{"type":"ScholarlyArticle","title":"Effective High-order Graph Representation Learning for Credit Card Fraud Detection","authors":[{"name":"Yao Zou","slug":"yao-zou","given":"Yao","family":"Zou","affiliation":"Department of Computer Science and Technology , Tongji University , Shanghai , China"},{"name":"Dawei Cheng","slug":"dawei-cheng","given":"Dawei","family":"Cheng","affiliation":"Department of Computer Science and Technology , Tongji University , Shanghai , China; Key Laboratory of Artificial Intelligence , Ministry of Education , Shanghai , China; Shanghai Artificial Intelligence Laboratory , Shanghai , China"}],"abstract":"Credit card fraud imposes significant costs on both cardholders and issuing banks. Fraudsters often disguise their crimes, such as using legitimate transactions through several benign users to bypass anti-fraud detection. Existing graph neural network (GNN) models struggle with learning features of camouflaged, indirect multi-hop transactions due to their inherent over-smoothing issues in deep multi-layer aggregation, presenting a major challenge in detecting disguised relationships. Therefore, in this paper, we propose a novel High-order Graph Representation Learning model (HOGRL) to avoid incorporating excessive noise during the multi-layer aggregation process. In particular, HOGRL learns different orders of \\emph{pure} representations directly from high-order transaction graphs. We realize this goal by effectively constructing high-order transaction graphs first and then learning the \\emph{pure} representations of each order so that the model could identify fraudsters' multi-hop indirect transactions via multi-layer \\emph{pure} feature learning. In addition, we introduce a mixture-of-expert attention mechanism to automatically determine the importance of different orders for jointly optimizing fraud detection performance. We conduct extensive experiments in both the open source and real-world datasets, the result demonstrates the significant improvements of our proposed HOGRL compared with state-of-the-art fraud detection baselines. HOGRL's superior performance also proves its effectiveness in addressing high-order fraud camouflage criminals.","doi":"10.24963/ijcai.2024/839","url":"https://ijcai.org/proceedings/2024/839","pdf":"https://ijcai.org/proceedings/2024/0839.pdf","markdown":"https://ijcai.org/proceedings/2024/839.md","bibtex":"https://ijcai.org/proceedings/2024/bibtex/839","volume":{"slug":"2024","title":"Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2024","url":"https://ijcai.org/proceedings/2024/","year":2024,"location":"Jeju, Korea","dates":"3-9 August 2024","editors":"Kate Larson","isbn":"978-1-956792-04-1","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2024"},"section":"AI for Good","track":"AI for Good","number":839,"pages":{"first":7581,"last":7589},"keywords":["Data Mining: General","Multidisciplinary Topics and Applications: General"],"videos":[],"references":[{"position":1,"raw":"[Bhattacharyya et al., 2011] Siddhartha Bhattacharyya, San- jeev Jha, Kurian Tharakunnel, and J Christopher Westland. Data mining for credit card fraud: A comparative study. Decision support systems, 50(3):602-613, 2011.","title":"Data mining for credit card fraud: A comparative study","authors":"Siddhartha Bhattacharyya, Sanjeev Jha, Kurian Tharakunnel, J Christopher, Westland","year":"2011","venue":"Decision support systems","doi":null,"url":null,"internal_url":null},{"position":2,"raw":"[Bo et al., 2021] Deyu Bo, Xiao Wang, Chuan Shi, and Huawei Shen. Beyond low-frequency information in graph convolutional networks. In Proceedings of the AAAI con- ference on artificial intelligence, volume 35, pages 3950- 3957, 2021.","title":"Beyond low-frequency information in graph convolutional networks","authors":"Deyu Bo, Xiao Wang, Chuan Shi, Huawei Shen","year":"2021","venue":"Proceedings of the AAAI conference on artificial intelligence","doi":null,"url":null,"internal_url":null},{"position":3,"raw":"[Burrell, 2016] Jenna Burrell. How the machine 'thinks': Understanding opacity in machine learning algorithms. Big data & society, 3(1):2053951715622512, 2016.","title":"How the machine 'thinks': Understanding opacity in machine learning algorithms","authors":"Jenna Burrell, Burrell","year":"2016","venue":"Big data & society","doi":null,"url":null,"internal_url":null},{"position":4,"raw":"[Cheng et al., 2020] Dawei Cheng, Xiaoyang Wang, Ying Zhang, and Liqing Zhang. Graph neural network for fraud detection via spatial-temporal attention. IEEE Transac- tions on Knowledge and Data Engineering, 34(8):3800- 3813, 2020.","title":"Graph neural network for fraud detection via spatial-temporal attention","authors":"Dawei Cheng, Xiaoyang Wang, Ying Zhang, Liqing Zhang","year":"2020","venue":"IEEE Transactions on Knowledge and Data Engineering","doi":null,"url":null,"internal_url":null},{"position":5,"raw":"[Cheng et al., 2023] Dawei Cheng, Yujia Ye, Sheng Xiang, Zhenwei Ma, Ying Zhang, and Changjun Jiang. Anti- money laundering by group-aware deep graph learning. IEEE Transactions on Knowledge and Data Engineering, 2023.","title":"Antimoney laundering by group-aware deep graph learning","authors":"Dawei Cheng, Yujia Ye, Sheng Xiang, Zhenwei Ma, Ying Zhang, Changjun Jiang","year":"2023","venue":"IEEE Transactions on Knowledge and Data Engineering","doi":null,"url":null,"internal_url":null},{"position":6,"raw":"[Chien et al., 2021] Eli Chien, Jianhao Peng, Pan Li, and Ol- gica Milenkovic. Adaptive universal generalized pager- ank graph neural network. In International Conference on Learning Representations, 2021.","title":"Adaptive universal generalized pagerank graph neural network","authors":"Eli Chien, Jianhao Peng, Pan Li, Olgica Milenkovic","year":"2021","venue":"International Conference on Learning Representations","doi":null,"url":null,"internal_url":null},{"position":7,"raw":"[Dong et al., 2021] Yushun Dong, Kaize Ding, Brian Jala- ian, Shuiwang Ji, and Jundong Li. Adagnn: Graph neural networks with adaptive frequency response filter. In Pro- ceedings of the 30th ACM Conference on Information and Knowledge Management, pages 392-401, 2021.","title":"Adagnn: Graph neural networks with adaptive frequency response filter","authors":"Yushun Dong, Kaize Ding, Brian Jalaian, Shuiwang Ji, Jundong Li","year":"2021","venue":"Proceedings of the 30th ACM Conference on Information and Knowledge Management","doi":null,"url":null,"internal_url":null},{"position":8,"raw":"[Dou et al., 2020] Yingtong Dou, Zhiwei Liu, Li Sun, Yu- tong Deng, Hao Peng, and Philip S Yu. Enhancing graph neural network-based fraud detectors against camouflaged fraudsters. In Proceedings of the 29th ACM Conference on Information and Knowledge Management, pages 315-324, 2020.","title":"Enhancing graph neural network-based fraud detectors against camouflaged fraudsters","authors":"Yingtong Dou, Zhiwei Liu, Li Sun, Yutong Deng, Hao Peng, Philip S Yu","year":"2020","venue":"Proceedings of the 29th ACM Conference on Information and Knowledge Management","doi":null,"url":null,"internal_url":null},{"position":9,"raw":"[Feng et al., 2020] Wenzheng Feng, Jie Zhang, Yuxiao Dong, Yu Han, Huanbo Luan, Qian Xu, Qiang Yang, Evgeny Kharlamov, and Jie Tang. Graph random neu- ral networks for semi-supervised learning on graphs. Advances in neural information processing systems, 33:22092-22103, 2020.","title":"Graph random neural networks for semi-supervised learning on graphs","authors":"Wenzheng Feng, Jie Zhang, Yuxiao Dong, Yu Han, Huanbo Luan, Qian Xu, Qiang Yang, Evgeny Kharlamov, Jie Tang","year":"2020","venue":"Advances in neural information processing systems","doi":null,"url":null,"internal_url":null},{"position":10,"raw":"[Fiore et al., 2019] Ugo Fiore, Alfredo De Santis, Francesca Perla, Paolo Zanetti, and Francesco Palmieri. Using gen- erative adversarial networks for improving classification effectiveness in credit card fraud detection. Information Sciences, 479:448-455, 2019.","title":"Using generative adversarial networks for improving classification effectiveness in credit card fraud detection","authors":"Ugo Fiore, Alfredo De Santis, Francesca Perla, Paolo Zanetti, Francesco Palmieri","year":"2019","venue":"Information Sciences","doi":null,"url":null,"internal_url":null},{"position":11,"raw":"[Fu et al., 2016] Kang Fu, Dawei Cheng, Yi Tu, and Liqing Zhang. Credit card fraud detection using convolutional neural networks. In International Conference on Neural Information Processing, 2016.","title":"Credit card fraud detection using convolutional neural networks","authors":"Kang Fu, Dawei Cheng, Yi Tu, Liqing Zhang","year":"2016","venue":"International Conference on Neural Information Processing","doi":null,"url":null,"internal_url":null},{"position":12,"raw":"[Hamilton et al., 2017] Will Hamilton, Zhitao Ying, and Jure Leskovec. Inductive representation learning on large graphs. Advances in neural information processing sys- tems, 30, 2017.","title":"Inductive representation learning on large graphs","authors":"Hamilton","year":"2017","venue":"Advances in neural information processing systems","doi":null,"url":null,"internal_url":null},{"position":13,"raw":"[He et al., 2016] Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Deep residual learning for image recog- nition. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 770-778, 2016.","title":"Deep residual learning for image recognition","authors":"Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun","year":"2016","venue":"Proceedings of the IEEE conference on computer vision and pattern recognition","doi":null,"url":null,"internal_url":null},{"position":14,"raw":"[He et al., 2021] Mingguo He, Zhewei Wei, Hongteng Xu, et al. Bernnet: Learning arbitrary graph spectral filters via bernstein approximation. Advances in Neural Information Processing Systems, 34:14239-14251, 2021.","title":"Bernnet: Learning arbitrary graph spectral filters via bernstein approximation","authors":"He","year":"2021","venue":"Advances in Neural Information Processing Systems","doi":null,"url":null,"internal_url":null},{"position":15,"raw":"[Kingma and Ba, 2015] Diederik Pieter Kingma and Jimmy Ba. Adam: A method for stochastic optimization. In Inter- national Conference on Learning Representations, 2015.","title":"Adam: A method for stochastic optimization","authors":"Pieter Diederik, Jimmy Kingma, Ba","year":"2015","venue":"International Conference on Learning Representations","doi":null,"url":null,"internal_url":null},{"position":16,"raw":"[Kipf and Welling, 2016] Thomas Nikolaus Kipf and Max Welling. Semi-supervised classification with graph convo- lutional networks. In International Conference on Learn- ing Representations, 2016.","title":"Semi-supervised classification with graph convolutional networks","authors":"Thomas Nikolaus, Kipf, Max Welling","year":"2016","venue":"International Conference on Learning Representations","doi":null,"url":null,"internal_url":null},{"position":17,"raw":"[Li et al., 2018] Qimai Li, Zhichao Han, and Xiao-Ming Wu. Deeper insights into graph convolutional networks for semi-supervised learning. In Proceedings of the AAAI con- ference on artificial intelligence, volume 32, 2018.","title":"Deeper insights into graph convolutional networks for semi-supervised learning","authors":"Li","year":"2018","venue":"Proceedings of the AAAI conference on artificial intelligence","doi":null,"url":null,"internal_url":null},{"position":18,"raw":"[Li et al., 2019] Guohao Li, Matthias Muller, Ali Thabet, and Bernard Ghanem. Deepgcns: Can gcns go as deep as cnns? In Proceedings of the IEEE/CVF international conference on computer vision, pages 9267-9276, 2019.","title":"Deepgcns: Can gcns go as deep as cnns?","authors":"Li","year":"2019","venue":"Proceedings of the IEEE/CVF international conference on computer vision","doi":null,"url":null,"internal_url":null},{"position":19,"raw":"[Liu et al., 2018] Ziqi Liu, Chaochao Chen, Xinxing Yang, Jun Zhou, Xiaolong Li, and Le Song. Heterogeneous graph neural networks for malicious account detection. In Proceedings of the 27th ACM international conference on information and knowledge management, pages 2077- 2085, 2018.","title":"Heterogeneous graph neural networks for malicious account detection","authors":"Ziqi Liu, Chaochao Chen, Xinxing Yang, Jun Zhou, Xiaolong Li, Le Song","year":"2018","venue":"Proceedings of the 27th ACM international conference on information and knowledge management","doi":null,"url":null,"internal_url":null},{"position":20,"raw":"[Liu et al., 2020] Zhiwei Liu, Yingtong Dou, Philip S Yu, Yutong Deng, and Hao Peng. Alleviating the inconsistency problem of applying graph neural network to fraud detec- tion. In Proceedings of the 43rd ACM SIGIR Conference on Information Retrieval, pages 1569-1572, 2020.","title":"Alleviating the inconsistency problem of applying graph neural network to fraud detection","authors":"Zhiwei Liu, Yingtong Dou, Philip S Yu, Yutong Deng, Hao Peng","year":"2020","venue":"Proceedings of the 43rd ACM SIGIR Conference on Information Retrieval","doi":null,"url":null,"internal_url":null},{"position":21,"raw":"[Liu et al., 2021] Yang Liu, Xiang Ao, Zidi Qin, Jianfeng Chi, Jinghua Feng, Hao Yang, and Qing He. Pick and choose: a gnn-based imbalanced learning approach for fraud detection. In Proceedings of the web conference 2021, pages 3168-3177, 2021.","title":"Pick and choose: a gnn-based imbalanced learning approach for fraud detection","authors":"Yang Liu, Xiang Ao, Zidi Qin, Jianfeng Chi, Jinghua Feng, Hao Yang, Qing He","year":"2021","venue":"Proceedings of the web conference 2021","doi":null,"url":null,"internal_url":null},{"position":22,"raw":"[Luque et al., 2019] Amalia Luque, Alejandro Carrasco, Alejandro Martín, and Ana de Las Heras. The impact of class imbalance in classification performance metrics based on the binary confusion matrix. Pattern Recogni- tion, 91:216-231, 2019.","title":"The impact of class imbalance in classification performance metrics based on the binary confusion matrix","authors":"Amalia Luque, Alejandro Carrasco, Alejandro Martín, Ana De, Las Heras","year":"2019","venue":"Pattern Recognition","doi":null,"url":null,"internal_url":null},{"position":23,"raw":"[Ma et al., 2023] Jiacheng Ma, Fan Li, Rui Zhang, Zhikang Xu, Dawei Cheng, Yi Ouyang, Ruihui Zhao, Jianguang Zheng, Yefeng Zheng, and Changjun Jiang. Fighting against organized fraudsters using risk diffusion-based parallel graph neural network. In Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence, pages 6138-6146, 2023.","title":"Fighting against organized fraudsters using risk diffusion-based parallel graph neural network","authors":"Jiacheng Ma, Fan Li, Rui Zhang, Zhikang Xu, Dawei Cheng, Yi Ouyang, Ruihui Zhao, Jianguang Zheng, Yefeng Zheng, Changjun Jiang","year":"2023","venue":"Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence","doi":null,"url":null,"internal_url":"/proceedings/2023/681"},{"position":24,"raw":"[McAuley and Leskovec, 2013] Julian John McAuley and Jure Leskovec. From amateurs to connoisseurs: model- ing the evolution of user expertise through online reviews. In Proceedings of the 22nd international conference on World Wide Web, pages 897-908, 2013.","title":"From amateurs to connoisseurs: modeling the evolution of user expertise through online reviews","authors":"Leskovec ; Mcauley, John Julian, Jure Mcauley, Leskovec","year":"2013","venue":"Proceedings of the 22nd international conference on World Wide Web","doi":null,"url":null,"internal_url":null},{"position":25,"raw":"[Meng et al., 2023] Lin Meng, Hesham Mostafa, Marcel Nassar, Xiaonan Zhang, and Jiawei Zhang. Generative graph augmentation for minority class in fraud detection. In Proceedings of the 32nd ACM International Conference on Information and Knowledge Management, pages 4200- 4204, 2023.","title":"Generative graph augmentation for minority class in fraud detection","authors":"Lin Meng, Hesham Mostafa, Marcel Nassar, Xiaonan Zhang, Jiawei Zhang","year":"2023","venue":"Proceedings of the 32nd ACM International Conference on Information and Knowledge Management","doi":null,"url":null,"internal_url":null},{"position":26,"raw":"[Niu et al., 2020] Zhibin Niu, Runlin Li, Junqi Wu, Dawei Cheng, and Jiawan Zhang. iconviz: Interactive visual exploration of the default contagion risk of networked- guarantee loans. In 2020 IEEE conference on visual an- alytics science and technology, pages 84-94. IEEE, 2020.","title":"iconviz: Interactive visual exploration of the default contagion risk of networkedguarantee loans","authors":"Zhibin Niu, Runlin Li, Junqi Wu, Dawei Cheng, Jiawan Zhang","year":"2020","venue":"IEEE conference on visual analytics science and technology","doi":null,"url":null,"internal_url":null},{"position":27,"raw":"[Panigrahi et al., 2009] Suvasini Panigrahi, Amlan Kundu, Shamik Sural, and Arun Kumar Majumdar. Credit card fraud detection: A fusion approach using dempster- shafer theory and bayesian learning. Information Fusion, 10(4):354-363, 2009.","title":"Credit card fraud detection: A fusion approach using dempstershafer theory and bayesian learning","authors":"Panigrahi","year":"2009","venue":"Information Fusion","doi":null,"url":null,"internal_url":null},{"position":28,"raw":"[Paszke et al., 2019] Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, et al. Pytorch: An imperative style, high- performance deep learning library. Advances in neural in- formation processing systems, 32, 2019.","title":"Pytorch: An imperative style, highperformance deep learning library","authors":"Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga","year":"2019","venue":"Advances in neural information processing systems","doi":null,"url":null,"internal_url":null},{"position":29,"raw":"[Pei et al., 2020] Hongbin Pei, Bingzhe Wei, Kevin Chen- Chuan Chang, Yu Lei, and Bo Yang. Geom-gcn: Geomet- ric graph convolutional networks. In International Confer- ence on Learning Representations, 2020.","title":"Geom-gcn: Geometric graph convolutional networks","authors":"Hongbin Pei, Bingzhe Wei, Kevin Chen-Chuan, Yu Chang, Bo Lei, Yang","year":"2020","venue":"International Conference on Learning Representations","doi":null,"url":null,"internal_url":null},{"position":30,"raw":"[Platonov et al., 2024] Oleg Platonov, Denis Kuznedelev, Artem Babenko, and Liudmila Prokhorenkova. Character- izing graph datasets for node classification: Homophily- heterophily dichotomy and beyond. Advances in Neural Information Processing Systems, 36, 2024.","title":"Characterizing graph datasets for node classification: Homophilyheterophily dichotomy and beyond","authors":"Oleg Platonov, Denis Kuznedelev, Artem Babenko, Liudmila Prokhorenkova","year":"2024","venue":"Advances in Neural Information Processing Systems","doi":null,"url":null,"internal_url":null},{"position":31,"raw":"[Rayana and Akoglu, 2015] Shebuti Rayana and Leman Akoglu. Collective opinion spam detection: Bridging re- view networks and metadata. In Proceedings of the 21st ACM SIGKDD Conference on Data Mining, pages 985- 994, 2015.","title":"Shebuti Rayana and Leman Akoglu. Collective opinion spam detection: Bridging review networks and metadata","authors":"Akoglu Rayana","year":"2015","venue":"Proceedings of the 21st ACM SIGKDD Conference on Data Mining","doi":null,"url":null,"internal_url":null},{"position":32,"raw":"[Sánchez et al., 2009] Daniel Sánchez, MA Vila, L Cerda, and José-Maria Serrano. Association rules applied to credit card fraud detection. Expert systems with applica- tions, 36(2):3630-3640, 2009.","title":"Association rules applied to credit card fraud detection","authors":"Daniel Sánchez, M A Vila, L Cerda, José-Maria Serrano","year":"2009","venue":"Expert systems with applications","doi":null,"url":null,"internal_url":null},{"position":33,"raw":"[Seeja et al., 2014] KR Seeja, Masoumeh Zareapoor, et al. Fraudminer: A novel credit card fraud detection model based on frequent itemset mining. The Scientific World Journal, 2014, 2014.","title":"Fraudminer: A novel credit card fraud detection model based on frequent itemset mining","authors":"Seeja","year":"2014","venue":"The Scientific World Journal","doi":null,"url":null,"internal_url":null},{"position":34,"raw":"[Shi et al., 2022] Fengzhao Shi, Yanan Cao, Yanmin Shang, Yuchen Zhou, Chuan Zhou, and Jia Wu. H2-fdetector: A gnn-based fraud detector with homophilic and heterophilic connections. In Proceedings of the ACM Web Conference 2022, pages 1486-1494, 2022.","title":"H2-fdetector: A gnn-based fraud detector with homophilic and heterophilic connections","authors":"Fengzhao Shi, Yanan Cao, Yanmin Shang, Yuchen Zhou, Chuan Zhou, Jia Wu","year":"2022","venue":"Proceedings of the ACM Web Conference 2022","doi":null,"url":null,"internal_url":null},{"position":35,"raw":"[Tang et al., 2022] Jianheng Tang, Jiajin Li, Ziqi Gao, and Jia Li. Rethinking graph neural networks for anomaly de- tection. In International Conference on Machine Learn- ing, pages 21076-21089, 2022.","title":"Rethinking graph neural networks for anomaly detection","authors":"Tang","year":"2022","venue":"International Conference on Machine Learning","doi":null,"url":null,"internal_url":null},{"position":36,"raw":"[van der Maaten and Hinton, 2008] Laurens van der Maaten and Geoffrey Hinton. Visualizing data using t-sne. Journal of Machine Learning Research, 9(86):2579-2605, 2008.","title":"Visualizing data using t-sne","authors":"Van Der Maaten, Laurens Hinton, Geoffrey Van Der Maaten, Hinton","year":"2008","venue":"Journal of Machine Learning Research","doi":null,"url":null,"internal_url":null},{"position":37,"raw":"[Wang and Derr, 2021] Yu Wang and Tyler Derr. Tree de- composed graph neural network. In Proceedings of the 30th ACM international conference on information & knowledge management, pages 2040-2049, 2021.","title":"Tree decomposed graph neural network","authors":"Yu Wang, Tyler Derr","year":"2021","venue":"Proceedings of the 30th ACM international conference on information & knowledge management","doi":null,"url":null,"internal_url":null},{"position":38,"raw":"[Wang and Zhang, 2022] Xiyuan Wang and Muhan Zhang. How powerful are spectral graph neural networks. In Inter- national Conference on Machine Learning, pages 23341- 23362, 2022.","title":"How powerful are spectral graph neural networks","authors":"Wang, Zhang, Xiyuan Wang, Muhan Zhang","year":"2022","venue":"International Conference on Machine Learning","doi":null,"url":null,"internal_url":null},{"position":39,"raw":"[Wang et al., 2019] Daixin Wang, Jianbin Lin, Peng Cui, Quanhui Jia, Zhen Wang, Yanming Fang, Quan Yu, Jun Zhou, Shuang Yang, and Yuan Qi. A semi-supervised graph attentive network for financial fraud detection. In 2019 IEEE International Conference on Data Mining, pages 598-607. IEEE, 2019.","title":"A semi-supervised graph attentive network for financial fraud detection","authors":"Daixin Wang, Jianbin Lin, Peng Cui, Quanhui Jia, Zhen Wang, Yanming Fang, Quan Yu, Jun Zhou, Shuang Yang, Yuan Qi","year":"2019","venue":"IEEE International Conference on Data Mining","doi":null,"url":null,"internal_url":null},{"position":40,"raw":"[Xiang et al., 2023] Sheng Xiang, Mingzhi Zhu, Dawei Cheng, Enxia Li, Ruihui Zhao, Yi Ouyang, Ling Chen, and Yefeng Zheng. Semi-supervised credit card fraud de- tection via attribute-driven graph representation. In Pro- ceedings of the AAAI Conference on Artificial Intelligence, volume 37, pages 14557-14565, 2023.","title":"Semi-supervised credit card fraud detection via attribute-driven graph representation","authors":"Sheng Xiang, Mingzhi Zhu, Dawei Cheng, Enxia Li, Ruihui Zhao, Yi Ouyang, Ling Chen, Yefeng Zheng","year":"2023","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","doi":null,"url":null,"internal_url":null},{"position":41,"raw":"[Xu et al., 2018] Keyulu Xu, Chengtao Li, Yonglong Tian, Tomohiro Sonobe, Ken-ichi Kawarabayashi, and Stefanie Jegelka. Representation learning on graphs with jumping knowledge networks. In International conference on ma- chine learning, pages 5453-5462, 2018.","title":"Representation learning on graphs with jumping knowledge networks","authors":"Xu","year":"2018","venue":"International conference on machine learning","doi":null,"url":null,"internal_url":null},{"position":42,"raw":"[Xue et al., 2020] Hui Xue, Xin-Kai Sun, and Wei-Xiang Sun. Multi-hop hierarchical graph neural networks. In 2020 IEEE International Conference on Big Data and Smart Computing (BigComp), pages 82-89. IEEE, 2020.","title":"Multi-hop hierarchical graph neural networks","authors":"Hui Xue, Xin-Kai Sun, Wei-Xiang Sun","year":"2020","venue":"IEEE International Conference on Big Data and Smart Computing (BigComp)","doi":null,"url":null,"internal_url":null},{"position":43,"raw":"[Zhang et al., 2024] Rui Zhang, Dawei Cheng, Jie Yang, Yi Ouyang, Xian Wu, Yefeng Zheng, and Changjun Jiang. Pre-trained online contrastive learning for insurance fraud detection. In Proceedings of the AAAI Conference on Arti- ficial Intelligence, volume 38, pages 22511-22519, 2024.","title":"Pre-trained online contrastive learning for insurance fraud detection","authors":"Rui Zhang, Dawei Cheng, Jie Yang, Yi Ouyang, Xian Wu, Yefeng Zheng, Changjun Jiang","year":"2024","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","doi":null,"url":null,"internal_url":null}],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}