@inproceedings{wangSemisupervisedGraphAttentive2019,
  title = {A {{Semi-supervised Graph Attentive Network}} for {{Financial Fraud Detection}}},
  booktitle = {2019 {{IEEE International Conference}} on {{Data Mining}} ({{ICDM}})},
  author = {Wang, Daixin and Lin, Jianbin and Cui, Peng and Jia, Quanhui and Wang, Zhen and Fang, Yanming and Yu, Quan and Zhou, Jun and Yang, Shuang and Qi, Yuan},
  year = {2019},
  month = nov,
  eprint = {2003.01171},
  primaryclass = {cs, stat},
  pages = {598--607},
  doi = {10.1109/ICDM.2019.00070},
  urldate = {2022-11-04},
  abstract = {With the rapid growth of financial services, fraud detection has been a very important problem to guarantee a healthy environment for both users and providers. Conventional solutions for fraud detection mainly use some rule-based methods or distract some features manually to perform prediction. However, in financial services, users have rich interactions and they themselves always show multifaceted information. These data form a large multiview network, which is not fully exploited by conventional methods. Additionally, among the network, only very few of the users are labelled, which also poses a great challenge for only utilizing labeled data to achieve a satisfied performance on fraud detection.},
  archiveprefix = {arXiv},
  langid = {english},
  keywords = {-,Anomaly Detection,Computer Science - Cryptography and Security,Computer Science - Social and Information Networks,Statistics - Machine Learning},
  annotation = {GSCC: 0000241 \\
70 citations (Crossref) [2022-11-05]},
  file = {C:\Users\xyqye\Desktop\Data\Sync\study\zotero\attachments\2019\Wang et al_2019_A Semi-supervised Graph Attentive Network for Financial Fraud Detection.pdf}
}

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Conventional solutions for fraud detection mainly use some rule-based methods or distract some features manually to perform prediction. However, in financial services, users have rich interactions and they themselves always show multifaceted information. These data form a large multiview network, which is not fully exploited by conventional methods. Additionally, among the network, only very few of the users are labelled, which also poses a great challenge for only utilizing labeled data to achieve a satisfied performance on fraud detection.","archiveprefix":"arXiv","langid":"english","keywords":"-,Anomaly Detection,Computer Science - Cryptography and Security,Computer Science - Social and Information Networks,Statistics - Machine Learning","annotation":"GSCC: 0000241 \\\\ 70 citations (Crossref) [2022-11-05]","file":"C:\\Users\\xyqye\\Desktop\\Data§ync\\study\\zotero\\attachments\\2019\\Wang et al_2019_A Semi-supervised Graph Attentive Network for Financial Fraud Detection.pdf","bibtex":"@inproceedings{wangSemisupervisedGraphAttentive2019,\n title = {A {{Semi-supervised Graph Attentive Network}} for {{Financial Fraud Detection}}},\n booktitle = {2019 {{IEEE International Conference}} on {{Data Mining}} ({{ICDM}})},\n author = {Wang, Daixin and Lin, Jianbin and Cui, Peng and Jia, Quanhui and Wang, Zhen and Fang, Yanming and Yu, Quan and Zhou, Jun and Yang, Shuang and Qi, Yuan},\n year = {2019},\n month = nov,\n eprint = {2003.01171},\n primaryclass = {cs, stat},\n pages = {598--607},\n doi = {10.1109/ICDM.2019.00070},\n urldate = {2022-11-04},\n abstract = {With the rapid growth of financial services, fraud detection has been a very important problem to guarantee a healthy environment for both users and providers. Conventional solutions for fraud detection mainly use some rule-based methods or distract some features manually to perform prediction. However, in financial services, users have rich interactions and they themselves always show multifaceted information. These data form a large multiview network, which is not fully exploited by conventional methods. Additionally, among the network, only very few of the users are labelled, which also poses a great challenge for only utilizing labeled data to achieve a satisfied performance on fraud detection.},\n archiveprefix = {arXiv},\n langid = {english},\n keywords = {-,Anomaly Detection,Computer Science - Cryptography and Security,Computer Science - Social and Information Networks,Statistics - Machine Learning},\n annotation = {GSCC: 0000241 \\\\\n70 citations (Crossref) [2022-11-05]},\n file = {C:\\Users\\xyqye\\Desktop\\Data\\Sync\\study\\zotero\\attachments\\2019\\Wang et al_2019_A Semi-supervised Graph Attentive Network for Financial Fraud Detection.pdf}\n}\n\n","author_short":["Wang, D.","Lin, J.","Cui, P.","Jia, Q.","Wang, Z.","Fang, Y.","Yu, Q.","Zhou, J.","Yang, S.","Qi, Y."],"key":"wangSemisupervisedGraphAttentive2019","id":"wangSemisupervisedGraphAttentive2019","bibbaseid":"wang-lin-cui-jia-wang-fang-yu-zhou-etal-asemisupervisedgraphattentivenetworkforfinancialfrauddetection-2019","role":"author","urls":{},"keyword":["-","Anomaly Detection","Computer Science - Cryptography and Security","Computer Science - Social and Information Networks","Statistics - Machine Learning"],"metadata":{"authorlinks":{}},"downloads":0,"html":""},"bibtype":"inproceedings","biburl":"https://bibbase.org/network/files/4R2kjSopEaLRshhei","dataSources":["NrLCq59S7uEbLrQ4u"],"keywords":["-","anomaly detection","computer science - cryptography and security","computer science - social and information networks","statistics - machine learning"],"search_terms":["semi","supervised","graph","attentive","network","financial","fraud","detection","wang","lin","cui","jia","wang","fang","yu","zhou","yang","qi"],"title":"A Semi-supervised Graph Attentive Network for Financial Fraud Detection","year":2019}