research-article

Free access

Group-based Fraud Detection Network on e-Commerce Platforms

Authors:

Ying ZhangAuthors Info & Claims

KDD '23: Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining

Pages 5463 - 5475

https://doi.org/10.1145/3580305.3599836

Published: 04 August 2023 Publication History

Abstract

Along with the rapid technological and commercial innovation on the e-commerce platforms, there are an increasing number of frauds that bring great harm to these platforms. Many frauds are conducted by organized groups of fraudsters for higher efficiency and lower costs, which are also known as group-based frauds. Despite the high concealment and strong destructiveness of group-based fraud, there is no existing research work that can thoroughly exploit the information within the transaction networks of e-commerce platforms for group-based fraud detection. In this work, we analyze and summarize the characteristics of group-based frauds, based on which we propose a novel end-to-end semi-supervised Group-based Fraud Detection Network (GFDN) to support such fraud detection in real-world applications. Experimental results on large-scale e-commerce datasets from Taobao and Bitcoin trading datasets show the superior effectiveness and efficiency of our proposed model for group-based fraud detection on bipartite graphs.

Supplementary Material

MP4 File (adfp160-2min-promo.mp4)

Presentation video - short version

Download
23.06 MB

MP4 File (adfp160-20min-video.mp4)

Presentation video

Download
196.38 MB

References

[1]

2016. http://bhooi.github.io/code/camo.zip.

[2]

2018. https://github.com/hadisfr/biclique.

[3]

2020. https://github.com/boge-liu/alpha-beta-core.

[4]

2021. https://tianchi.aliyun.com/dataset/dataDetail?dataId=123862.

[5]

2021. https://tianchi.aliyun.com/competition/entrance/531925/introduction.

[6]

2021. https://tianchi.aliyun.com/forum/postDetail'spm=5176.12586969.1002.15. 333a7671eOe7Cf&postId=316896.

[7]

2022. https://github.com/yujianke100/GFDN.

[8]

2022. https://github.com/swig/swig.

[9]

Charu C Aggarwal, Yao Li, S Yu Philip, and Yuchen Zhao. 2017. On edge classification in networks with structure and content. In 2017 IEEE 33rd International Conference on Data Engineering (ICDE). IEEE, 187--190.

[10]

Leman Akoglu, Rishi Chandy, and Christos Faloutsos. 2013. Opinion Fraud Detection in Online Reviews by Network Effects. In Proceedings of the Seventh International Conference on Weblogs and Social Media, ICWSM 2013, Cambridge, Massachusetts, USA, July 8-11, 2013, Emre Kiciman, Nicole B. Ellison, Bernie Hogan, Paul Resnick, and Ian Soboroff (Eds.). The AAAI Press. http://www.aaai. org/ocs/index.php/ICWSM/ICWSM13/paper/view/5981

[11]

Gabriela Alexe, Sorin Alexe, Yves Crama, Stephan Foldes, Peter L Hammer, and Bruno Simeone. 2004. Consensus algorithms for the generation of all maximal bicliques. Discrete Applied Mathematics 145, 1 (2004), 11--21.

Digital Library

[12]

David M Allen. 1971. Mean square error of prediction as a criterion for selecting variables. Technometrics 13, 3 (1971), 469--475.

[13]

Pierre Baldi and Zhiqin Lu. 2012. Complex-valued autoencoders. Neural Networks 33 (2012), 136--147.

Digital Library

[14]

Alex Beutel, Wanhong Xu, Venkatesan Guruswami, Christopher Palow, and Christos Faloutsos. 2013. Copycatch: stopping group attacks by spotting lockstep behavior in social networks. In Proceedings of the 22nd international conference on World Wide Web. 119--130.

Digital Library

[15]

Deyu Bo, Xiao Wang, Chuan Shi, Meiqi Zhu, Emiao Lu, and Peng Cui. 2020. Structural deep clustering network. In Proceedings of The Web Conference 2020. 1400--1410.

Digital Library

[16]

Markus M Breunig, Hans-Peter Kriegel, Raymond T Ng, and Jörg Sander. 2000. LOF: identifying density-based local outliers. In Proceedings of the 2000 ACM SIGMOD international conference on Management of data. 93--104.

Digital Library

[17]

Jonathan Bryan and Pablo Moriano. 2021. Graph-Based Machine Learning Improves Just-in-Time Defect Prediction. arXiv preprint arXiv:2110.05371 (2021).

[18]

Jiangxia Cao, Xixun Lin, Shu Guo, Luchen Liu, Tingwen Liu, and BinWang. 2021. Bipartite graph embedding via mutual information maximization. In Proceedings of the 14th ACM International Conference on Web Search and Data Mining. 635--643.

Digital Library

[19]

Hao Chen, Zhong Huang, Yue Xu, Zengde Deng, Feiran Huang, Peng He, and Zhoujun Li. 2022. Neighbor enhanced graph convolutional networks for node classification and recommendation. Knowl. Based Syst. 246 (2022), 108594. https: //doi.org/10.1016/j.knosys.2022.108594

Digital Library

[20]

Yizong Cheng. 1995. Mean shift, mode seeking, and clustering. IEEE transactions on pattern analysis and machine intelligence 17, 8 (1995), 790--799.

[21]

Tyler Derr, Cassidy Johnson, Yi Chang, and Jiliang Tang. 2019. Balance in signed bipartite networks. In Proceedings of the 28th ACM International Conference on Information and Knowledge Management. 1221--1230.

Digital Library

[22]

Matthias Fey and Jan Eric Lenssen. 2019. Fast graph representation learning with PyTorch Geometric. arXiv preprint arXiv:1903.02428 (2019).

[23]

Santo Fortunato. 2010. Community detection in graphs. Physics reports 486, 3--5 (2010), 75--174.

[24]

Saptarshi Ghosh, Bimal Viswanath, Farshad Kooti, Naveen Kumar Sharma, Gautam Korlam, Fabrício Benevenuto, Niloy Ganguly, and P. Krishna Gummadi. 2012. Understanding and combating link farming in the twitter social network. In Proceedings of the 21st World Wide Web Conference 2012, WWW 2012, Lyon, France, April 16-20, 2012, Alain Mille, Fabien Gandon, Jacques Misselis, Michael Rabinovich, and Steffen Staab (Eds.). ACM, 61--70. https://doi.org/10.1145/2187836. 2187846

Digital Library

[25]

Aditya Grover and Jure Leskovec. 2016. node2vec: Scalable feature learning for networks. In Proceedings of the 22nd ACM SIGKDD international conference on Knowledge discovery and data mining. 855--864.

Digital Library

[26]

Roger Guimera, Marta Sales-Pardo, and Luís A Nunes Amaral. 2007. Module identification in bipartite and directed networks. Physical Review E 76, 3 (2007), 036102.

[27]

Will Hamilton, Zhitao Ying, and Jure Leskovec. 2017. Inductive representation learning on large graphs. Advances in neural information processing systems 30 (2017).

[28]

John A Hartigan and Manchek A Wong. 1979. Algorithm AS 136: A k-means clustering algorithm. Journal of the royal statistical society. series c (applied statistics) 28, 1 (1979), 100--108.

[29]

Fang He, Feiping Nie, Rong Wang, Haojie Hu, Weimin Jia, and Xuelong Li. 2020. Fast semi-supervised learning with optimal bipartite graph. IEEE Transactions on Knowledge and Data Engineering 33, 9 (2020), 3245--3257.

Digital Library

[30]

Xiangnan He, Kuan Deng, Xiang Wang, Yan Li, Yongdong Zhang, and Meng Wang. 2020. Lightgcn: Simplifying and powering graph convolution network for recommendation. In Proceedings of the 43rd International ACM SIGIR conference on research and development in Information Retrieval. 639--648.

Digital Library

[31]

Bryan Hooi, Neil Shah, Alex Beutel, Stephan Günnemann, Leman Akoglu, Mohit Kumar, Disha Makhija, and Christos Faloutsos. 2016. BIRDNEST: Bayesian Inference for Ratings-Fraud Detection. In Proceedings of the 2016 SIAM International Conference on Data Mining, Miami, Florida, USA, May 5-7, 2016, Sanjay Chawla Venkatasubramanian and Wagner Meira Jr. (Eds.). SIAM, 495--503. https://doi.org/10.1137/1.9781611974348.56

[32]

Bryan Hooi, Hyun Ah Song, Alex Beutel, Neil Shah, Kijung Shin, and Christos Faloutsos. 2016. Fraudar: Bounding graph fraud in the face of camouflage. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining. 895--904.

Digital Library

[33]

Benjamin Horne and Sibel Adali. 2017. This just in: Fake news packs a lot in title, uses simpler, repetitive content in text body, more similar to satire than real news. In Proceedings of the international AAAI conference on web and social media, Vol. 11. 759--766.

[34]

Junjie Huang, Huawei Shen, Qi Cao, Shuchang Tao, and Xueqi Cheng. 2021. Signed Bipartite Graph Neural Networks. In Proceedings of the 30th ACM International Conference on Information & Knowledge Management. 740--749.

Digital Library

[35]

Dmitry I Ignatov, Polina Ivanova, and Albina Zamaletdinova. 2018. Mixed integer programming for searching maximum quasi-bicliques. In International Conference on Network Analysis. Springer, 19--35.

[36]

Cuixia Jiang, Jun Zhu, and Qifa Xu. 2020. Dissecting click farming on the Taobao platform in China via PU learning and weighted logistic regression. Electronic Commerce Research (2020), 1--20.

[37]

Cuixia Jiang, Jun Zhu, and Qifa Xu. 2021. Which goods are most likely to be subject to click farming? An evidence from the Taobao platform. Electronic Commerce Research and Applications 50 (2021), 101107.

Digital Library

[38]

Fang Jin, Edward Dougherty, Parang Saraf, Yang Cao, and Naren Ramakrishnan. 2013. Epidemiological modeling of news and rumors on twitter. In Proceedings of the 7th workshop on social network mining and analysis. 1--9.

Digital Library

[39]

Parisa Kaghazgaran, James Caverlee, and Majid Alfifi. 2017. Behavioral Analysis of Review Fraud: Linking Malicious Crowdsourcing to Amazon and Beyond. In Proceedings of the Eleventh International Conference on Web and Social Media, ICWSM 2017, Montréal, Québec, Canada, May 15-18, 2017. AAAI Press, 560--563. https://aaai.org/ocs/index.php/ICWSM/ICWSM17/paper/view/15659

[40]

Diederik P. Kingma and Jimmy Ba. 2015. Adam: A Method for Stochastic Optimization. In 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings, Yoshua Bengio and Yann LeCun (Eds.). http://arxiv.org/abs/1412.6980

[41]

Thomas N Kipf and Max Welling. 2016. Variational graph auto-encoders. arXiv preprint arXiv:1611.07308 (2016).

[42]

Thomas N. Kipf and Max Welling. 2017. Semi-Supervised Classification with Graph Convolutional Networks. In 5th International Conference on Learning Representations, ICLR 2017, Toulon, France, April 24-26, 2017, Conference Track Proceedings. OpenReview.net. https://openreview.net/forum?id=SJU4ayYgl

[43]

Emmanouil Krasanakis, Symeon Papadopoulos, and Ioannis Kompatsiaris. 2022. p2pGNN: A Decentralized Graph Neural Network for Node Classification in Peer-to-Peer Networks. IEEE Access 10 (2022), 34755--34765. https://doi.org/10. 1109/ACCESS.2022.3159688

[44]

Wataru Kudo, Mao Nishiguchi, and Fujio Toriumi. 2020. GCNEXT: graph convolutional network with expanded balance theory for fraudulent user detection. Soc. Netw. Anal. Min. 10, 1 (2020), 85. https://doi.org/10.1007/s13278-020-00697-w

[45]

Solomon Kullback and Richard A Leibler. 1951. On information and sufficiency. The annals of mathematical statistics 22, 1 (1951), 79--86.

[46]

Srijan Kumar, Bryan Hooi, Disha Makhija, Mohit Kumar, Christos Faloutsos, and V. S. Subrahmanian. 2018. REV2: Fraudulent User Prediction in Rating Platforms. In Proceedings of the Eleventh ACM International Conference on Web Search and Data Mining, WSDM 2018, Marina Del Rey, CA, USA, February 5-9, 2018, Yi Chang, Chengxiang Zhai, Yan Liu, and Yoelle Maarek (Eds.). ACM, 333--341. https://doi.org/10.1145/3159652.3159729

Digital Library

[47]

Srijan Kumar, Robert West, and Jure Leskovec. 2016. Disinformation on the web: Impact, characteristics, and detection of wikipedia hoaxes. In Proceedings of the 25th international conference on World Wide Web. 591--602.

Digital Library

[48]

Sergei O Kuznetsov. 2001. On computing the size of a lattice and related decision problems. Order 18, 4 (2001), 313--321.

[49]

Geon Lee, Seonggoo Kang, and Joyce Jiyoung Whang. 2019. Hyperlink classification via structured graph embedding. In Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval. 1017--1020.

Digital Library

[50]

Fangyuan Lei, Xun Liu, Jianjian Jiang, Liping Liao, Jun Cai, and Huimin Zhao. 2022. Graph convolutional networks with higher-order pooling for semisupervised node classification. Concurr. Comput. Pract. Exp. 34, 16 (2022). https: //doi.org/10.1002/cpe.5695

[51]

Jingdong Li, Zhao Li, Jiaming Huang, Ji Zhang, Xiaoling Wang, Xingjian Lu, and Jingren Zhou. 2021. Large-scale Fake Click Detection for E-commerce Recommendation Systems. In 2021 IEEE 37th International Conference on Data Engineering (ICDE). IEEE, 2595--2606.

[52]

Neng Li, Suguo Du, Haizhong Zheng, Minhui Xue, and Haojin Zhu. 2018. Fake reviews tell no tales? dissecting click farming in content-generated social networks. China Communications 15, 4 (2018), 98--109.

[53]

Rong-Hua Li, Jeffrey Xu Yu, Xin Huang, and Hong Cheng. 2012. Robust Reputation-Based Ranking on Bipartite Rating Networks. In Proceedings of the Twelfth SIAM International Conference on Data Mining, Anaheim, California, USA, April 26-28, 2012. SIAM / Omnipress, 612--623. https://doi.org/10.1137/1. 9781611972825.53

[54]

Dawen Liang, Rahul G Krishnan, Matthew D Hoffman, and Tony Jebara. 2018. Variational autoencoders for collaborative filtering. In Proceedings of the 2018 world wide web conference. 689--698.

Digital Library

[55]

Tsung-Yi Lin, Priya Goyal, Ross Girshick, Kaiming He, and Piotr Dollár. 2017. Focal loss for dense object detection. In Proceedings of the IEEE international conference on computer vision. 2980--2988.

[56]

Boge Liu, Long Yuan, Xuemin Lin, Lu Qin,Wenjie Zhang, and Jingren Zhou. 2019. Efficient (α, β)-core computation: An index-based approach. In The World Wide Web Conference. 1130--1141.

Digital Library

[57]

Rui Liu, Runze Liu, Andrea Pugliese, and V. S. Subrahmanian. 2020. STARS: Defending against Sockpuppet-Based Targeted Attacks on Reviewing Systems. ACM Trans. Intell. Syst. Technol. 11, 5 (2020), 56:1--56:25. https://doi.org/10.1145/ 3397463

Digital Library

[58]

Xiaowen Liu, Jinyan Li, and Lusheng Wang. 2008. Quasi-bicliques: Complexity and binding pairs. In International Computing and Combinatorics Conference. Springer, 255--264.

Digital Library

[59]

Yajing Liu, Zhengya Sun, andWensheng Zhang. 2022. Improving Fraud Detection via Hierarchical Attention-based Graph Neural Network. CoRR abs/2202.06096 (2022). arXiv:2202.06096 https://arxiv.org/abs/2202.06096

[60]

Yadan Luo, Zi Huang, Hongxu Chen, Yang Yang, Hongzhi Yin, and Mahsa Baktashmotlagh. 2021. Interpretable signed link prediction with signed infomax hyperbolic graph. IEEE Transactions on Knowledge and Data Engineering (2021).

[61]

Bingqing Lyu, Lu Qin, Xuemin Lin, Ying Zhang, Zhengping Qian, and Jingren Zhou. 2020. Maximum biclique search at billion scale. Proceedings of the VLDB Endowment (2020).

Digital Library

[62]

Arpit Merchant, Ananth Mahadevan, and Michael Mathioudakis. 2022. Scalably Using Node Attributes and Graph Structure for Node Classification. Entropy 24, 7 (2022), 906. https://doi.org/10.3390/e24070906

[63]

Amanda J. Minnich, Nikan Chavoshi, Abdullah Mueen, Shuang Luan, and Michalis Faloutsos. 2015. TrueView: Harnessing the Power of Multiple Review Sites. In Proceedings of the 24th International Conference on World Wide Web, WWW 2015, Florence, Italy, May 18-22, 2015, Aldo Gangemi, Stefano Leonardi, and Alessandro Panconesi (Eds.). ACM, 787--797. https://doi.org/10.1145/2736277. 2741655

Digital Library

[64]

Abhinav Mishra and Arnab Bhattacharya. 2011. Finding the bias and prestige of nodes in networks based on trust scores. In Proceedings of the 20th International Conference on World Wide Web, WWW 2011, Hyderabad, India, March 28 - April 1, 2011, Sadagopan Srinivasan, Krithi Ramamritham, Arun Kumar, M. P. Ravindra, Elisa Bertino, and Ravi Kumar (Eds.). ACM, 567--576. https://doi.org/10.1145/ 1963405.1963485

Digital Library

[65]

Mark EJ Newman and Michelle Girvan. 2004. Finding and evaluating community structure in networks. Physical review E 69, 2 (2004), 026113.

[66]

Bryan Perozzi, Rami Al-Rfou, and Steven Skiena. 2014. Deepwalk: Online learning of social representations. In Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining. 701--710.

Digital Library

[67]

B Aditya Prakash, Ashwin Sridharan, Mukund Seshadri, Sridhar Machiraju, and Christos Faloutsos. 2010. Eigenspokes: Surprising patterns and scalable community chipping in large graphs. In Pacific-Asia Conference on Knowledge Discovery and Data Mining. Springer, 435--448.

Digital Library

[68]

Usha Nandini Raghavan, Réka Albert, and Soundar Kumara. 2007. Near linear time algorithm to detect community structures in large-scale networks. Physical review E 76, 3 (2007), 036106.

[69]

Yuxiang Ren, Hao Zhu, Jiawei Zhang, Peng Dai, and Liefeng Bo. 2021. EnsemFDet: An Ensemble Approach to Fraud Detection based on Bipartite Graph. In 37th IEEE International Conference on Data Engineering, ICDE 2021, Chania, Greece, April 19-22, 2021. IEEE, 2039--2044. https://doi.org/10.1109/ICDE51399.2021.00197

[70]

Chonggang Song, Qian Lin, Guohui Ling, Zongyi Zhang, Hongzhao Chen, Jun Liao, and Chuan Chen. 2020. LoCEC: local community-based edge classification in large online social networks. In 2020 IEEE 36th International Conference on Data Engineering (ICDE). IEEE, 1689--1700.

[71]

Laurens Van der Maaten and Geoffrey Hinton. 2008. Visualizing data using t-SNE. Journal of machine learning research 9, 11 (2008).

[72]

Petar Velickovic, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Liò, and Yoshua Bengio. 2018. Graph Attention Networks. (2018). https: //openreview.net/forum?id=rJXMpikCZ

[73]

Guan Wang, Sihong Xie, Bing Liu, and Philip S. Yu. 2011. Review Graph Based Online Store Review Spammer Detection. In 11th IEEE International Conference on Data Mining, ICDM 2011, Vancouver, BC, Canada, December 11-14, 2011, Diane J. Cook, Jian Pei, Wei Wang, Osmar R. Zaïane, and Xindong Wu (Eds.). IEEE Computer Society, 1242--1247. https://doi.org/10.1109/ICDM.2011.124

Digital Library

[74]

Guan Wang, Sihong Xie, Bing Liu, and Philip S. Yu. 2012. Identify Online Store Review Spammers via Social Review Graph. ACM Trans. Intell. Syst. Technol. 3, 4 (2012), 61:1--61:21. https://doi.org/10.1145/2337542.2337546

Digital Library

[75]

Jianyu Wang, Rui Wen, Chunming Wu, Yu Huang, and Jian Xiong. 2019. FdGars: Fraudster Detection via Graph Convolutional Networks in Online App Review System. In Companion of The 2019 World Wide Web Conference, WWW 2019, San Francisco, CA, USA, May 13-17, 2019, Sihem Amer-Yahia, Mohammad Mahdian, Ashish Goel, Geert-Jan Houben, Kristina Lerman, Julian J. McAuley, Ricardo Baeza-Yates, and Leila Zia (Eds.). ACM, 310--316. https://doi.org/10.1145/3308560. 3316586

Digital Library

[76]

Kai Wang, Xuemin Lin, Lu Qin, Wenjie Zhang, and Ying Zhang. 2020. Efficient bitruss decomposition for large-scale bipartite graphs. In 2020 IEEE 36th International Conference on Data Engineering (ICDE). IEEE, 661--672.

[77]

Lusheng Wang. 2013. Near optimal solutions for maximum quasi-bicliques. Journal of Combinatorial Optimization 25, 3 (2013), 481--497.

Digital Library

[78]

Guangyu Wu, Derek Greene, and Padraig Cunningham. 2010. Merging multiple criteria to identify suspicious reviews. In Proceedings of the 2010 ACM Conference on Recommender Systems, RecSys 2010, Barcelona, Spain, September 26-30, 2010, Xavier Amatriain, Marc Torrens, Paul Resnick, and Markus Zanker (Eds.). ACM, 241--244. https://doi.org/10.1145/1864708.1864757

Digital Library

[79]

Ke Wu, Song Yang, and Kenny Q Zhu. 2015. False rumors detection on sina weibo by propagation structures. In 2015 IEEE 31st international conference on data engineering. IEEE, 651--662.

[80]

Sihong Xie, Guan Wang, Shuyang Lin, and Philip S. Yu. 2012. Review spam detection via temporal pattern discovery. In The 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD '12, Beijing, China, August 12-16, 2012, Qiang Yang, Deepak Agarwal, and Jian Pei (Eds.). ACM, 823--831. https://doi.org/10.1145/2339530.2339662

Digital Library

[81]

Chang Xu and Jie Zhang. 2015. Towards Collusive Fraud Detection in Online Reviews. In 2015 IEEE International Conference on Data Mining, ICDM 2015, Atlantic City, NJ, USA, November 14-17, 2015, Charu C. Aggarwal, Zhi-Hua Zhou, Alexander Tuzhilin, Hui Xiong, and Xindong Wu (Eds.). IEEE Computer Society, 1051--1056. https://doi.org/10.1109/ICDM.2015.62

Digital Library

[82]

Chang Xu and Jie Zhang. 2017. Collusive Opinion Fraud Detection in Online Reviews: A Probabilistic Modeling Approach. ACM Trans. Web 11, 4 (2017), 25:1--25:28. https://doi.org/10.1145/3098859

Digital Library

[83]

Yixing Yang, Yixiang Fang, Maria E Orlowska, Wenjie Zhang, and Xuemin Lin. 2021. Efficient bi-triangle counting for large bipartite networks. Proceedings of the VLDB Endowment 14, 6 (2021), 984--996.

Digital Library

[84]

Kaiqiang Yu, Cheng Long, Shengxin Liu, and Da Yan. 2022. Efficient Algorithms for Maximal k-Biplex Enumeration. In Proceedings of the 2022 International Conference on Management of Data. 860--873.

Digital Library

[85]

Mengxi Yu, Ziyu Liu, Yuhang Tang, and Jianfeng Jiang. 2021. Recognition algorithm of e-commerce click farming based on K-means technology. In 2021 6th International Conference on Intelligent Computing and Signal Processing (ICSP). IEEE, 103--106.

[86]

Baoliang Zhang, Xiaoxin Guo, Zhenchuan Tu, and Jia Zhang. 2022. Graph alternate learning for robust graph neural networks in node classification. Neural Comput. Appl. 34, 11 (2022), 8723--8735. https://doi.org/10.1007/s00521-021-06863-1

Digital Library

[87]

Shijie Zhang, Hongzhi Yin, Tong Chen, Quoc Viet Hung Nguyen, Zi Huang, and Lizhen Cui. 2020. GCN-Based User Representation Learning for Unifying Robust Recommendation and Fraudster Detection. In Proceedings of the 43rd International ACM SIGIR conference on research and development in Information Retrieval, SIGIR 2020, Virtual Event, China, July 25-30, 2020, Jimmy X. Huang, Yi Chang, Xueqi Cheng, Jaap Kamps, Vanessa Murdock, Ji-Rong Wen, and Yiqun Liu (Eds.). ACM, 689--698. https://doi.org/10.1145/3397271.3401165

Digital Library

[88]

Xianhang Zhang, Hanchen Wang, Jianke Yu, Chen Chen, Xiaoyang Wang, and Wenjie Zhang. 2022. Bipartite graph capsule network. World Wide Web (2022), 1--20.

[89]

Xianhang Zhang, Hanchen Wang, Jianke Yu, Chen Chen, Xiaoyang Wang, and Wenjie Zhang. 2022. Polarity-based graph neural network for sign prediction in signed bipartite graphs. World Wide Web 25, 2 (2022), 471--487. https://doi.org/ 10.1007/s11280-022-01015-4

Digital Library

[90]

Yun Zhang, Charles A Phillips, Gary L Rogers, Erich J Baker, Elissa J Chesler, and Michael A Langston. 2014. On finding bicliques in bipartite graphs: a novel algorithm and its application to the integration of diverse biological data types. BMC bioinformatics 15, 1 (2014), 1--18.

[91]

Jie Zhao, Raymond YK Lau, Wenping Zhang, Kaihang Zhang, Xu Chen, and Deyu Tang. 2016. Extracting and reasoning about implicit behavioral evidences for detecting fraudulent online transactions in e-Commerce. Decision support systems 86 (2016), 109--121.

Cited By

Zhang ZAo XTessone CLiu GZhou MMao RLiao H(2025)Multiplex graph fusion network with reinforcement structure learning for fraud detection in online e-commerce platformsExpert Systems with Applications10.1016/j.eswa.2024.125598262(125598)Online publication date: Mar-2025
https://doi.org/10.1016/j.eswa.2024.125598
Wu XXiong KXiong YHe XZhang YJiao YZhang JBaeza-Yates RBonchi F(2024)ProCom: A Few-shot Targeted Community Detection AlgorithmProceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3637528.3671749(3414-3424)Online publication date: 25-Aug-2024
https://dl.acm.org/doi/10.1145/3637528.3671749
Xiao FCai SChen GJagadish HOoi BZhang MBaeza-Yates RBonchi F(2024)VecAug: Unveiling Camouflaged Frauds with Cohort Augmentation for Enhanced DetectionProceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3637528.3671527(6025-6036)Online publication date: 25-Aug-2024
https://dl.acm.org/doi/10.1145/3637528.3671527
Show More Cited By

Index Terms

Group-based Fraud Detection Network on e-Commerce Platforms
1. Information systems
  1. Information systems applications
    1. Data mining

Recommendations

Research on Credit Card Fraud Detection Model Based on Distance Sum
JCAI '09: Proceedings of the 2009 International Joint Conference on Artificial Intelligence

Along with increasing credit cards and growing trade volume in China, credit card fraud rises sharply. How to enhance the detection and prevention of credit card fraud becomes the focus of risk control of banks. This paper proposes a credit card fraud ...
Fraud detection and prevention in e-commerce: A systematic literature review
Abstract
The high volume of money involved in e-commerce transactions draws the attention of fraudsters, which makes fraud prevention and detection techniques of high importance. Current surveys and reviews on fraud systems focuses mainly on ...
Highlights
- This study reviews 64 articles on fraud detection and prevention for e-commerce.
What Happens Behind the Scene? Towards Fraud Community Detection in E-Commerce from Online to Offline
WWW '21: Companion Proceedings of the Web Conference 2021

Fraud behavior poses a severe threat to e-commerce platforms and anti-fraud systems have become indispensable infrastructure of these platforms. Recently, there have been a large number of fraud detection models proposed to monitor online purchasing ...

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences

KDD '23: Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining

August 2023

5996 pages

ISBN:9798400701030

DOI:10.1145/3580305

General Chairs:
Ambuj Singh
UC Santa Barbara, USA
,
Yizhou Sun
UC Los Angeles, USA
,
Program Chairs:
Leman Akoglu
Carnegie Mellon University, USA
,
Dimitrios Gunopulos
University of Athens, Greece
,
Xifeng Yan
UC Santa Barbara, USA
,
Ravi Kumar
Google, USA
,
Fatma Ozcan
Google, USA
,
Jieping Ye
Alibaba DAMO Academy

Copyright © 2023 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

Sponsors

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 04 August 2023

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article

Funding Sources

ARC
ZJNSF

Conference

KDD '23

Sponsor:

KDD '23: The 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining

August 6 - 10, 2023

CA, Long Beach, USA

Acceptance Rates

Overall Acceptance Rate 1,133 of 8,635 submissions, 13%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

13
Total Citations
View Citations
1,617
Total Downloads

Downloads (Last 12 months)1,137
Downloads (Last 6 weeks)99

Reflects downloads up to 10 Nov 2024

Other Metrics

View Author Metrics

Citations

Cited By

Zhang ZAo XTessone CLiu GZhou MMao RLiao H(2025)Multiplex graph fusion network with reinforcement structure learning for fraud detection in online e-commerce platformsExpert Systems with Applications10.1016/j.eswa.2024.125598262(125598)Online publication date: Mar-2025
https://doi.org/10.1016/j.eswa.2024.125598
Wu XXiong KXiong YHe XZhang YJiao YZhang JBaeza-Yates RBonchi F(2024)ProCom: A Few-shot Targeted Community Detection AlgorithmProceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3637528.3671749(3414-3424)Online publication date: 25-Aug-2024
https://dl.acm.org/doi/10.1145/3637528.3671749
Xiao FCai SChen GJagadish HOoi BZhang MBaeza-Yates RBonchi F(2024)VecAug: Unveiling Camouflaged Frauds with Cohort Augmentation for Enhanced DetectionProceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3637528.3671527(6025-6036)Online publication date: 25-Aug-2024
https://dl.acm.org/doi/10.1145/3637528.3671527
Chen YWang XChen CSerra ESpezzano F(2024)Hyperedge Importance Estimation via Identity-aware Hypergraph Attention NetworkProceedings of the 33rd ACM International Conference on Information and Knowledge Management10.1145/3627673.3679685(334-343)Online publication date: 21-Oct-2024
https://dl.acm.org/doi/10.1145/3627673.3679685
Yang SLi CChua TNgo CKa-Wei Lee RKumar RLauw H(2024)Detecting Illicit Food Factories from Chemical Declaration Data via Graph-aware Self-supervised Contrastive Anomaly RankingProceedings of the ACM Web Conference 202410.1145/3589334.3648138(4501-4511)Online publication date: 13-May-2024
https://dl.acm.org/doi/10.1145/3589334.3648138
Wang HWang KZhang WZhang Y(2024)Bipartite Graph Analytics: Current Techniques and Future Trends2024 IEEE 40th International Conference on Data Engineering (ICDE)10.1109/ICDE60146.2024.00405(01-07)Online publication date: 13-May-2024
https://doi.org/10.1109/ICDE60146.2024.00405
Hewageegana KHarischandra JSenanayake NDanansuriya GHapuarachchi KIllangarathne P(2024)A Survey on Graph Neural Networks for Fraud Detection in Ride Hailing Platforms2024 7th International Conference on Artificial Intelligence and Big Data (ICAIBD)10.1109/ICAIBD62003.2024.10604597(168-179)Online publication date: 24-May-2024
https://doi.org/10.1109/ICAIBD62003.2024.10604597
Ding YYang ZLi SChen LNing HHao KLiu Y(2024)FGAQ: Accelerating Graph Analytical Queries Using FPGAWeb and Big Data10.1007/978-981-97-7244-5_25(357-361)Online publication date: 28-Aug-2024
https://doi.org/10.1007/978-981-97-7244-5_25
Jiang YYu JXu ZChen CChow YZhang Y(2024)SBGMN: A Multi-view Sign Prediction Network for Bipartite GraphsWeb and Big Data10.1007/978-981-97-7238-4_6(81-96)Online publication date: 28-Aug-2024
https://doi.org/10.1007/978-981-97-7238-4_6
Kang LMu TNing X(2024)Spoofing Transaction Detection with Group Perceptual Enhanced Graph Neural NetworkMachine Learning and Knowledge Discovery in Databases. Applied Data Science Track10.1007/978-3-031-70378-2_7(106-122)Online publication date: 22-Aug-2024
https://doi.org/10.1007/978-3-031-70378-2_7
Show More Cited By

View Options

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Get Access

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

Media

Figures

Other

Tables

View Table of Contents