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  2022 (15)
Dual Interactive Graph Convolutional Networks for Hyperspectral Image Classification. Wan, S.; Pan, S.; Zhong, P.; Chang, X.; Yang, J.; and Gong, C. IEEE Transactions on Geoscience and Remote Sensing (TGRS), 60: 1-14. 2022.
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A Survey on Knowledge Graphs: Representation, Acquisition, and Applications. Ji, S.; Pan, S.; Cambria, E.; Marttinen, P.; and Yu, P., S. IEEE Transactions on Neural Networks and Learning Systems (TNNLS), 33(2): 494-514. 2022.
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One-shot Learning-based Animal Video Segmentation. Xue, T.; Qiao, Y.; Kong, H.; Su, D.; Pan, S.; Rafique, K.; and Sukkarieh, S. IEEE Transactions on Industrial Informatics (TII), 18(6): 3799-3807. 2022.
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Predicting Human Mobility via Graph Convolutional Dual-attentive Networks. Dang, W.; Wang, H.; Pan, S.; Zhang, P.; Zhou, C.; Chen, X.; and Wang, J. In International Conference on Web Search and Data Mining (WSDM), pages 192–200 (CORE Ranked A*), 2022. ACM
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Dual Space Graph Contrastive Learning. Yang, H.; Chen, H.; Pan, S.; Li, L.; Yu, P., S.; and Xu, G. In The Web Conference (WWW), 2022. ACM
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Exploring Relational Semantics for Inductive Knowledge Graph Completion. Wang, C.; Zhou, X.; Pan, S.; Dong, L.; Song, Z.; and Sha, Y. In Proceedings of the AAAI Conference on Artificial Intelligence (AAAI), 2022. AAAI Press
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Towards Unsupervised Deep Graph Structure Learning. Liu, Y.; Zheng, Y.; Zhang, D.; Chen, H.; Peng, H.; and Pan, S. In The Web Conference (WWW), 2022. ACM
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Reinforced, Incremental and Cross-lingual Event Detection From Social Messages. Peng, H.; Zhang, R.; Li, S.; Cao, Y.; Pan, S.; and Yu, P., S. IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI),1. 2022.
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Attraction and Repulsion: Unsupervised Domain Adaptive Graph Contrastive Learning Network. Wu, M.; Pan, S.; and Zhu, X. IEEE Transactions on Emerging Topics in Computational Intelligence (TETCI),1. 2022.
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BaLeNAS: Differentiable Architecture Search via Bayesian Learning Rule. Zhang, M.; Pan, S.; Chang, X.; Su, S.; Hu, J.; Haffari, R.; and Yang, B. In IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR-22, New Orleans, Louisiana, US, Jun 19-24, 2022 (accepted on 03/03/2022) (CORE Ranked A*), 2022.
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Fire Burns, Swords Cut: Commonsense Inductive Bias for Exploration in Text-based Games. Ryu, D.; Shareghi, E.; Fang, M.; Xu, Y.; Pan, S.; and Haffari, R. In 60th Annual Meeting of the Association for Computational Linguistics (ACL-2022), Dublin, Ireland, May 22-27, 2022 (accepted on 24/02/2022) (CORE Ranked A*), 2022.
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Cross-modal Clinical Graph Transformer For Ophthalmic Report Generation. Li, M.; Cai, W.; Verspoor, K.; Pan, S.; Li, X.; and Chang, X. In IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR-22, New Orleans, Louisiana, US, Jun 19-24, 2022 (accepted on 03/03/2022) (CORE Ranked A*), 2022.
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A Probabilistic Graphical Model Based on Neural-symbolic Reasoning for Visual Relationship Detection. Yu, D.; Yang, B.; Wei, Q.; Li, A.; and Pan, S. In IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR-22, New Orleans, Louisiana, US, Jun 19-24, 2022 (accepted on 03/03/2022) (CORE Ranked A*), 2022.
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Towards Spatio-Temporal Aware Traffic Time Series Forecasting. Cirstea, R.; Yang, B.; Guo, C.; Kieu, T.; and Pan, S. In IEEE International Conference on Data Engineering (ICDE-22), (Virtual) Kuala Lumpur, Malaysia, May 9-12, 2022 (accepted on 14/01/2022) (CORE Ranked A*), 2022.
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Predicting Best-Selling New Products in a Major Promotion Campaign Through Graph Convolutional Networks. Li, C.; Jiang, W.; Yang, Y.; Pan, S.; Guo, L.; and Huang, G. IEEE Transactions on Neural Networks and Learning Systems (TNNLS),1-14. 2022.
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  2021 (22)
Contrastive and Generative Graph Convolutional Networks for Graph-based Semi-Supervised Learning. Wan, S.; Pan, S.; Yang, J.; and Gong, C. In Thirty-Fifth AAAI Conference on Artificial Intelligence, AAAI 2021, pages 10049-10057 (CORE Ranked A*), 2021. AAAI Press
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One-Shot Neural Architecture Search: Maximising Diversity to Overcome Catastrophic Forgetting. Zhang, M.; Li, H.; Pan, S.; Chang, X.; Zhou, C.; Ge, Z.; and W. Su, S. IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 43(9): 2921-2935. 2021.
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Suicidal Ideation Detection: A Review of Machine Learning Methods and Applications. Ji, S.; Pan, S.; Li, X.; Cambria, E.; Long, G.; and Huang, Z. IEEE Transactions on Computational Social Systems (TCSS), 8(1): 214-226. 2021.
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Identify Topic Relations in Scientific Literature Using Topic Modeling. Chen, H.; Wang, X.; Pan, S.; and Xiong, F. IEEE Transactions on Engineering Management (TEM), 68(5): 1232-1244. 2021.
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Convolutional Neural Networks based Lung Nodule Classification: A Surrogate-Assisted Evolutionary Algorithm for Hyperparameter Optimization. Zhang, M.; Li, H.; Pan, S.; Lyu, J.; Ling, S.; and Su, S. IEEE Transactions on Evolutionary Computation (TEvC), 25(5): 869-882. 2021.
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Anomaly Detection on Attributed Networks via Contrastive Self-Supervised Learning. Liu, Y.; Li, Z.; Pan, S.; Gong, C.; Zhou, C.; and Karypis, G. IEEE Transactions on Neural Networks and Learning Systems (TNNLS),1-15. 2021.
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Temporal Network Embedding for Link Prediction via VAE Joint Attention Mechanism. Jiao, P.; Guo, X.; Jing, X.; He, D.; Wu, H.; Pan, S.; Gong, M.; and Wang, W. IEEE Transactions on Neural Networks and Learning Systems (TNNLS),1-14. 2021.
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A Comprehensive Survey on Graph Neural Networks. Wu, Z.; Pan, S.; Chen, F.; Long, G.; Zhang, C.; and Yu, P., S. IEEE Transactions on Neural Networks and Learning Systems (TNNLS), 32(1): 4-24. 2021.
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Influence Spread in Geo-Social Networks: A Multiobjective Optimization Perspective. Wang, L.; Yu, Z.; Xiong, F.; Yang, D.; Pan, S.; and Yan, Z. IEEE Transactions on Cybernetics (TCYB), 51(5): 2663-2675. 2021.
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Graph Learning: A Survey. Xia, F.; Sun, K.; Yu, S.; Aziz, A.; Wan, L.; Pan, S.; and Liu, H. IEEE Transactions on Artificial Intelligence (TAI), 2(2): 109-127. 2021.
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Hyperspectral Image Classification With Context-Aware Dynamic Graph Convolutional Network. Wan, S.; Gong, C.; Zhong, P.; Pan, S.; Li, G.; and Yang, J. IEEE Transactions on Geoscience and Remote Sensing (TGRS), 59(1): 597-612. 2021.
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Task-adaptive Neural Process for User Cold-Start Recommendation. Lin, X.; Wu, J.; Zhou, C.; Pan, S.; Cao, Y.; and Wang, B. In Leskovec, J.; Grobelnik, M.; Najork, M.; Tang, J.; and Zia, L., editor(s), WWW '21: The Web Conference 2021, Virtual Event / Ljubljana, Slovenia, April 19-23, 2021, pages 1306-1316, 2021. ACM / IW3C2
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iDARTS: Differentiable Architecture Search with Stochastic Implicit Gradients. Zhang, M.; Su, S.; Pan, S.; Chang, X.; Abbasnejad, E.; and Haffari, R. In International Conference on Machine Learning (ICML), pages 12557-12566 (CORE Ranked A*), 2021.
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Multi-Scale Contrastive Siamese Networks for Self-Supervised Graph Representation Learning. Jin, M.; Zheng, Y.; Li, Y.; Gong, C.; Zhou, C.; and Pan, S. In International Joint Conference on Artificial Intelligence, IJCAI, pages 1477-1483 (CORE Ranked A*), 2021.
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A Survey of Community Detection Approaches: From Statistical Modeling to Deep Learning. Jin, D.; Yu, Z.; Jiao, P.; Pan, S.; He, D.; Wu, J.; Yu, P.; and Zhang, W. IEEE Transactions on Knowledge and Data Engineering (TKDE),1. 2021.
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Learning Graph Representations With Maximal Cliques. Molaei, S.; Bousejin, N., G.; Zare, H.; Jalili, M.; and Pan, S. IEEE Transactions on Neural Networks and Learning Systems (TNNLS),1-8. 2021.
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IGNSCDA: Predicting CircRNA-Disease Associations Based on Improved Graph Convolutional Network and Negative Sampling. Lan, W.; Dong, Y.; Chen, Q.; Liu, J.; Wang, J.; Chen, Y., P.; and Pan, S. IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB),1. 2021.
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Learning Graph Neural Networks with Positive and Unlabeled Nodes. Wu, M.; Pan, S.; Du, L.; and Zhu, X. ACM Transactions on Knowledge Discovery from Data (TKDD), 15(6): 101:1--101:25. 2021.
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Anomaly Detection in Dynamic Graphs via Transformer. Liu, Y.; Pan, S.; Wang, Y., G.; Xiong, F.; Wang, L.; Chen, Q.; and Lee, V., C., S. IEEE Transactions on Knowledge and Data Engineering (TKDE),1-14. 2021.
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Adapting Membership Inference Attacks to GNN for Graph Classification: Approaches and Implications. Wu, B.; Yang, X.; Pan, S.; and Yuan, X. In IEEE International Conference on Data Mining (ICDM), pages 1421-1426 (CORE Ranked A*), 2021.
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Hypergraph Convolutional Network for Group Recommendation. Jia, R.; Zhou, X.; Dong, L.; and Pan, S. In IEEE International Conference on Data Mining (ICDM), pages 260-269 (CORE Ranked A*), 2021.
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Contrastive Graph Poisson Networks: Semi-Supervised Learning with Extremely Limited Labels. Wan, S.; Zhan, Y.; Liu, L.; Yu, B.; Pan, S.; and Gong, C. In Advances in Neural Information Processing Systems (NeurIPS), volume 34, 2021.
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  2020 (22)
Social Recommendation with Evolutionary Opinion Dynamics. Xiong, F.; Wang, X.; Pan, S.; Yang, H.; Wang, H.; and Zhang, C. IEEE Transactions on Systems, Man, and Cybernetics: Systems (TSMC), 50(10): 3804-3816. 2020.
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Connecting the Dots: Multivariate Time Series Forecasting with Graph Neural Networks. Wu, Z.; Pan, S.; Long, G.; Jiang, J.; Chang, X.; and Zhang, C. In Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), pages 753-763 (CORE Ranked A*), 2020.
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Familial Clustering for Weakly-Labeled Android Malware Using Hybrid Representation Learning. Zhang, Y.; Sui, Y.; Pan, S.; Zheng, Z.; Ning, B.; Tsang, I.; and Zhou, W. IEEE Transactions on Information Forensics and Security (TIFS), 15: 3401-3414. 2020.
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Clustering social audiences in business information networks. Zheng, Y.; Hu, R.; Fung, S., f.; Yu, C.; Long, G.; Guo, T.; and Pan, S. Pattern Recognition, 100: 107126. 2020.
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Compact Scheduling for Task Graph Oriented Mobile Crowdsourcing. Wang, L.; Yu, Z.; Han, Q.; Yang, D.; Pan, S.; Yao, Y.; and Zhang, D. IEEE Transactions on Mobile Computing (TMC),1. 2020.
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Distributed Feature Selection for Big Data Using Fuzzy Rough Sets. Kong, L.; Qu, W.; Yu, J.; Zuo, H.; Chen, G.; Xiong, F.; Pan, S.; Lin, S.; and Qiu, M. IEEE Transactions on Fuzzy Systems (TFS), 28(5): 846-857. 2020.
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Learning Graph Embedding With Adversarial Training Methods. Pan, S.; Hu, R.; Fung, S.; Long, G.; Jiang, J.; and Zhang, C. IEEE Transactions on Cybernetics (TCYB), 50(6): 2475-2487. 2020.
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Exploiting Implicit Influence From Information Propagation for Social Recommendation. Xiong, F.; Shen, W.; Chen, H.; Pan, S.; Wang, X.; and Yan, Z. IEEE Transactions on Cybernetics (TCYB), 50(10): 4186-4199. 2020.
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GSSNN: Graph Smoothing Splines Neural Networks. Zhu, S.; Zhou, L.; Pan, S.; Zhou, C.; Yan, G.; and Wang, B. 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.
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Grounding visual concepts for zero-shot Event Detection and Event Captioning. Li, Z.; Chang, X.; Yao, L.; Pan, S.; Zongyuan, G.; and Zhang, H. In Tang, J.; and Aditya Prakash, B., editor(s), Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (KDD), pages 297–305, 8 2020. Association for Computing Machinery (ACM)
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One-Shot Neural Architecture Search via Novelty Driven Sampling. Zhang, M.; Li, H.; Pan, S.; Liu, T.; and Su, S. In International Joint Conference on Artificial Intelligence, IJCAI-20, Yokohama, Japan, January, 2021, pages 3188--3194 (CORE Ranked A*), 2020.
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Reinforcement Learning based Meta-path Discovery in Large-scale Heterogeneous Information Networks. Wan, G.; Du, B.; Pan, S.; and Haffari, G. 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.
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Graph Stochastic Neural Networks for Semi-supervised Learning. Wang, H.; Zhou, C.; Chen, X.; Wu, J.; Pan, S.; and Wang, J. 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 Ma, 2020.
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Overcoming Multi-Model Forgetting in One-Shot NAS with Diversity Maximization. Zhang, M.; Li, H.; Pan, S.; Chang, X.; and Su, S. In IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR-20, pages 7809--7818 (CORE Ranked A*), 2020.
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Reasoning Like Human: Hierarchical Reinforcement Learning for Knowledge Graph Reasoning. Wan, G.; Pan, S.; Gong, C.; Zhou, C.; and Haffari, G. In International Joint Conference on Artificial Intelligence, IJCAI-20, Yokohama, Japan, January, 2021, pages 1926--1932 (CORE Ranked A*), 2020.
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OpenWGL: Open-World Graph Learning. Wu, M.; Pan, S.; and Zhu, X. 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.
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Cross-Graph: Robust and Unsupervised Embedding for Attributed Graphs with Corrupted Structure. Wang, C.; Han, B.; Pan, S.; Jiang, J.; Niu, G.; and Long, G. In IEEE International Conference on Data Mining, ICDM-20, November 17-20, 2020, Sorrento, Italy, pages 571-580 (CORE Ranked A*), 2020.
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Graph Geometry Interaction Learning. Zhu, S.; Pan, S.; Zhou, C.; Wu, J.; Cao, Y.; and Wang, B. 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 Mach, 2020.
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Going Deep: Graph Convolutional Ladder-Shape Networks. Hu, R.; Pan, S.; Long, G.; Lu, Q.; Zhu, L.; and Jiang, J. 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.
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A Relation-Specific Attention Network for Joint Entity and Relation Extraction. Yuan, Y.; Zhou, X.; Pan, S.; Zhu, Q.; Song, Z.; and Guo, L. In International Joint Conference on Artificial Intelligence, IJCAI-20, Yokohama, Japan, January, 2021, pages 4054--4060 (CORE Ranked A*), 2020.
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Unsupervised Domain Adaptive Graph Convolutional Networks. Wu, M.; Pan, S.; Zhou, C.; Chang, X.; and Zhu, X. In The Web Conference (WWW), WWW-20, Taipei, Taiwan, April 20-24, 2020, pages 1457-1467 (CORE Ranked A*), 2020.
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Differentiable Neural Architecture Search in Equivalent Space with Exploration Enhancement. Zhang, M.; Li, H.; Pan, S.; Chang, X.; Ge, Z.; and Su, S. 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 Ma, 2020.
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  2019 (9)
Low-Bit Quantization for Attributed Network Representation Learning. Yang, H.; Pan, S.; Chen, L.; Zhou, C.; and Zhang, P. 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 (CORE Ranked A*), 2019. ijcai.org
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Time series feature learning with labeled and unlabeled data. Wang, H.; Zhang, Q.; Wu, J.; Pan, S.; and Chen, Y. Pattern Recognition, 89: 55-66. 2019.
Time series feature learning with labeled and unlabeled data [link]Website   doi   link   bibtex   abstract   1 download  
Label Embedding with Partial Heterogeneous Contexts. Shi, Y.; Xu, D.; Pan, Y.; Tsang, I., W.; and Pan, S. In Proceedings of the AAAI Conference on Artificial Intelligence, AAAI, volume 33, pages 4926-4933 (CORE Ranked A*), 2019. AAAI Press
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CFOND: Consensus Factorization for Co-Clustering Networked Data. Guo, T.; Pan, S.; Zhu, X.; and Zhang, C. IEEE Transactions on Knowledge and Data Engineering (TKDE), 31(4): 706-719. 2019.
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Graph wavenet for deep spatial-temporal graph modeling. Wu, Z.; Pan, S.; Long, G.; Jiang, J.; and Zhang, C. In IJCAI International Joint Conference on Artificial Intelligence, volume 2019-Augus, pages 1907-1913, 2019.
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Attributed graph clustering: A deep attentional embedding approach. Wang, C.; Pan, S.; Hu, R.; Long, G.; Jiang, J.; and Zhang, C. In IJCAI International Joint Conference on Artificial Intelligence, volume 2019-Augus, pages 3670-3676, 2019.
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Relation structure-aware heterogeneous graph neural network. Zhu, S.; Zhou, C.; Pan, S.; Zhu, X.; and Wang, B. In Proceedings - IEEE International Conference on Data Mining, ICDM, volume 2019-Novem, pages 1534-1539 (CORE Ranked A*), 2019.
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Domain-adversarial graph neural networks for text classification. Wu, M.; Pan, S.; Zhu, X.; Zhou, C.; and Pan, L. In Proceedings - IEEE International Conference on Data Mining, ICDM, volume 2019-Novem, pages 648-657 (CORE Ranked A*), 2019.
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Cost-Sensitive Parallel Learning Framework for Insurance Intelligence Operation. Jiang, X.; Pan, S.; Long, G.; Xiong, F.; Jiang, J.; and Zhang, C. IEEE Transactions on Industrial Electronics (TIE), 66(12): 9713-9723. 2019.
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  2018 (9)
Active discriminative network representation learning. Gao, L.; Yang, H.; Zhou, C.; Wu, J.; Pan, S.; and Hu, Y. In IJCAI International Joint Conference on Artificial Intelligence, IJCAI, volume 2018-July, pages 2142-2148 (CORE Ranked A*), 7 2018. International Joint Conferences on Artificial Intelligence Organization
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Discrete network embedding. Shen, X.; Pan, S.; Liu, W.; Ong, Y., S.; and Sun, Q., S. In IJCAI International Joint Conference on Artificial Intelligence, IJCAI, volume 2018-July, pages 3549-3555 (CORE Ranked A*), 7 2018. International Joint Conferences on Artificial Intelligence Organization
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Adversarially regularized graph autoencoder for graph embedding. Pan, S.; Hu, R.; Long, G.; Jiang, J.; Yao, L.; and Zhang, C. In IJCAI International Joint Conference on Artificial Intelligence, IJCAI, volume 2018-July, pages 2609-2615 (CORE Ranked A*), 7 2018. International Joint Conferences on Artificial Intelligence Organization
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Binarized attributed network embedding. Yang, H.; Pan, S.; Zhang, P.; Chen, L.; Lian, D.; and Zhang, C. In Proceedings - IEEE International Conference on Data Mining, ICDM, volume 2018-Novem, pages 1476-1481 (CORE Ranked A*), 2018. IEEE Computer Society
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DISAN: Directional self-attention network for RnN/CNN-free language understanding. Shen, T.; Jiang, J.; Zhou, T.; Pan, S.; Long, G.; and Zhang, C. In McIlraith, S., A.; and Weinberger, K., Q., editor(s), 32nd AAAI Conference on Artificial Intelligence, AAAI 2018, pages 5446-5455, 2018. AAAI Press
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Multiple structure-view learning for graph classification. Wu, J.; Pan, S.; Zhu, X.; Zhang, C.; and Yu, P., S. IEEE Transactions on Neural Networks and Learning Systems (TNNLS), 29(7): 3236-3251. 2018.
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Hashing for Adaptive Real-Time Graph Stream Classification with Concept Drifts. Chi, L.; Li, B.; Zhu, X.; Pan, S.; and Chen, L. IEEE Transactions on Cybernetics (TCYB), 48(5): 1591-1604. 2018.
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Multi-instance learning with discriminative bag mapping. Wu, J.; Pan, S.; Zhu, X.; Zhang, C.; and Wu, X. IEEE Transactions on Knowledge and Data Engineering (TKDE), 30(6): 1065-1080. 2018.
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A Three-Layered Mutually Reinforced Model for Personalized Citation Recommendation. Cai, X.; Han, J.; Li, W.; Zhang, R.; Pan, S.; and Yang, L. IEEE Transactions on Neural Networks and Learning Systems (TNNLS), 29(12): 6026-6037. 2018.
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  2017 (3)
Positive and Unlabeled Multi-Graph Learning. Wu, J.; Pan, S.; Zhu, X.; Zhang, C.; and Wu, X. IEEE Transactions on Cybernetics (TCYB), 47(4): 818-829. 2017.
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Task Sensitive Feature Exploration and Learning for Multitask Graph Classification. Pan, S.; Wu, J.; Zhu, X.; Long, G.; and Zhang, C. IEEE Transactions on Cybernetics (TCYB), 47(3): 744-758. 2017.
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Exploiting Attribute Correlations: A Novel Trace Lasso-Based Weakly Supervised Dictionary Learning Method. Wu, L.; Wang, Y.; and Pan, S. IEEE Transactions on Cybernetics (TCYB), 47(12): 4497-4508. 2017.
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  2016 (4)
Joint Structure Feature Exploration and Regularization for Multi-Task Graph Classification. Pan, S.; Wu, J.; Zhu, X.; Zhang, C.; and Yu, P., S. IEEE Transactions on Knowledge and Data Engineering (TKDE), 28(3): 715-728. 3 2016.
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SODE: Self-Adaptive One-Dependence Estimators for classification. Wu, J.; Pan, S.; Zhu, X.; Zhang, P.; and Zhang, C. Pattern Recognition, 51: 358-377. 3 2016.
SODE: Self-Adaptive One-Dependence Estimators for classification [link]Website   doi   link   bibtex   abstract  
Tri-party deep network representation. Pan, S.; Wu, J.; Zhu, X.; Zhang, C.; and Wang, Y. In IJCAI International Joint Conference on Artificial Intelligence, volume 2016-Janua, pages 1895-1901, 2016. AAAI
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Iterative views agreement: An iterative low-rank based structured optimization method to multi-view spectral clustering. Wang, Y.; Wenjie, Z.; Wu, L.; Lin, X.; Fang, M.; and Pan, S. In IJCAI International Joint Conference on Artificial Intelligence, volume 2016-Janua, 2016.
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  2015 (5)
CogBoost: Boosting for Fast Cost-Sensitive Graph Classification. Pan, S.; Wu, J.; and Zhu, X. IEEE Transactions on Knowledge and Data Engineering (TKDE), 27(11): 2933-2946. 11 2015.
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Finding the best not the most: Regularized loss minimization subgraph selection for graph classification. Pan, S.; Wu, J.; Zhu, X.; Long, G.; and Zhang, C. Pattern Recognition, 48(11): 3783-3796. 11 2015.
Finding the best not the most: Regularized loss minimization subgraph selection for graph classification [link]Website   doi   link   bibtex   abstract  
Graph ensemble boosting for imbalanced noisy graph stream classification. Pan, S.; Wu, J.; Zhu, X.; and Zhang, C. IEEE Transactions on Cybernetics (TCYB), 45(5): 940-954. 5 2015.
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Boosting for multi-graph classification. Wu, J.; Pan, S.; Zhu, X.; and Cai, Z. IEEE Transactions on Cybernetics (TCYB), 45(3): 430-43. 3 2015.
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Multi-graph-view learning for complicated object classification. Wu, J.; Pan, S.; Zhu, X.; Cai, Z.; and Zhang, C. In IJCAI International Joint Conference on Artificial Intelligence, volume 2015-Janua, pages 3953-3959 (CORE Ranked A*), 2015. International Joint Conferences on Artificial Intelligence
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  2014 (1)
Multi-Graph-View Learning for Graph Classification. Wu, J.; Hong, Z.; Pan, S.; Zhu, X.; and Zhang, C. In International Conference on Data Mining, pages 590-599, 2014.
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  2013 (2)
Graph classification with imbalanced class distributions and noise. Pan, S.; and Zhu, X. In IJCAI International Joint Conference on Artificial Intelligence, pages 1586-1592, 2013.
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Graph stream classification using labeled and unlabeled graphs. Pan, S.; Zhu, X.; Zhang, C.; and Yu, P., S. In Proceedings - International Conference on Data Engineering, pages 398-409, 2013.
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