{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,16]],"date-time":"2026-07-16T05:15:28Z","timestamp":1784178928968,"version":"3.55.0"},"publisher-location":"New York, NY, USA","reference-count":52,"publisher":"ACM","license":[{"start":{"date-parts":[[2022,4,25]],"date-time":"2022-04-25T00:00:00Z","timestamp":1650844800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2022,4,25]]},"DOI":"10.1145\/3485447.3512178","type":"proceedings-article","created":{"date-parts":[[2022,4,25]],"date-time":"2022-04-25T05:13:07Z","timestamp":1650863587000},"page":"1311-1321","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":97,"title":["AUC-oriented Graph Neural Network for Fraud Detection"],"prefix":"10.1145","author":[{"given":"Mengda","family":"Huang","sequence":"first","affiliation":[{"name":"Key Lab of Intelligent Information Processing of Chinese Academy of Sciences (CAS), Institute of Computing Technology, CAS, China and University of Chinese Academy of Sciences, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yang","family":"Liu","sequence":"additional","affiliation":[{"name":"Key Lab of Intelligent Information Processing of Chinese Academy of Sciences (CAS), Institute of Computing Technology, CAS, China and University of Chinese Academy of Sciences, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiang","family":"Ao","sequence":"additional","affiliation":[{"name":"Key Lab of Intelligent Information Processing of Chinese Academy of Sciences (CAS), Institute of Computing Technology, CAS, China and University of Chinese Academy of Sciences, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kuan","family":"Li","sequence":"additional","affiliation":[{"name":"Key Lab of Intelligent Information Processing of Chinese Academy of Sciences (CAS), Institute of Computing Technology, CAS, China and University of Chinese Academy of Sciences, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jianfeng","family":"Chi","sequence":"additional","affiliation":[{"name":"Alibaba Group, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jinghua","family":"Feng","sequence":"additional","affiliation":[{"name":"Alibaba Group, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hao","family":"Yang","sequence":"additional","affiliation":[{"name":"Alibaba Group, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qing","family":"He","sequence":"additional","affiliation":[{"name":"Key Lab of Intelligent Information Processing of Chinese Academy of Sciences (CAS), Institute of Computing Technology, CAS, China and University of Chinese Academy of Sciences, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2022,4,25]]},"reference":[{"key":"e_1_3_2_1_1_1","unstructured":"Marcin Andrychowicz Filip Wolski Alex Ray Jonas Schneider Rachel Fong Peter Welinder Bob McGrew Josh Tobin Pieter Abbeel and Wojciech Zaremba. 2017. Hindsight experience replay. In NeurIPS. 5055\u20135065."},{"key":"e_1_3_2_1_2_1","volume-title":"Convex optimization","author":"Boyd Stephen","unstructured":"Stephen Boyd, Stephen\u00a0P Boyd, and Lieven Vandenberghe. 2004. Convex optimization. Cambridge university press."},{"key":"e_1_3_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1613\/jair.953"},{"key":"e_1_3_2_1_4_1","doi-asserted-by":"crossref","unstructured":"Hao Chen Yue Xu Feiran Huang Zengde Deng Wenbing Huang Senzhang Wang Peng He and Zhoujun Li. 2020. Label-aware graph convolutional networks. In CIKM. 1977\u20131980.","DOI":"10.1145\/3340531.3412139"},{"key":"e_1_3_2_1_5_1","doi-asserted-by":"crossref","unstructured":"Jianfeng Chi Guanxiong Zeng Qiwei Zhong Ting Liang Jinghua Feng Xiang Ao and Jiayu Tang. 2020. Learning to Undersampling for Class Imbalanced Credit Risk Forecasting. In ICDM. 72\u201381.","DOI":"10.1109\/ICDM50108.2020.00016"},{"key":"e_1_3_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2017.08.035"},{"key":"e_1_3_2_1_7_1","unstructured":"Yingtong Dou Zhiwei Liu Li Sun Yutong Deng Hao Peng and Philip\u00a0S Yu. 2020. Enhancing graph neural network-based fraud detectors against camouflaged fraudsters. In CIKM. 315\u2013324."},{"key":"e_1_3_2_1_8_1","unstructured":"Wei Gao Rong Jin Shenghuo Zhu and Zhi-Hua Zhou. 2013. One-pass AUC optimization. In ICML. 906\u2013914."},{"key":"e_1_3_2_1_9_1","unstructured":"Wei Gao and Zhi-Hua Zhou. 2015. On the consistency of AUC pairwise optimization. In IJCAI. 939\u2013945."},{"key":"e_1_3_2_1_10_1","unstructured":"William\u00a0L Hamilton Rex Ying and Jure Leskovec. 2017. Inductive representation learning on large graphs. In NeurIPS. 1025\u20131035."},{"key":"e_1_3_2_1_11_1","volume-title":"ADASYN: Adaptive synthetic sampling approach for imbalanced learning. In IJCNN. 1322\u20131328.","author":"He Haibo","year":"2008","unstructured":"Haibo He, Yang Bai, Edwardo\u00a0A Garcia, and Shutao Li. 2008. ADASYN: Adaptive synthetic sampling approach for imbalanced learning. In IJCNN. 1322\u20131328."},{"key":"e_1_3_2_1_12_1","unstructured":"Thomas\u00a0N Kipf and Max Welling. 2017. Semi-supervised classification with graph convolutional networks. In ICLR."},{"key":"e_1_3_2_1_13_1","doi-asserted-by":"publisher","DOI":"10.1007\/s13748-016-0094-0"},{"key":"e_1_3_2_1_14_1","unstructured":"Tsung-Yi Lin Priya Goyal Ross Girshick Kaiming He and Piotr Doll\u00e1r. 2017. Focal loss for dense object detection. In ICCV. 2980\u20132988."},{"key":"e_1_3_2_1_15_1","doi-asserted-by":"crossref","unstructured":"Wangli Lin Li Sun Qiwei Zhong Can Liu Jinghua Feng Xiang Ao and Hao Yang. 2021. Online Credit Payment Fraud Detection via Structure-Aware Hierarchical Recurrent Neural Network. In IJCAI. 3670\u20133676.","DOI":"10.24963\/ijcai.2021\/505"},{"key":"e_1_3_2_1_16_1","doi-asserted-by":"crossref","unstructured":"Can Liu Li Sun Xiang Ao Jinghua Feng Qing He and Hao Yang. 2021. Intention-aware heterogeneous graph attention networks for fraud transactions detection. In SIGKDD. 3280\u20133288.","DOI":"10.1145\/3447548.3467142"},{"key":"e_1_3_2_1_17_1","doi-asserted-by":"crossref","unstructured":"Can Liu Qiwei Zhong Xiang Ao Li Sun Wangli Lin Jinghua Feng Qing He and Jiayu Tang. 2020. Fraud transactions detection via behavior tree with local intention calibration. In SIGKDD. 3035\u20133043.","DOI":"10.1145\/3394486.3403354"},{"key":"e_1_3_2_1_18_1","unstructured":"Mingrui Liu Zhuoning Yuan Yiming Ying and Tianbao Yang. 2019. Stochastic AUC Maximization with Deep Neural Networks. In ICLR."},{"key":"e_1_3_2_1_19_1","unstructured":"Mingrui Liu Xiaoxuan Zhang Zaiyi Chen Xiaoyu Wang and Tianbao Yang. 2018. Fast Stochastic AUC Maximization with O(1\/n)-Convergence Rate. In ICML. 3189\u20133197."},{"key":"e_1_3_2_1_20_1","doi-asserted-by":"crossref","unstructured":"Yang Liu Xiang Ao Zidi Qin Jianfeng Chi Jinghua Feng Hao Yang and Qing He. 2021. Pick and Choose: A GNN-based Imbalanced Learning Approach for Fraud Detection. In WWW. 3168\u20133177.","DOI":"10.1145\/3442381.3449989"},{"key":"e_1_3_2_1_21_1","doi-asserted-by":"crossref","unstructured":"Yang Liu Xiang Ao Qiwei Zhong Jinghua Feng Jiayu Tang and Qing He. 2020. Alike and Unlike: Resolving Class Imbalance Problem in Financial Credit Risk Assessment. In CIKM. 2125\u20132128.","DOI":"10.1145\/3340531.3412111"},{"key":"e_1_3_2_1_22_1","unstructured":"Zhiwei Liu Yingtong Dou Philip\u00a0S Yu Yutong Deng and Hao Peng. 2020. Alleviating the inconsistency problem of applying graph neural network to fraud detection. In SIGIR. 1569\u20131572."},{"key":"e_1_3_2_1_23_1","unstructured":"Zhining Liu Pengfei Wei Jing Jiang Wei Cao Jiang Bian and Yi Chang. 2020. MESA: boost ensemble imbalanced learning with meta-sampler. In NeurIPS. 14463\u201314474."},{"key":"e_1_3_2_1_24_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2019.02.023"},{"key":"e_1_3_2_1_25_1","doi-asserted-by":"crossref","unstructured":"Julian\u00a0John McAuley and Jure Leskovec. 2013. From amateurs to connoisseurs: modeling the evolution of user expertise through online reviews. In WWW. 897\u2013908.","DOI":"10.1145\/2488388.2488466"},{"key":"e_1_3_2_1_26_1","volume-title":"V-net: Fully convolutional neural networks for volumetric medical image segmentation. In 3DV. 565\u2013571.","author":"Milletari Fausto","year":"2016","unstructured":"Fausto Milletari, Nassir Navab, and Seyed-Ahmad Ahmadi. 2016. V-net: Fully convolutional neural networks for volumetric medical image segmentation. In 3DV. 565\u2013571."},{"key":"e_1_3_2_1_27_1","volume-title":"NeurIPS Workshop.","author":"Mnih Volodymyr","year":"2013","unstructured":"Volodymyr Mnih, Koray Kavukcuoglu, David Silver, Alex Graves, Ioannis Antonoglou, Daan Wierstra, and Martin Riedmiller. 2013. Playing atari with deep reinforcement learning. In NeurIPS Workshop."},{"key":"e_1_3_2_1_28_1","unstructured":"Michael Natole Yiming Ying and Siwei Lyu. 2018. Stochastic proximal algorithms for auc maximization. In ICML. 3710\u20133719."},{"key":"e_1_3_2_1_29_1","unstructured":"Adam Paszke Sam Gross Francisco Massa Adam Lerer James Bradbury Gregory Chanan Trevor Killeen Zeming Lin Natalia Gimelshein Luca Antiga 2019. PyTorch: an imperative style high-performance deep learning library. In NeurIPS. 8026\u20138037."},{"key":"e_1_3_2_1_30_1","doi-asserted-by":"crossref","unstructured":"Minlong Peng Qi Zhang Xiaoyu Xing Tao Gui Xuanjing Huang Yu-Gang Jiang Keyu Ding and Zhigang Chen. 2019. Trainable undersampling for class-imbalance learning. In AAAI. 4707\u20134714.","DOI":"10.1609\/aaai.v33i01.33014707"},{"key":"e_1_3_2_1_31_1","unstructured":"Mengye Ren Wenyuan Zeng Bin Yang and Raquel Urtasun. 2018. Learning to Reweight Examples for Robust Deep Learning. In ICML. 4334\u20134343."},{"key":"e_1_3_2_1_32_1","doi-asserted-by":"crossref","unstructured":"Patricia\u00a0Iglesias S\u00e1nchez Emmanuel M\u00fcller Fabian Laforet Fabian Keller and Klemens B\u00f6hm. 2013. Statistical selection of congruent subspaces for mining attributed graphs. In ICDM. 647\u2013656.","DOI":"10.1109\/ICDM.2013.88"},{"key":"e_1_3_2_1_33_1","doi-asserted-by":"publisher","DOI":"10.1109\/TNN.2008.2005605"},{"key":"e_1_3_2_1_34_1","doi-asserted-by":"crossref","unstructured":"Min Shi Yufei Tang Xingquan Zhu David Wilson and Jianxun Liu. 2021. Multi-class imbalanced graph convolutional network learning. In IJCAI. 2879\u20132885.","DOI":"10.24963\/ijcai.2020\/398"},{"key":"e_1_3_2_1_35_1","unstructured":"Richard\u00a0S Sutton and Andrew\u00a0G Barto. 2018. Reinforcement learning: An introduction."},{"key":"e_1_3_2_1_36_1","unstructured":"Richard\u00a0S Sutton David\u00a0A McAllester Satinder\u00a0P Singh Yishay Mansour 1999. Policy gradient methods for reinforcement learning with function approximation. In NeurIPS. 1057\u20131063."},{"key":"e_1_3_2_1_37_1","unstructured":"Petar Velickovic Guillem Cucurull Arantxa Casanova Adriana Romero Pietro Lio and Yoshua Bengio. 2018. Graph Attebtion Networks. In ICLR."},{"key":"e_1_3_2_1_38_1","doi-asserted-by":"crossref","unstructured":"Daixin Wang Jianbin Lin Peng Cui Quanhui Jia Zhen Wang Yanming Fang Quan Yu Jun Zhou Shuang Yang and Yuan Qi. 2019. A Semi-supervised Graph Attentive Network for Financial Fraud Detection. In ICDM. 598\u2013607.","DOI":"10.1109\/ICDM.2019.00070"},{"key":"e_1_3_2_1_39_1","volume-title":"Fdgars: Fraudster detection via graph convolutional networks in online app review system. In WWW. 310\u2013316.","author":"Wang Jianyu","year":"2019","unstructured":"Jianyu Wang, Rui Wen, Chunming Wu, Yu Huang, and Jian Xion. 2019. Fdgars: Fraudster detection via graph convolutional networks in online app review system. In WWW. 310\u2013316."},{"key":"e_1_3_2_1_40_1","unstructured":"Minjie Wang Da Zheng Zihao Ye Quan Gan Mufei Li Xiang Song Jinjing Zhou Chao Ma Lingfan Yu Yu Gai Tianjun Xiao Tong He George Karypis Jinyang Li and Zheng Zhang. 2019. Deep Graph Library: A Graph-Centric Highly-Performant Package for Graph Neural Networks. arXiv preprint arXiv:1909.01315(2019)."},{"key":"e_1_3_2_1_41_1","doi-asserted-by":"crossref","unstructured":"Yuguang Yan Mingkui Tan Yanwu Xu Jiezhang Cao Michael Ng Huaqing Min and Qingyao Wu. 2019. Oversampling for imbalanced data via optimal transport. In AAAI. 5605\u20135612.","DOI":"10.1609\/aaai.v33i01.33015605"},{"key":"e_1_3_2_1_42_1","volume-title":"Learning with Multiclass AUC: Theory and Algorithms","author":"Yang Zhiyong","year":"2021","unstructured":"Zhiyong Yang, Qianqian Xu, Shilong Bao, Xiaochun Cao, and Qingming Huang. 2021. Learning with Multiclass AUC: Theory and Algorithms. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021)."},{"key":"e_1_3_2_1_43_1","unstructured":"Zhiyong Yang Qianqian Xu Shilong Bao Yuan He Xiaochun Cao and Qingming Huang. 2021. When All We Need is a Piece of the Pie: A Generic Framework for Optimizing Two-way Partial AUC. In ICML. 11820\u201311829."},{"key":"e_1_3_2_1_44_1","doi-asserted-by":"crossref","unstructured":"Jian Yin Chunjing Gan Kaiqi Zhao Xuan Lin Zhe Quan and Zhi-Jie Wang. 2020. A Novel Model for Imbalanced Data Classification. In AAAI. 6680\u20136687.","DOI":"10.1609\/aaai.v34i04.6145"},{"key":"e_1_3_2_1_45_1","volume-title":"Gnnexplainer: Generating explanations for graph neural networks. In NeurIPS. 9240.","author":"Ying Rex","year":"2019","unstructured":"Rex Ying, Dylan Bourgeois, Jiaxuan You, Marinka Zitnik, and Jure Leskovec. 2019. Gnnexplainer: Generating explanations for graph neural networks. In NeurIPS. 9240."},{"key":"e_1_3_2_1_46_1","unstructured":"Yiming Ying Longyin Wen and Siwei Lyu. 2016. Stochastic online AUC maximization. In NeurIPS. 451\u2013459."},{"key":"e_1_3_2_1_47_1","unstructured":"Hanqing Zeng Hongkuan Zhou Ajitesh Srivastava Rajgopal Kannan and Viktor Prasanna. 2019. GraphSAINT: Graph Sampling Based Inductive Learning Method. In ICLR."},{"key":"e_1_3_2_1_48_1","volume-title":"FRAUDRE: Fraud Detection Dual-Resistant to Graph Inconsistency and Imbalance. In ICDM. 867\u2013876.","author":"Zhang Ge","year":"2021","unstructured":"Ge Zhang, Jia Wu, Jian Yang, Amin Beheshti, Shan Xue, Chuan Zhou, and Quan\u00a0Z Sheng. 2021. FRAUDRE: Fraud Detection Dual-Resistant to Graph Inconsistency and Imbalance. In ICDM. 867\u2013876."},{"key":"e_1_3_2_1_49_1","unstructured":"Peilin Zhao Steven\u00a0CH Hoi Rong Jin and Tianbao Yang. 2011. Online AUC maximization. In ICML. 233\u2013240."},{"key":"e_1_3_2_1_50_1","doi-asserted-by":"crossref","unstructured":"Qiwei Zhong Yang Liu Xiang Ao Binbin Hu Jinghua Feng Jiayu Tang and Qing He. 2020. Financial Defaulter Detection on Online Credit Payment via Multi-view Attributed Heterogeneous Information Network. In WWW. 785\u2013795.","DOI":"10.1145\/3366423.3380159"},{"key":"e_1_3_2_1_51_1","volume-title":"BBN: Bilateral-Branch Network with Cumulative Learning for Long-Tailed Visual Recognition. In CVPR. 9719\u20139728.","author":"Zhou Boyan","year":"2020","unstructured":"Boyan Zhou, Quan Cui, Xiu-Shen Wei, and Zhao-Min Chen. 2020. BBN: Bilateral-Branch Network with Cumulative Learning for Long-Tailed Visual Recognition. In CVPR. 9719\u20139728."},{"key":"e_1_3_2_1_52_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.xinn.2021.100176"}],"event":{"name":"WWW '22: The ACM Web Conference 2022","location":"Virtual Event, Lyon France","acronym":"WWW '22","sponsor":["SIGWEB ACM Special Interest Group on Hypertext, Hypermedia, and Web"]},"container-title":["Proceedings of the ACM Web Conference 2022"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3485447.3512178","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3485447.3512178","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T19:31:14Z","timestamp":1750188674000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3485447.3512178"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,4,25]]},"references-count":52,"alternative-id":["10.1145\/3485447.3512178","10.1145\/3485447"],"URL":"https:\/\/doi.org\/10.1145\/3485447.3512178","relation":{},"subject":[],"published":{"date-parts":[[2022,4,25]]},"assertion":[{"value":"2022-04-25","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}