights reserved},\n\tbooktitle = {Proceedings of the 2014 {SIAM} international conference on data mining ({SDM})},\n\tpublisher = {Society for Industrial and Applied Mathematics},\n\tauthor = {Wu, Jia and Hong, Zhibin and Pan, Shirui and Zhu, Xingquan and Zhang, Chengqi and Cai, Zhihua},\n\tyear = {2014},\n\tpages = {217--225},\n}\n\n\n\n
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\n \n\n \n \n \n \n \n Multi-graph-view learning for graph classification.\n \n \n \n\n\n \n Wu, J.; Hong, Z.; Pan, S.; Zhu, X.; Cai, Z.; and Zhang, C.\n\n\n \n\n\n\n In IEEE International Conference on Data Mining, ICDM-14, pages 590–599, 2014. IEEE\n \n\n\n\n
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@inproceedings{wu_multi-graph-view_2014,\n\ttitle = {Multi-graph-view learning for graph classification},\n\tcopyright = {All rights reserved},\n\tbooktitle = {{IEEE} {International} {Conference} on {Data} {Mining}, {ICDM}-14},\n\tpublisher = {IEEE},\n\tauthor = {Wu, Jia and Hong, Zhibin and Pan, Shirui and Zhu, Xingquan and Cai, Zhihua and Zhang, Chengqi},\n\tyear = {2014},\n\tpages = {590--599},\n}\n\n\n\n
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\n \n\n \n \n \n \n \n Exploring Features for Complicated Objects: Cross-View Feature Selection for Multi-Instance Learning.\n \n \n \n\n\n \n Wu, J.; Hong, Z.; Pan, S.; Zhu, X.; Cai, Z.; and Zhang, C.\n\n\n \n\n\n\n In CIKM'14-Proceedings of the 23rd ACM International Conference on Conference on Information and Knowledge Management, pages 1699–1708, 2014. ACM\n \n\n\n\n
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@inproceedings{wu_exploring_2014,\n\ttitle = {Exploring {Features} for {Complicated} {Objects}: {Cross}-{View} {Feature} {Selection} for {Multi}-{Instance} {Learning}},\n\tcopyright = {All rights reserved},\n\tbooktitle = {{CIKM}'14-{Proceedings} of the 23rd {ACM} {International} {Conference} on {Conference} on {Information} and {Knowledge} {Management}},\n\tpublisher = {ACM},\n\tauthor = {Wu, Jia and Hong, Zhibin and Pan, Shirui and Zhu, Xingquan and Cai, Zhihua and Zhang, Chengqi},\n\tyear = {2014},\n\tpages = {1699--1708},\n}\n\n\n\n
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\n \n\n \n \n \n \n \n Graph ensemble boosting for imbalanced noisy graph stream classification.\n \n \n \n\n\n \n Pan, S.; Wu, J.; Zhu, X.; and Zhang, C.\n\n\n \n\n\n\n IEEE Transactions on Cybernetics (TCYB), 45(5): 954–968. 2014.\n Publisher: IEEE\n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@article{pan_graph_2014,\n\ttitle = {Graph ensemble boosting for imbalanced noisy graph stream classification},\n\tvolume = {45},\n\tcopyright = {All rights reserved},\n\tnumber = {5},\n\tjournal = {IEEE Transactions on Cybernetics (TCYB)},\n\tauthor = {Pan, Shirui and Wu, Jia and Zhu, Xingquan and Zhang, Chengqi},\n\tyear = {2014},\n\tnote = {Publisher: IEEE},\n\tpages = {954--968},\n}\n\n\n\n
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\n \n\n \n \n \n \n \n Boosting for multi-graph classification.\n \n \n \n\n\n \n Wu, J.; Pan, S.; Zhu, X.; and Cai, Z.\n\n\n \n\n\n\n IEEE Transactions on Cybernetics (TCYB), 45(3): 416–429. 2014.\n Publisher: IEEE\n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@article{wu_boosting_2014,\n\ttitle = {Boosting for multi-graph classification},\n\tvolume = {45},\n\tcopyright = {All rights reserved},\n\tnumber = {3},\n\tjournal = {IEEE Transactions on Cybernetics (TCYB)},\n\tauthor = {Wu, Jia and Pan, Shirui and Zhu, Xingquan and Cai, Zhihua},\n\tyear = {2014},\n\tnote = {Publisher: IEEE},\n\tpages = {416--429},\n}\n\n\n\n
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\n \n\n \n \n \n \n \n Multi-graph-view Learning for Graph Classification.\n \n \n \n\n\n \n Wu, J.; Hong, Z.; Pan, S.; Zhu, X.; Cai, Z.; and Zhang, C.\n\n\n \n\n\n\n In Kumar, R.; Toivonen, H.; Pei, J.; Huang, J. Z.; and Wu, X., editor(s), 2014 IEEE International Conference on Data Mining, ICDM 2014, Shenzhen, China, December 14-17, 2014, pages 590–599, 2014. IEEE Computer Society\n \n\n\n\n
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@inproceedings{wu_multi-graph-view_2014,\n\ttitle = {Multi-graph-view {Learning} for {Graph} {Classification}},\n\tcopyright = {All rights reserved},\n\tdoi = {10.1109/ICDM.2014.97 (CORE Ranked A*)},\n\tbooktitle = {2014 {IEEE} {International} {Conference} on {Data} {Mining}, {ICDM} 2014, {Shenzhen}, {China}, {December} 14-17, 2014},\n\tpublisher = {IEEE Computer Society},\n\tauthor = {Wu, Jia and Hong, Zhibin and Pan, Shirui and Zhu, Xingquan and Cai, Zhihua and Zhang, Chengqi},\n\teditor = {Kumar, Ravi and Toivonen, Hannu and Pei, Jian and Huang, Joshua Zhexue and Wu, Xindong},\n\tyear = {2014},\n\tpages = {590--599},\n}\n\n\n\n
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\n  \n 2013\n \n \n (4)\n \n \n
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\n \n\n \n \n \n \n \n Graph classification with imbalanced class distributions and noise.\n \n \n \n\n\n \n Pan, S.; and Zhu, X.\n\n\n \n\n\n\n In International Joint Conference on Artificial Intelligence, IJCAI-13, pages 1586–1592, 2013. AAAI Press\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@inproceedings{pan_graph_2013,\n\ttitle = {Graph classification with imbalanced class distributions and noise},\n\tcopyright = {All rights reserved},\n\tbooktitle = {International {Joint} {Conference} on {Artificial} {Intelligence}, {IJCAI}-13},\n\tpublisher = {AAAI Press},\n\tauthor = {Pan, Shirui and Zhu, Xingquan},\n\tyear = {2013},\n\tpages = {1586--1592},\n}\n\n\n\n
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\n \n\n \n \n \n \n \n Graph stream classification using labeled and unlabeled graphs.\n \n \n \n\n\n \n Pan, S.; Zhu, X.; Zhang, C.; and Philip, S Y.\n\n\n \n\n\n\n In 2013 IEEE 29th International Conference on Data Engineering (ICDE), pages 398–409, 2013. IEEE\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@inproceedings{pan_graph_2013,\n\ttitle = {Graph stream classification using labeled and unlabeled graphs},\n\tcopyright = {All rights reserved},\n\tbooktitle = {2013 {IEEE} 29th {International} {Conference} on {Data} {Engineering} ({ICDE})},\n\tpublisher = {IEEE},\n\tauthor = {Pan, Shirui and Zhu, Xingquan and Zhang, Chengqi and Philip, S Yu},\n\tyear = {2013},\n\tpages = {398--409},\n}\n\n\n\n
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\n \n\n \n \n \n \n \n Graph stream classification using labeled and unlabeled graphs.\n \n \n \n\n\n \n Pan, S.; Zhu, X.; Zhang, C.; and Yu, P. S.\n\n\n \n\n\n\n In Jensen, C. S.; Jermaine, C. M.; and Zhou, X., editor(s), 29th IEEE International Conference on Data Engineering, ICDE 2013, Brisbane, Australia, April 8-12, 2013, pages 398–409, 2013. IEEE Computer Society\n \n\n\n\n
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@inproceedings{pan_graph_2013,\n\ttitle = {Graph stream classification using labeled and unlabeled graphs},\n\tcopyright = {All rights reserved},\n\tdoi = {10.1109/ICDE.2013.6544842 (CORE Ranked A*)},\n\tbooktitle = {29th {IEEE} {International} {Conference} on {Data} {Engineering}, {ICDE} 2013, {Brisbane}, {Australia}, {April} 8-12, 2013},\n\tpublisher = {IEEE Computer Society},\n\tauthor = {Pan, Shirui and Zhu, Xingquan and Zhang, Chengqi and Yu, Philip S.},\n\teditor = {Jensen, Christian S. and Jermaine, Christopher M. and Zhou, Xiaofang},\n\tyear = {2013},\n\tpages = {398--409},\n}\n\n\n\n
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\n \n\n \n \n \n \n \n Graph Classification with Imbalanced Class Distributions and Noise.\n \n \n \n\n\n \n Pan, S.; and Zhu, X.\n\n\n \n\n\n\n In Rossi, F., editor(s), IJCAI 2013, Proceedings of the 23rd International Joint Conference on Artificial Intelligence, Beijing, China, August 3-9, 2013, pages 1586–1592 (CORE Ranked A*), 2013. IJCAI/AAAI\n \n\n\n\n
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@inproceedings{pan_graph_2013,\n\ttitle = {Graph {Classification} with {Imbalanced} {Class} {Distributions} and {Noise}},\n\tcopyright = {All rights reserved},\n\tbooktitle = {{IJCAI} 2013, {Proceedings} of the 23rd {International} {Joint} {Conference} on {Artificial} {Intelligence}, {Beijing}, {China}, {August} 3-9, 2013},\n\tpublisher = {IJCAI/AAAI},\n\tauthor = {Pan, Shirui and Zhu, Xingquan},\n\teditor = {Rossi, Francesca},\n\tyear = {2013},\n\tpages = {1586--1592 (CORE Ranked A*)},\n}\n\n\n\n
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\n  \n 2012\n \n \n (2)\n \n \n
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\n \n\n \n \n \n \n \n CGStream: continuous correlated graph query for data streams.\n \n \n \n\n\n \n Pan, S.; and Zhu, X.\n\n\n \n\n\n\n In CIKM'12-Proceedings of the 21st ACM international conference on Information and knowledge management, 2012. \n \n\n\n\n
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@inproceedings{pan_cgstream:_2012,\n\ttitle = {{CGStream}: continuous correlated graph query for data streams},\n\tcopyright = {All rights reserved},\n\tbooktitle = {{CIKM}'12-{Proceedings} of the 21st {ACM} international conference on {Information} and knowledge management},\n\tauthor = {Pan, Shirui and Zhu, Xingquan},\n\tyear = {2012},\n}\n\n\n\n
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\n \n\n \n \n \n \n \n Continuous top-k query for graph streams.\n \n \n \n\n\n \n Pan, S.; and Zhu, X.\n\n\n \n\n\n\n In CIKM'12-Proceedings of the 21st ACM international conference on Information and knowledge management, pages 2659–2662, 2012. ACM\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@inproceedings{pan_continuous_2012,\n\ttitle = {Continuous top-k query for graph streams},\n\tcopyright = {All rights reserved},\n\tbooktitle = {{CIKM}'12-{Proceedings} of the 21st {ACM} international conference on {Information} and knowledge management},\n\tpublisher = {ACM},\n\tauthor = {Pan, Shirui and Zhu, Xingquan},\n\tyear = {2012},\n\tpages = {2659--2662},\n}\n\n\n\n
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