{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,14]],"date-time":"2026-04-14T15:50:09Z","timestamp":1776181809154,"version":"3.50.1"},"publisher-location":"New York, NY, USA","reference-count":51,"publisher":"ACM","license":[{"start":{"date-parts":[[2020,10,19]],"date-time":"2020-10-19T00:00:00Z","timestamp":1603065600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"name":"NSF","award":["III-1526499"],"award-info":[{"award-number":["III-1526499"]}]},{"name":"NSF","award":["III-1909323"],"award-info":[{"award-number":["III-1909323"]}]},{"name":"NSF","award":["III-1763325"],"award-info":[{"award-number":["III-1763325"]}]},{"name":"NSF","award":["CNS-1930941"],"award-info":[{"award-number":["CNS-1930941"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2020,10,19]]},"DOI":"10.1145\/3340531.3411903","type":"proceedings-article","created":{"date-parts":[[2020,10,19]],"date-time":"2020-10-19T05:31:05Z","timestamp":1603085465000},"page":"315-324","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":472,"title":["Enhancing Graph Neural Network-based Fraud Detectors against Camouflaged Fraudsters"],"prefix":"10.1145","author":[{"given":"Yingtong","family":"Dou","sequence":"first","affiliation":[{"name":"University of Illinois at Chicago, Chicago, IL, USA"}]},{"given":"Zhiwei","family":"Liu","sequence":"additional","affiliation":[{"name":"University of Illinois at Chicago, Chicago, IL, USA"}]},{"given":"Li","family":"Sun","sequence":"additional","affiliation":[{"name":"Beijing University of Posts and Telecommunications, Beijing, China"}]},{"given":"Yutong","family":"Deng","sequence":"additional","affiliation":[{"name":"Beijing University of Posts and Telecommunications, Beijing, China"}]},{"given":"Hao","family":"Peng","sequence":"additional","affiliation":[{"name":"Beihang University, Beijing, China"}]},{"given":"Philip S.","family":"Yu","sequence":"additional","affiliation":[{"name":"University of Illinois at Chicago, Chicago, IL, USA"}]}],"member":"320","published-online":{"date-parts":[[2020,10,19]]},"reference":[{"key":"e_1_3_2_2_1_1","doi-asserted-by":"crossref","unstructured":"L. Akoglu H. Tong and D. Koutra. 2015. Graph based anomaly detection and description: a survey. Data mining and knowledge discovery (2015).  L. Akoglu H. Tong and D. Koutra. 2015. Graph based anomaly detection and description: a survey. Data mining and knowledge discovery (2015).","DOI":"10.1007\/s10618-014-0365-y"},{"key":"e_1_3_2_2_2_1","volume-title":"Faux: Graph-Based Early Detection of Fake Accounts on Social Networks. In WWW.","author":"Breuer A.","year":"2020","unstructured":"A. Breuer , R. Eilat , and U. Weinsberg . 2020 . Friend or Faux: Graph-Based Early Detection of Fake Accounts on Social Networks. In WWW. A. Breuer, R. Eilat, and U. Weinsberg. 2020. Friend or Faux: Graph-Based Early Detection of Fake Accounts on Social Networks. In WWW."},{"key":"e_1_3_2_2_3_1","doi-asserted-by":"crossref","unstructured":"D. Chen Y. Lin Wei Li Peng Li J. Zhou and Xu Sun. 2020 a. Measuring and Relieving the Over-smoothing Problem for Graph Neural Networks from the Topological View. In AAAI.  D. Chen Y. Lin Wei Li Peng Li J. Zhou and Xu Sun. 2020 a. Measuring and Relieving the Over-smoothing Problem for Graph Neural Networks from the Topological View. In AAAI.","DOI":"10.1609\/aaai.v34i04.5747"},{"key":"e_1_3_2_2_4_1","unstructured":"H. Chen L. Wang S. Wang D. Luo W. Huang and Z. Li. 2019. Label Aware Graph Convolutional Network--Not All Edges Deserve Your Attention. arXiv preprint arXiv:1907.04707 (2019).  H. Chen L. Wang S. Wang D. Luo W. Huang and Z. Li. 2019. Label Aware Graph Convolutional Network--Not All Edges Deserve Your Attention. arXiv preprint arXiv:1907.04707 (2019)."},{"key":"e_1_3_2_2_5_1","unstructured":"J. Chen T. Ma and C. Xiao. 2018. Fastgcn: fast learning with graph convolutional networks via importance sampling. In ICLR.  J. Chen T. Ma and C. Xiao. 2018. Fastgcn: fast learning with graph convolutional networks via importance sampling. In ICLR."},{"key":"e_1_3_2_2_6_1","volume-title":"Deep Iterative and Adaptive Learning for Graph Neural Networks. AAAI Workshops","author":"Chen Y.","year":"2020","unstructured":"Y. Chen , L. Wu , and M. J. Zaki . 2020 b . Deep Iterative and Adaptive Learning for Graph Neural Networks. AAAI Workshops ( 2020 ). Y. Chen, L. Wu, and M. J. Zaki. 2020 b. Deep Iterative and Adaptive Learning for Graph Neural Networks. AAAI Workshops (2020)."},{"key":"e_1_3_2_2_7_1","doi-asserted-by":"crossref","unstructured":"S. Dhawan S.C.R. Gangireddy S. Kumar and T. Chakraborty. 2019. Spotting Collusive Behaviour of Online Fraud Groups in Customer Reviews. In IJCAI.  S. Dhawan S.C.R. Gangireddy S. Kumar and T. Chakraborty. 2019. Spotting Collusive Behaviour of Online Fraud Groups in Customer Reviews. In IJCAI.","DOI":"10.24963\/ijcai.2019\/35"},{"key":"e_1_3_2_2_8_1","doi-asserted-by":"crossref","unstructured":"Y. Dou G. Ma P. S. Yu and S. Xie. 2020. Robust Spammer Detection by Nash Reinforcement Learning. In KDD.  Y. Dou G. Ma P. S. Yu and S. Xie. 2020. Robust Spammer Detection by Nash Reinforcement Learning. In KDD.","DOI":"10.1145\/3394486.3403135"},{"key":"e_1_3_2_2_9_1","unstructured":"L. Franceschi M. Niepert M. Pontil and X. He. 2019. Learning discrete structures for graph neural networks. In ICML.  L. Franceschi M. Niepert M. Pontil and X. He. 2019. Learning discrete structures for graph neural networks. In ICML."},{"key":"e_1_3_2_2_10_1","doi-asserted-by":"crossref","unstructured":"S. Ge G. Ma S. Xie and P. S. Yu. 2018. Securing behavior-based opinion spam detection. In IEEE Big Data.  S. Ge G. Ma S. Xie and P. S. Yu. 2018. Securing behavior-based opinion spam detection. In IEEE Big Data.","DOI":"10.1109\/BigData.2018.8622582"},{"key":"e_1_3_2_2_11_1","unstructured":"P. Goyal P. Doll\u00e1r R. Girshick P. Noordhuis L. Wesolowski A. Kyrola A. Tulloch Y. Jia and K. He. 2017. Accurate large minibatch sgd: Training imagenet in 1 hour. arXiv preprint arXiv:1706.02677 (2017).  P. Goyal P. Doll\u00e1r R. Girshick P. Noordhuis L. Wesolowski A. Kyrola A. Tulloch Y. Jia and K. He. 2017. Accurate large minibatch sgd: Training imagenet in 1 hour. arXiv preprint arXiv:1706.02677 (2017)."},{"key":"e_1_3_2_2_12_1","volume":"201","author":"Hamilton W.","unstructured":"W. Hamilton , Z. Ying , and J. Leskovec. 201 7. Inductive representation learning on large graphs. In NeurIPS. W. Hamilton, Z. Ying, and J. Leskovec. 2017. Inductive representation learning on large graphs. In NeurIPS.","journal-title":"J. Leskovec."},{"key":"e_1_3_2_2_13_1","unstructured":"Y. Hou J. Zhang J. Cheng K. Ma R. T. B. Ma H. Chen and M. Yang. 2020. Measuring and Improving the Use of Graph Information in Graph Neural Networks. In ICLR.  Y. Hou J. Zhang J. Cheng K. Ma R. T. B. Ma H. Chen and M. Yang. 2020. Measuring and Improving the Use of Graph Information in Graph Neural Networks. In ICLR."},{"key":"e_1_3_2_2_14_1","doi-asserted-by":"crossref","unstructured":"M. Jiang P. Cui and C. Faloutsos. 2016. Suspicious behavior detection: Current trends and future directions. IEEE Intelligent Systems (2016).  M. Jiang P. Cui and C. Faloutsos. 2016. Suspicious behavior detection: Current trends and future directions. IEEE Intelligent Systems (2016).","DOI":"10.1109\/MIS.2016.5"},{"key":"e_1_3_2_2_15_1","volume":"201","author":"Kaghazgaran P.","unstructured":"P. Kaghazgaran , M. Alfifi , and J. Caverlee. 201 9. Wide-Ranging Review Manipulation Attacks: Model, Empirical Study, and Countermeasures. In CIKM. P. Kaghazgaran, M. Alfifi, and J. Caverlee. 2019. Wide-Ranging Review Manipulation Attacks: Model, Empirical Study, and Countermeasures. In CIKM.","journal-title":"J. Caverlee."},{"key":"e_1_3_2_2_16_1","doi-asserted-by":"crossref","unstructured":"P. Kaghazgaran J. Caverlee and A. Squicciarini. 2018. Combating crowdsourced review manipulators: A neighborhood-based approach. In WSDM.  P. Kaghazgaran J. Caverlee and A. Squicciarini. 2018. Combating crowdsourced review manipulators: A neighborhood-based approach. In WSDM.","DOI":"10.1145\/3159652.3159726"},{"key":"e_1_3_2_2_17_1","unstructured":"T.N. Kipf and M. Welling. 2017. Semi-supervised classification with graph convolutional networks. In ICLR.  T.N. Kipf and M. Welling. 2017. Semi-supervised classification with graph convolutional networks. In ICLR."},{"key":"e_1_3_2_2_18_1","doi-asserted-by":"crossref","unstructured":"S. Kumar B. Hooi D. Makhija M. Kumar C. Faloutsos and VS Subrahmanian. 2018. Rev2: Fraudulent user prediction in rating platforms. In WSDM.  S. Kumar B. Hooi D. Makhija M. Kumar C. Faloutsos and VS Subrahmanian. 2018. Rev2: Fraudulent user prediction in rating platforms. In WSDM.","DOI":"10.1145\/3159652.3159729"},{"key":"e_1_3_2_2_19_1","doi-asserted-by":"crossref","unstructured":"A. Li Z. Qin R. Liu Y. Yang and D. Li. 2019. Spam Review Detection with Graph Convolutional Networks. In CIKM.  A. Li Z. Qin R. Liu Y. Yang and D. Li. 2019. Spam Review Detection with Graph Convolutional Networks. In CIKM.","DOI":"10.1145\/3357384.3357820"},{"key":"e_1_3_2_2_20_1","volume":"201","author":"Li R.","unstructured":"R. Li , S. Wang , F. Zhu , and J. Huang. 201 8. Adaptive graph convolutional neural networks. In AAAI. R. Li, S. Wang, F. Zhu, and J. Huang. 2018. Adaptive graph convolutional neural networks. In AAAI.","journal-title":"J. Huang."},{"key":"e_1_3_2_2_21_1","doi-asserted-by":"crossref","unstructured":"X. Li S. Liu Z. Li X. Han C. Shi B. Hooi H. Huang and X. Cheng. 2020. FlowScope: Spotting Money Laundering Based on Graphs. In AAAI.  X. Li S. Liu Z. Li X. Han C. Shi B. Hooi H. Huang and X. Cheng. 2020. FlowScope: Spotting Money Laundering Based on Graphs. In AAAI.","DOI":"10.1609\/aaai.v34i04.5906"},{"key":"e_1_3_2_2_22_1","unstructured":"X. Liu J. Wu and Z. Zhou. 2008. Exploratory undersampling for class-imbalance learning. IEEE TSMC (2008).  X. Liu J. Wu and Z. Zhou. 2008. Exploratory undersampling for class-imbalance learning. IEEE TSMC (2008)."},{"key":"e_1_3_2_2_23_1","volume-title":"Geniepath: Graph neural networks with adaptive receptive paths. In AAAI.","author":"Liu Z.","year":"2019","unstructured":"Z. Liu , C. Chen , L. Li , J. Zhou , X. Li , L. Song , and Y. Qi . 2019 . Geniepath: Graph neural networks with adaptive receptive paths. In AAAI. Z. Liu, C. Chen, L. Li, J. Zhou, X. Li, L. Song, and Y. Qi. 2019. Geniepath: Graph neural networks with adaptive receptive paths. In AAAI."},{"key":"e_1_3_2_2_24_1","doi-asserted-by":"crossref","unstructured":"Z. Liu C. Chen X. Yang J. Zhou X. Li and L. Song. 2018. Heterogeneous Graph Neural Networks for Malicious Account Detection. In CIKM.  Z. Liu C. Chen X. Yang J. Zhou X. Li and L. Song. 2018. Heterogeneous Graph Neural Networks for Malicious Account Detection. In CIKM.","DOI":"10.1145\/3269206.3272010"},{"key":"e_1_3_2_2_25_1","doi-asserted-by":"crossref","unstructured":"Z. Liu Y. Dou P. S. Yu Y. Deng and H. Peng. 2020. Alleviating the Inconsistency Problem of Applying Graph Neural Network to Fraud Detection. SIGIR.  Z. Liu Y. Dou P. S. Yu Y. Deng and H. Peng. 2020. Alleviating the Inconsistency Problem of Applying Graph Neural Network to Fraud Detection. SIGIR.","DOI":"10.1145\/3397271.3401253"},{"key":"e_1_3_2_2_26_1","volume":"201","author":"McAuley J.","unstructured":"J. McAuley and J. Leskovec. 201 3. From amateurs to connoisseurs: modeling the evolution of user expertise through online reviews. In WWW. J. McAuley and J. Leskovec. 2013. From amateurs to connoisseurs: modeling the evolution of user expertise through online reviews. In WWW.","journal-title":"J. Leskovec."},{"key":"e_1_3_2_2_27_1","unstructured":"A. Mukherjee V. Venkataraman B. Liu and N. S. Glance. 2013. What Yelp Fake Review Filter Might Be Doing?. In ICWSM.  A. Mukherjee V. Venkataraman B. Liu and N. S. Glance. 2013. What Yelp Fake Review Filter Might Be Doing?. In ICWSM."},{"key":"e_1_3_2_2_28_1","doi-asserted-by":"crossref","unstructured":"H. Nilforoshan and N. Shah. 2019. SilceNDice: Mining Suspicious Multi-attribute Entity Groups with Multi-view Graphs. In DSAA.  H. Nilforoshan and N. Shah. 2019. SilceNDice: Mining Suspicious Multi-attribute Entity Groups with Multi-view Graphs. In DSAA.","DOI":"10.1109\/DSAA.2019.00050"},{"key":"e_1_3_2_2_29_1","doi-asserted-by":"crossref","unstructured":"S. Rayana and L. Akoglu. 2015. Collective Opinion Spam Detection: Bridging Review Networks and Metadata. In KDD.  S. Rayana and L. Akoglu. 2015. Collective Opinion Spam Detection: Bridging Review Networks and Metadata. In KDD.","DOI":"10.1145\/2783258.2783370"},{"key":"e_1_3_2_2_30_1","doi-asserted-by":"crossref","unstructured":"Y. Sahin S. Bulkan and E. Duman. 2013. A cost-sensitive decision tree approach for fraud detection. Expert Systems with Applications (2013).  Y. Sahin S. Bulkan and E. Duman. 2013. A cost-sensitive decision tree approach for fraud detection. Expert Systems with Applications (2013).","DOI":"10.1016\/j.eswa.2013.05.021"},{"key":"e_1_3_2_2_31_1","volume-title":"I. Titov, and M. Welling.","author":"Schlichtkrull M.","year":"2018","unstructured":"M. Schlichtkrull , T. N. Kipf , P. Bloem , R. Van Den Berg , I. Titov, and M. Welling. 2018 . Modeling relational data with graph convolutional networks. In ESWC. M. Schlichtkrull, T. N. Kipf, P. Bloem, R. Van Den Berg, I. Titov, and M. Welling. 2018. Modeling relational data with graph convolutional networks. In ESWC."},{"key":"e_1_3_2_2_32_1","volume-title":"KOLLECTOR: Detecting Fraudulent Activities on Mobile Devices Using Deep Learning","author":"Sun L.","year":"2020","unstructured":"L. Sun , B. Cao , J. Wang , W. Srisa-an, P. Yu , A. D. Leow , and S. Checkoway . 2020 . KOLLECTOR: Detecting Fraudulent Activities on Mobile Devices Using Deep Learning . IEEE TMC ( 2020). L. Sun, B. Cao, J. Wang, W. Srisa-an, P. Yu, A. D. Leow, and S. Checkoway. 2020. KOLLECTOR: Detecting Fraudulent Activities on Mobile Devices Using Deep Learning. IEEE TMC (2020)."},{"key":"e_1_3_2_2_33_1","volume-title":"Graph Data: A Survey. arXiv preprint arXiv:1812.10528","author":"Sun L.","year":"2018","unstructured":"L. Sun , Y. Dou , C. Yang , J. Wang , P. S. Yu , and B. Li . 2018 . Adversarial Attack and Defense on Graph Data: A Survey. arXiv preprint arXiv:1812.10528 (2018). L. Sun, Y. Dou, C. Yang, J. Wang, P. S. Yu, and B. Li. 2018. Adversarial Attack and Defense on Graph Data: A Survey. arXiv preprint arXiv:1812.10528 (2018)."},{"key":"e_1_3_2_2_34_1","unstructured":"P. Velivc kovi\u0107 G. Cucurull A. Casanova A. Romero P. Lio and Y. Bengio. 2017. Graph attention networks. In ICLR.  P. Velivc kovi\u0107 G. Cucurull A. Casanova A. Romero P. Lio and Y. Bengio. 2017. Graph attention networks. In ICLR."},{"key":"e_1_3_2_2_35_1","volume":"201","author":"Verma V.","unstructured":"V. Verma , M. Qu , A. Lamb , Y. Bengio , J. Kannala , and J. Tang. 201 9. GraphMix: Regularized Training of Graph Neural Networks for Semi-Supervised Learning. arXiv preprint arXiv:1909.11715 (2019). V. Verma, M. Qu, A. Lamb, Y. Bengio, J. Kannala, and J. Tang. 2019. GraphMix: Regularized Training of Graph Neural Networks for Semi-Supervised Learning. arXiv preprint arXiv:1909.11715 (2019).","journal-title":"J. Tang."},{"key":"e_1_3_2_2_36_1","doi-asserted-by":"crossref","unstructured":"J. Vermorel and M. Mohri. 2005. Multi-armed bandit algorithms and empirical evaluation. In ECML.  J. Vermorel and M. Mohri. 2005. Multi-armed bandit algorithms and empirical evaluation. In ECML.","DOI":"10.1007\/11564096_42"},{"key":"e_1_3_2_2_37_1","doi-asserted-by":"crossref","unstructured":"D. Wang J. Lin P. Cui Q. Jia Z. Wang Y. Fang Q. Yu J. Zhou S. Yang and Y. Qi. 2019 a. A Semi-supervised Graph Attentive Network for Fraud Detection. In ICDM.  D. Wang J. Lin P. Cui Q. Jia Z. Wang Y. Fang Q. Yu J. Zhou S. Yang and Y. Qi. 2019 a. A Semi-supervised Graph Attentive Network for Fraud Detection. In ICDM.","DOI":"10.1109\/ICDM.2019.00070"},{"key":"e_1_3_2_2_38_1","volume":"201","author":"Wang H.","unstructured":"H. Wang , C. Zhou , J. Wu , W. Dang , X. Zhu , and J. Wang. 201 8. Deep structure learning for fraud detection. In ICDM. H. Wang, C. Zhou, J. Wu, W. Dang, X. Zhu, and J. Wang. 2018. Deep structure learning for fraud detection. In ICDM.","journal-title":"J. Wang."},{"key":"e_1_3_2_2_39_1","volume-title":"WWW Workshops.","author":"Wang J.","unstructured":"J. Wang , R. Wen , C. Wu , Y. Huang , and J. Xiong . 2019 b. FdGars: Fraudster Detection via Graph Convolutional Networks in Online App Review System . In WWW Workshops. J. Wang, R. Wen, C. Wu, Y. Huang, and J. Xiong. 2019 b. FdGars: Fraudster Detection via Graph Convolutional Networks in Online App Review System. In WWW Workshops."},{"key":"e_1_3_2_2_40_1","volume-title":"KDD Workshops","author":"Weber M.","year":"2019","unstructured":"M. Weber , G. Domeniconi , J. Chen , D. K. I. Weidele , C. Bellei , T. Robinson , and C. E. Leiserson . 2019. Anti-money laundering in bitcoin: Experimenting with graph convolutional networks for financial forensics . KDD Workshops ( 2019 ). M. Weber, G. Domeniconi, J. Chen, D. K. I. Weidele, C. Bellei, T. Robinson, and C. E. Leiserson. 2019. Anti-money laundering in bitcoin: Experimenting with graph convolutional networks for financial forensics. KDD Workshops (2019)."},{"key":"e_1_3_2_2_41_1","volume":"202","author":"Wen R.","unstructured":"R. Wen , J. Wang , C. Wu , and J. Xiong. 202 0. ASA: Adversary Situation Awareness via Heterogeneous Graph Convolutional Networks. In WWW Workshops. R. Wen, J. Wang, C. Wu, and J. Xiong. 2020. ASA: Adversary Situation Awareness via Heterogeneous Graph Convolutional Networks. In WWW Workshops.","journal-title":"J. Xiong."},{"key":"e_1_3_2_2_42_1","unstructured":"Z. Wu S. Pan F. Chen G. Long C. Zhang and P. S. Yu. 2020. A comprehensive survey on graph neural networks. IEEE TNNLS (2020).  Z. Wu S. Pan F. Chen G. Long C. Zhang and P. S. Yu. 2020. A comprehensive survey on graph neural networks. IEEE TNNLS (2020)."},{"key":"e_1_3_2_2_43_1","volume-title":"2020 b. Secure Network Release with Link Privacy. arXiv preprint arXiv:2005.00455","author":"Yang C.","year":"2020","unstructured":"C. Yang , H. Wang , L. Sun , and B. Li . 2020 b. Secure Network Release with Link Privacy. arXiv preprint arXiv:2005.00455 ( 2020 ). C. Yang, H. Wang, L. Sun, and B. Li. 2020 b. Secure Network Release with Link Privacy. arXiv preprint arXiv:2005.00455 (2020)."},{"key":"e_1_3_2_2_44_1","doi-asserted-by":"crossref","unstructured":"X. Yang Y. Lyu T. Tian Y. Liu Y. Liu and X. Zhang. 2020 a. Rumor Detection on Social Media with Graph Structured Adversarial Learning. In IJCAI.  X. Yang Y. Lyu T. Tian Y. Liu Y. Liu and X. Zhang. 2020 a. Rumor Detection on Social Media with Graph Structured Adversarial Learning. In IJCAI.","DOI":"10.24963\/ijcai.2020\/197"},{"key":"e_1_3_2_2_45_1","volume-title":"Unsupervised Anomaly Detection via Deep Metric Learning with End-to-End Optimization. arXiv preprint arXiv:2005.05865","author":"Yilmaz S. F","year":"2020","unstructured":"S. F Yilmaz and S. S Kozat . 2020. Unsupervised Anomaly Detection via Deep Metric Learning with End-to-End Optimization. arXiv preprint arXiv:2005.05865 ( 2020 ). S. F Yilmaz and S. S Kozat. 2020. Unsupervised Anomaly Detection via Deep Metric Learning with End-to-End Optimization. arXiv preprint arXiv:2005.05865 (2020)."},{"key":"e_1_3_2_2_46_1","volume-title":"Graphsaint: Graph sampling based inductive learning method. ICLR","author":"Zeng H.","year":"2020","unstructured":"H. Zeng , H. Zhou , A. Srivastava , R. Kannan , and V. Prasanna . 2020 . Graphsaint: Graph sampling based inductive learning method. ICLR (2020). H. Zeng, H. Zhou, A. Srivastava, R. Kannan, and V. Prasanna. 2020. Graphsaint: Graph sampling based inductive learning method. ICLR (2020)."},{"key":"e_1_3_2_2_47_1","doi-asserted-by":"crossref","unstructured":"S. Zhang H. Yin T. Chen Q. V. N. Hung Z. Huang and L. Cui. 2020. GCN-Based User Representation Learning for Unifying Robust Recommendation and Fraudster Detection. In SIGIR.  S. Zhang H. Yin T. Chen Q. V. N. Hung Z. Huang and L. Cui. 2020. GCN-Based User Representation Learning for Unifying Robust Recommendation and Fraudster Detection. In SIGIR.","DOI":"10.1145\/3397271.3401165"},{"key":"e_1_3_2_2_48_1","doi-asserted-by":"crossref","unstructured":"Y. Zhang Y. Fan Y. Ye L. Zhao and C. Shi. 2019. Key Player Identification in Underground Forums over Attributed Heterogeneous Information Network Embedding Framework. In CIKM.  Y. Zhang Y. Fan Y. Ye L. Zhao and C. Shi. 2019. Key Player Identification in Underground Forums over Attributed Heterogeneous Information Network Embedding Framework. In CIKM.","DOI":"10.1145\/3357384.3357876"},{"key":"e_1_3_2_2_49_1","doi-asserted-by":"crossref","unstructured":"H. Zheng M. Xue H. Lu S. Hao H. Zhu X. Liang and K. Ross. 2018. Smoke screener or straight shooter: Detecting elite sybil attacks in user-review social networks. NDSS (2018).  H. Zheng M. Xue H. Lu S. Hao H. Zhu X. Liang and K. Ross. 2018. Smoke screener or straight shooter: Detecting elite sybil attacks in user-review social networks. NDSS (2018).","DOI":"10.14722\/ndss.2018.23009"},{"key":"e_1_3_2_2_50_1","doi-asserted-by":"crossref","unstructured":"Q. Zhong Y. Liu X. Ao B. Hu J. Feng J. Tang and Q. He. 2020. Financial Defaulter Detection on Online Credit Payment via Multi-View Attributed Heterogeneous Information Network. In WWW.  Q. Zhong Y. Liu X. Ao B. Hu J. Feng J. Tang and Q. He. 2020. Financial Defaulter Detection on Online Credit Payment via Multi-View Attributed Heterogeneous Information Network. In WWW.","DOI":"10.1145\/3366423.3380159"},{"key":"e_1_3_2_2_51_1","unstructured":"D. Zou Z. Hu Y. Wang S. Jiang Y. Sun and Q. Gu. 2019. Layer-dependent importance sampling for training deep and large graph convolutional networks. In NeurIPS.  D. Zou Z. Hu Y. Wang S. Jiang Y. Sun and Q. Gu. 2019. Layer-dependent importance sampling for training deep and large graph convolutional networks. In NeurIPS."}],"event":{"name":"CIKM '20: The 29th ACM International Conference on Information and Knowledge Management","location":"Virtual Event Ireland","acronym":"CIKM '20","sponsor":["SIGWEB ACM Special Interest Group on Hypertext, Hypermedia, and Web","SIGIR ACM Special Interest Group on Information Retrieval"]},"container-title":["Proceedings of the 29th ACM International Conference on Information & Knowledge Management"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3340531.3411903","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3340531.3411903","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T22:01:22Z","timestamp":1750197682000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3340531.3411903"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,10,19]]},"references-count":51,"alternative-id":["10.1145\/3340531.3411903","10.1145\/3340531"],"URL":"https:\/\/doi.org\/10.1145\/3340531.3411903","relation":{},"subject":[],"published":{"date-parts":[[2020,10,19]]},"assertion":[{"value":"2020-10-19","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}