{"type":"ScholarlyArticle","title":"Safeguarding Fraud Detection from Attacks: A Robust Graph Learning Approach","authors":[{"name":"Jiasheng Wu","slug":"jiasheng-wu","given":"Jiasheng","family":"Wu","affiliation":"Department of Computer Science and Technology , Tongji University , Shanghai , China"},{"name":"Xin Liu","slug":"xin-liu","given":"Xin","family":"Liu","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; Shanghai Artificial Intelligence Laboratory , Shanghai , China"},{"name":"Yi Ouyang","slug":"yi-ouyang","given":"Yi","family":"Ouyang","affiliation":"Jarvis Research Center , Tencent YouTu Lab , Shenzhen , China"},{"name":"Xian Wu","slug":"xian-wu","given":"Xian","family":"Wu","affiliation":"Jarvis Research Center , Tencent YouTu Lab , Shenzhen , China"},{"name":"Yefeng Zheng","slug":"yefeng-zheng","given":"Yefeng","family":"Zheng","affiliation":"Jarvis Research Center , Tencent YouTu Lab , Shenzhen , China"}],"abstract":"Financial fraud is one of the most significant social issues and has caused tremendous property losses. Graph neural networks (GNNs) have been applied to anti-fraud practices and achieved decent results. However, recent researches have discovered flaws in the robustness of fraud-detection models based on GNNs, enabling fraudsters to mislead them through attacks like data poisoning. In addition, most existing attack-defense models tend to study on ideal settings and lose information during truncation or filtering, which lowers their performances in complicated financial fraud cases. Therefore, in this paper, we propose a novel robust anti-fraud GNN model. In particular, we first design an attack algorithm tampering with both features and structures of graph data to simulate fraudsters' attacking behaviors in real-life complex fraud scenarios. Then we apply singular value decomposition to the graph and learn the decomposed matrices in a GNN model with specifically designed joint losses. This enables our model to learn the graph patterns in low-rank subspaces without losing too much detailed information and fit the graph structure to characteristics including class-homophily and sparsity to guarantee robustness. The proposed approach is experimented on real-world fraud datasets, which demonstrates its advantages in fraud detection and robustness compared with the state-of-the-art baselines.","doi":"10.24963/ijcai.2024/830","url":"https://ijcai.org/proceedings/2024/830","pdf":"https://ijcai.org/proceedings/2024/0830.pdf","markdown":"https://ijcai.org/proceedings/2024/830.md","bibtex":"https://ijcai.org/proceedings/2024/bibtex/830","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":830,"pages":{"first":7500,"last":7508},"keywords":["Data Mining: General","Multidisciplinary Topics and Applications: General"],"videos":[],"references":[{"position":1,"raw":"[Akoglu et al., 2015] Leman Akoglu, Hanghang Tong, and Danai Koutra. Graph based anomaly detection and de- scription: a survey. Data mining and knowledge discovery, 29:626-688, 2015.","title":"Graph based anomaly detection and description: a survey","authors":"Akoglu","year":"2015","venue":"Data mining and knowledge discovery","doi":null,"url":null,"internal_url":null},{"position":2,"raw":"[Bandyopadhyay et al., 2020] Sambaran Bandyopadhyay, Lokesh N, Saley Vishal Vivek, and M Narasimha Murty. Outlier resistant unsupervised deep architectures for attributed network embedding. In Proceedings of the 13th international conference on web search and data mining, pages 25-33, 2020.","title":"Outlier resistant unsupervised deep architectures for attributed network embedding","authors":"Bandyopadhyay","year":"2020","venue":"Proceedings of the 13th international conference on web search and data mining","doi":null,"url":null,"internal_url":null},{"position":3,"raw":"[Bolton and Hand, 2002] Richard J Bolton and David J Hand. Statistical fraud detection: A review. Statistical science, 17(3):235-255, 2002.","title":"Statistical fraud detection: A review","authors":"J Richard, David J Bolton, Hand","year":"2002","venue":"Statistical science","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":"[Choi et al., 2022] Yoonhyuk Choi, Jiho Choi, Taewook Ko, Hyungho Byun, and Chong-Kwon Kim. Finding het- erophilic neighbors via confidence-based subgraph match- ing for semi-supervised node classification. In Proceed- ings of the 31st ACM International Conference on Infor- mation & Knowledge Management, pages 283-292, 2022.","title":"Finding heterophilic neighbors via confidence-based subgraph matching for semi-supervised node classification","authors":"Choi","year":"2022","venue":"Proceedings of the 31st ACM International Conference on Information & Knowledge Management","doi":null,"url":null,"internal_url":null},{"position":6,"raw":"[Deng et al., 2019] Zhijie Deng, Yinpeng Dong, and Jun Zhu. Batch virtual adversarial training for graph convolu- tional networks. arXiv preprint arXiv:1902.09192, 2019.","title":"Batch virtual adversarial training for graph convolutional networks","authors":"Zhijie Deng, Yinpeng Dong, Jun Zhu","year":"2019","venue":null,"doi":null,"url":null,"internal_url":null},{"position":7,"raw":"[Deng et al., 2022] Chenhui Deng, Xiuyu Li, Zhuo Feng, and Zhiru Zhang. Garnet: Reduced-rank topology learning for robust and scalable graph neural networks. In Learning on Graphs Conference, pages 3-1. PMLR, 2022.","title":"Garnet: Reduced-rank topology learning for robust and scalable graph neural networks","authors":"Deng","year":"2022","venue":"Learning on Graphs Conference","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 interna- tional conference on information & knowledge manage- ment, 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 international conference on information & knowledge management","doi":null,"url":null,"internal_url":null},{"position":9,"raw":"[Gupta, 2019] Rohit Anand Gupta. Healthcare provider fraud detection analysis. Website, 2019. https://www.kaggle.com/datasets/rohitrox/healthcare- provider-fraud-detection-analysis/data.","title":"Healthcare provider fraud detection analysis","authors":"Anand Rohit, Gupta","year":"2019","venue":null,"doi":null,"url":"https://www.kaggle.com/datasets/rohitrox/healthcare-provider-fraud-detection-analysis/data","internal_url":null},{"position":10,"raw":"[Hamilton et al., 2017] William L. Hamilton, Rex Ying, and Jure Leskovec. Inductive representation learning on large graphs. In NIPS, 2017.","title":"Inductive representation learning on large graphs","authors":"William L Hamilton, Rex Ying, Jure Leskovec","year":"2017","venue":"NIPS","doi":null,"url":null,"internal_url":null},{"position":11,"raw":"[Hu et al., 2019] Binbin Hu, Zhiqiang Zhang, Chuan Shi, Jun Zhou, Xiaolong Li, and Yuan Qi. Cash-out user de- tection based on attributed heterogeneous information net- work with a hierarchical attention mechanism. In The Thirty-Third AAAI Conference on Artificial Intelligence, 2019.","title":"Cash-out user de-tection based on attributed heterogeneous information network with a hierarchical attention mechanism","authors":"Binbin Hu, Zhiqiang Zhang, Chuan Shi, Jun Zhou, Xiaolong Li, Yuan Qi","year":"2019","venue":"The Thirty-Third AAAI Conference on Artificial Intelligence","doi":null,"url":null,"internal_url":null},{"position":12,"raw":"[Huang et al., 2023] Jincheng Huang, Lun Du, Xu Chen, Qiang Fu, Shi Han, and Dongmei Zhang. Robust mid-pass filtering graph convolutional networks. In Proceedings of the ACM Web Conference 2023, pages 328-338, 2023.","title":"Robust mid-pass filtering graph convolutional networks","authors":"Jincheng Huang, Lun Du, Xu Chen, Qiang Fu, Shi Han, Dongmei Zhang","year":"2023","venue":"Proceedings of the ACM Web Conference 2023","doi":null,"url":null,"internal_url":null},{"position":13,"raw":"[Jin and Zhang, 2019] Hongwei Jin and Xinhua Zhang. La- tent adversarial training of graph convolution networks. In ICML workshop on learning and reasoning with graph- structured representations, volume 2, 2019.","title":"Latent adversarial training of graph convolution networks","authors":"Jin, Zhang, Hongwei Jin, Xinhua Zhang","year":"2019","venue":"ICML workshop on learning and reasoning with graphstructured representations","doi":null,"url":null,"internal_url":null},{"position":14,"raw":"[Jin et al., 2020] Wei Jin, Yao Ma, Xiaorui Liu, Xianfeng Tang, Suhang Wang, and Jiliang Tang. Graph structure learning for robust graph neural networks. In Proceedings of the 26th ACM SIGKDD international conference on knowledge discovery & data mining, pages 66-74, 2020.","title":"Graph structure learning for robust graph neural networks","authors":"Wei Jin, Yao Ma, Xiaorui Liu, Xianfeng Tang, Suhang Wang, Jiliang Tang","year":"2020","venue":"Proceedings of the 26th ACM SIGKDD international conference on knowledge discovery & data mining","doi":null,"url":null,"internal_url":null},{"position":15,"raw":"[Khedmati et al., 2020] Mohamad Khedmati, Masoud Er- fani, and Mohammad GhasemiGol. Applying support vec- tor data description for fraud detection. arXiv preprint arXiv:2006.00618, 2020.","title":"Applying support vector data description for fraud detection","authors":"Mohamad Khedmati, Masoud Erfani, Mohammad Ghasemigol","year":"2020","venue":null,"doi":null,"url":null,"internal_url":null},{"position":16,"raw":"[Kingma and Ba, 2014] Diederik P Kingma and Jimmy Ba. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980, 2014.","title":"Adam: A method for stochastic optimization","authors":"P Diederik, Jimmy Kingma, Ba","year":"2014","venue":null,"doi":null,"url":null,"internal_url":null},{"position":17,"raw":"[Kipf and Welling, 2016] Thomas N Kipf and Max Welling. Semi-supervised classification with graph convolutional networks. arXiv preprint arXiv:1609.02907, 2016.","title":"Semi-supervised classification with graph convolutional networks","authors":"N Thomas, Max Kipf, Welling","year":"2016","venue":null,"doi":null,"url":null,"internal_url":null},{"position":18,"raw":"[Liang et al., 2023] Yongquan Liang, Qiuyu Song, Zhongy- ing Zhao, Hui Zhou, and Maoguo Gong. Ba- gnn: Behavior-aware graph neural network for session- based recommendation. Frontiers of Computer Science, 17(6):176613, 2023.","title":"Bagnn: Behavior-aware graph neural network for sessionbased recommendation","authors":"Yongquan Liang, Qiuyu Song, Zhongying Zhao, Hui Zhou, Maoguo Gong","year":"2023","venue":"Frontiers of Computer Science","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 43nd International ACM SIGIR Conference on Research and Development in Information Retrieval, 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 43nd International ACM SIGIR Conference on Research and Development in 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":"[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":23,"raw":"[PricewaterhouseCoopers, 2022] PricewaterhouseCoopers. Pwc's global economic crime and fraud survey 2022. 2022. available at: https://www.pwc.com/gx/en/services/ forensics/economic-crime-survey.html.","title":"PricewaterhouseCoopers. Pwc's global economic crime and fraud survey 2022","authors":null,"year":"2022","venue":null,"doi":null,"url":"https://www.pwc.com/gx/en/services/forensics/economic-crime-survey.html","internal_url":null},{"position":24,"raw":"[Qian et al., 2023] Siyi Qian, Haochao Ying, Renjun Hu, Jingbo Zhou, Jintai Chen, Danny Z Chen, and Jian Wu. Robust training of graph neural networks via noise gover- nance. In Proceedings of the Sixteenth ACM International Conference on Web Search and Data Mining, pages 607- 615, 2023.","title":"Robust training of graph neural networks via noise governance","authors":"Haochao Siyi Qian, Renjun Ying, Jingbo Hu, Jintai Zhou, Danny Z Chen, Jian Chen, Wu","year":"2023","venue":"Proceedings of the Sixteenth ACM International Conference on Web Search and Data Mining","doi":null,"url":null,"internal_url":null},{"position":25,"raw":"[Reurink, 2019] Arjan Reurink. Financial fraud: A literature review. Contemporary Topics in Finance: A Collection of Literature Surveys, pages 79-115, 2019.","title":"Financial fraud: A literature review","authors":"Arjan Reurink","year":"2019","venue":"Contemporary Topics in Finance: A Collection of Literature Surveys","doi":null,"url":null,"internal_url":null},{"position":26,"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":27,"raw":"[Velickovic et al., 2017] Petar Velickovic, Guillem Cucu- rull, Arantxa Casanova, Adriana Romero, Pietro Lio, Yoshua Bengio, et al. Graph attention networks. stat, 1050(20):10-48550, 2017.","title":"Graph attention networks","authors":"Petar Velickovic, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Lio, Yoshua Bengio","year":"2017","venue":"stat","doi":null,"url":null,"internal_url":null},{"position":28,"raw":"[Wang et al., 2019a] 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 (ICDM), pages 598-607. IEEE, 2019.","title":"A semi-supervised graph attentive network for financial fraud detection","authors":"Wang","year":"2019","venue":"IEEE International Conference on Data Mining (ICDM)","doi":null,"url":null,"internal_url":null},{"position":29,"raw":"[Wang et al., 2019b] Jianyu Wang, Rui Wen, Chunming Wu, Yu Huang, and Jian Xion. Fdgars: Fraudster detection via graph convolutional networks in online app review system. In Companion Proceedings of The 2019 World Wide Web Conference, pages 310-316, 2019.","title":"Fdgars: Fraudster detection via graph convolutional networks in online app review system","authors":"Wang","year":"2019","venue":"Companion Proceedings of The 2019 World Wide Web Conference","doi":null,"url":null,"internal_url":null},{"position":30,"raw":"[Wang et al., 2019c] Xiaoyun Wang, Xuanqing Liu, and Cho-Jui Hsieh. Graphdefense: Towards robust graph con- volutional networks. arXiv preprint arXiv:1911.04429, 2019.","title":"Graphdefense: Towards robust graph convolutional networks","authors":"Wang","year":"2019","venue":null,"doi":null,"url":null,"internal_url":null},{"position":31,"raw":"[Wang et al., 2021] Ruoxi Wang, Rakesh Shivanna, Derek Cheng, Sagar Jain, Dong Lin, Lichan Hong, and Ed Chi. Dcn v2: Improved deep & cross network and practical lessons for web-scale learning to rank systems. In Pro- ceedings of the web conference 2021, pages 1785-1797, 2021.","title":"Dcn v2: Improved deep & cross network and practical lessons for web-scale learning to rank systems","authors":"Wang","year":"2021","venue":"Proceedings of the web conference 2021","doi":null,"url":null,"internal_url":null},{"position":32,"raw":"[Wang et al., 2023] Yuchen Wang, Jinghui Zhang, Zhengjie Huang, Weibin Li, Shikun Feng, Ziheng Ma, Yu Sun, Di- anhai Yu, Fang Dong, Jiahui Jin, et al. Label information enhanced fraud detection against low homophily in graphs. In Proceedings of the ACM Web Conference 2023, pages 406-416, 2023.","title":"Label information enhanced fraud detection against low homophily in graphs","authors":"Yuchen Wang, Jinghui Zhang, Zhengjie Huang, Weibin Li, Shikun Feng, Ziheng Ma, Yu Sun, Dianhai Yu, Fang Dong, Jiahui Jin","year":"2023","venue":"Proceedings of the ACM Web Conference 2023","doi":null,"url":null,"internal_url":null},{"position":33,"raw":"[Wu et al., 2022] Zhebin Wu, Lin Shu, Ziyue Xu, Yaomin Chang, Chuan Chen, and Zibin Zheng. Robust tensor graph convolutional networks via t-svd based graph aug- mentation. In Proceedings of the 28th ACM SIGKDD Con- ference on Knowledge Discovery and Data Mining, pages 2090-2099, 2022.","title":"Robust tensor graph convolutional networks via t-svd based graph augmentation","authors":"Zhebin Wu, Lin Shu, Ziyue Xu, Yaomin Chang, Chuan Chen, Zibin Zheng","year":"2022","venue":"Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","doi":null,"url":null,"internal_url":null},{"position":34,"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":35,"raw":"[Zhang et al., 2020] Shijie Zhang, Hongzhi Yin, Tong Chen, Quoc Viet Nguyen Hung, Zi Huang, and Lizhen Cui. Gcn- based user representation learning for unifying robust rec- ommendation and fraudster detection. In Proceedings of the 43rd international ACM SIGIR conference on research and development in information retrieval, pages 689-698, 2020.","title":"Gcnbased user representation learning for unifying robust recommendation and fraudster detection","authors":"Shijie Zhang, Hongzhi Yin, Tong Chen, Quoc Viet Nguyen, Zi Hung, Lizhen Huang, Cui","year":"2020","venue":"Proceedings of the 43rd international ACM SIGIR conference on research and development in information retrieval","doi":null,"url":null,"internal_url":null},{"position":36,"raw":"[Zhang et al., 2021] Ge Zhang, Jia Wu, Jian Yang, Amin Be- heshti, Shan Xue, Chuan Zhou, and Quan Z Sheng. Frau- dre: Fraud detection dual-resistant to graph inconsistency and imbalance. In 2021 IEEE International Conference on Data Mining (ICDM), pages 867-876. IEEE, 2021.","title":"Fraudre: Fraud detection dual-resistant to graph inconsistency and imbalance","authors":"Ge Zhang, Jia Wu, Jian Yang, Amin Beheshti, Shan Xue, Chuan Zhou, Quan, Sheng","year":"2021","venue":"IEEE International Conference on Data Mining (ICDM)","doi":null,"url":null,"internal_url":null},{"position":37,"raw":"[Zhang et al., 2022] Mengmei Zhang, Xiao Wang, Meiqi Zhu, Chuan Shi, Zhiqiang Zhang, and Jun Zhou. Robust heterogeneous graph neural networks against adversarial attacks. In Proceedings of the AAAI Conference on Artifi- cial Intelligence, volume 36, pages 4363-4370, 2022.","title":"Robust heterogeneous graph neural networks against adversarial attacks","authors":"Mengmei Zhang, Xiao Wang, Meiqi Zhu, Chuan Shi, Zhiqiang Zhang, Jun Zhou","year":"2022","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","doi":null,"url":null,"internal_url":null},{"position":38,"raw":"[Zhang et al., 2023] Ya-Lin Zhang, Yi-Xuan Sun, Fangfang Fan, Meng Li, Yeyu Zhao, Wei Wang, Longfei Li, Jun Zhou, and Jinghua Feng. A framework for detecting frauds from extremely few labels. In Proceedings of the Sixteenth ACM International Conference on Web Search and Data Mining, pages 1124-1127, 2023.","title":"A framework for detecting frauds from extremely few labels","authors":"Ya-Lin Zhang, Yi-Xuan Sun, Fangfang Fan, Meng Li, Yeyu Zhao, Wei Wang, Longfei Li, Jun Zhou, Jinghua Feng","year":"2023","venue":"Proceedings of the Sixteenth ACM International Conference on Web Search and Data Mining","doi":null,"url":null,"internal_url":null},{"position":39,"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},{"position":40,"raw":"[Zhao et al., 2021] Tong Zhao, Yozen Liu, Leonardo Neves, Oliver Woodford, Meng Jiang, and Neil Shah. Data aug- mentation for graph neural networks. In Proceedings of the aaai conference on artificial intelligence, volume 35, pages 11015-11023, 2021.","title":"Data augmentation for graph neural networks","authors":"Tong Zhao, Yozen Liu, Leonardo Neves, Oliver Woodford, Meng Jiang, Neil Shah","year":"2021","venue":"Proceedings of the aaai conference on artificial intelligence","doi":null,"url":null,"internal_url":null},{"position":41,"raw":"[Zhao et al., 2023] Jianan Zhao, Qianlong Wen, Mingxuan Ju, Chuxu Zhang, and Yanfang Ye. Self-supervised graph structure refinement for graph neural networks. In Pro- ceedings of the Sixteenth ACM International Conference on Web Search and Data Mining, pages 159-167, 2023.","title":"Self-supervised graph structure refinement for graph neural networks","authors":"Jianan Zhao, Qianlong Wen, Mingxuan Ju, Chuxu Zhang, Yanfang Ye","year":"2023","venue":"Proceedings of the Sixteenth ACM International Conference on Web Search and Data Mining","doi":null,"url":null,"internal_url":null},{"position":42,"raw":"[Zhou et al., 2013] Ke Zhou, Hongyuan Zha, and Le Song. Learning social infectivity in sparse low-rank networks us- ing multi-dimensional hawkes processes. In Artificial In- telligence and Statistics, pages 641-649. PMLR, 2013.","title":"Learning social infectivity in sparse low-rank networks using multi-dimensional hawkes processes","authors":"Ke Zhou, Hongyuan Zha, Le Song","year":"2013","venue":"Artificial Intelligence and Statistics","doi":null,"url":null,"internal_url":null},{"position":43,"raw":"[Zhu et al., 2019] Dingyuan Zhu, Ziwei Zhang, Peng Cui, and Wenwu Zhu. Robust graph convolutional networks against adversarial attacks. In Proceedings of the 25th ACM SIGKDD international conference on knowledge discovery & data mining, pages 1399-1407, 2019.","title":"Robust graph convolutional networks against adversarial attacks","authors":"Dingyuan Zhu, Ziwei Zhang, Peng Cui, Wenwu Zhu","year":"2019","venue":"Proceedings of the 25th ACM SIGKDD international conference on knowledge discovery & data mining","doi":null,"url":null,"internal_url":null}],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}