{"type":"ScholarlyArticle","title":"LG-FGAD: An Effective Federated Graph Anomaly Detection Framework","authors":[{"name":"Jinyu Cai","slug":"jinyu-cai","given":"Jinyu","family":"Cai","affiliation":"Institute of Data Science , National University of Singapore , Singapore"},{"name":"Yunhe Zhang","slug":"yunhe-zhang","given":"Yunhe","family":"Zhang","affiliation":"Shenzhen Research Institute of Big Data , Shenzhen , China; School of Data Science , The Chinese University of Hong Kong , Shenzhen (CUHK-Shenzhen) , China"},{"name":"Jicong Fan","slug":"jicong-fan","given":"Jicong","family":"Fan","affiliation":"Shenzhen Research Institute of Big Data , Shenzhen , China; School of Data Science , The Chinese University of Hong Kong , Shenzhen (CUHK-Shenzhen) , China"},{"name":"See-Kiong Ng","slug":"see-kiong-ng","given":"See Kiong","family":"Ng","affiliation":"Institute of Data Science , National University of Singapore , Singapore"}],"abstract":"Graph anomaly detection (GAD), which aims to identify those graphs that are significantly different from other ones, has gained growing attention in many real-world scenarios. However, existing GAD methods are generally designed for centralized training, while in real-world collaboration, graph data is generally distributed across various clients and exhibits significant non-IID characteristics. To tackle this challenge, we propose a federated graph anomaly detection framework with local-global anomaly awareness (LG-FGAD). We first introduce a self-adversarial generation module and train a discriminator to identify the generated anomalous graphs from the normal graph. To enhance the anomaly awareness of the model, we propose to maximize/minimize the mutual information from local and global perspectives. Importantly, to alleviate the impact of non-IID problems in collaborative learning, we propose a dual knowledge distillation module. The knowledge distillation is conducted over both logits and embedding distributions, and only the student model engages in collaboration to preserve the personalization of each client. Empirical results on various types of real-world datasets prove the superiority of our method.","doi":"10.24963/ijcai.2024/416","url":"https://ijcai.org/proceedings/2024/416","pdf":"https://ijcai.org/proceedings/2024/0416.pdf","markdown":"https://ijcai.org/proceedings/2024/416.md","bibtex":"https://ijcai.org/proceedings/2024/bibtex/416","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":"Machine Learning","track":"Main Track","number":416,"pages":{"first":3760,"last":3769},"keywords":["Data Mining: DM: Anomaly/outlier detection","Machine Learning: ML: Unsupervised learning","Machine Learning: ML: Federated learning"],"videos":[],"references":[{"position":1,"raw":"[Aggarwal, 2011] Charu C Aggarwal. An introduction to so- cial network data analytics. Springer, 2011.","title":"An introduction to social network data analytics","authors":"C Charu, Aggarwal","year":"2011","venue":null,"doi":null,"url":null,"internal_url":null},{"position":2,"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 Discov- ery, 29:626-688, 2015.","title":"Graph based anomaly detection and description: a survey","authors":"Leman Akoglu, Hanghang Tong, Danai Koutra","year":"2015","venue":"Data Mining and Knowledge Discovery","doi":null,"url":null,"internal_url":null},{"position":3,"raw":"[Cai and Fan, 2022] Jinyu Cai and Jicong Fan. Perturbation learning based anomaly detection. NeurIPS, 35, 2022.","title":"Perturbation learning based anomaly detection","authors":"Fan ; Cai, Jinyu Cai, Jicong Fan","year":"2022","venue":"NeurIPS","doi":null,"url":null,"internal_url":null},{"position":4,"raw":"[Cai et al., 2023] Jinyu Cai, Yunhe Zhang, and Jicong Fan. Self-discriminative modeling for anomalous graph detec- tion. arXiv preprint arXiv:2310.06261, 2023.","title":"Self-discriminative modeling for anomalous graph detection","authors":"Jinyu Cai, Yunhe Zhang, Jicong Fan","year":"2023","venue":null,"doi":null,"url":null,"internal_url":null},{"position":5,"raw":"[Cai et al., 2024a] Jinyu Cai, Yi Han, Wenzhong Guo, and Jicong Fan. Deep graph-level clustering using pseudo- label-guided mutual information maximization network. NCAA, pages 1-16, 2024.","title":"Deep graph-level clustering using pseudolabel-guided mutual information maximization network","authors":"Cai","year":"2024","venue":null,"doi":null,"url":null,"internal_url":null},{"position":6,"raw":"[Cai et al., 2024b] Jinyu Cai, Yunhe Zhang, Shiping Wang, Jicong Fan, and Wenzhong Guo. Wasserstein embedding learning for deep clustering: A generative approach. IEEE T-MM, 2024.","title":"Wasserstein embedding learning for deep clustering: A generative approach","authors":"Cai","year":"2024","venue":"IEEE T-MM","doi":null,"url":null,"internal_url":null},{"position":7,"raw":"[Chen et al., 2022] Man-Sheng Chen, Chang-Dong Wang, Dong Huang, Jian-Huang Lai, and Philip S. Yu. Efficient orthogonal multi-view subspace clustering. In SIGKDD, pages 127-135. ACM, 2022.","title":"Efficient orthogonal multi-view subspace clustering","authors":"Chen","year":"2022","venue":"SIGKDD","doi":null,"url":null,"internal_url":null},{"position":8,"raw":"[Chen et al., 2023a] Zhaoliang Chen, Lele Fu, Shunxin Xiao, Shiping Wang, Claudia Plant, and Wenzhong Guo. Multi-view graph convolutional networks with differen- tiable node selection. ACM T-KDD, 18(1):1-21, 2023.","title":"Multi-view graph convolutional networks with differentiable node selection","authors":"Chen","year":"2023","venue":"ACM T-KDD","doi":null,"url":null,"internal_url":null},{"position":9,"raw":"[Chen et al., 2023b] Zhaoliang Chen, Lele Fu, Jie Yao, Wen- zhong Guo, Claudia Plant, and Shiping Wang. Learnable graph convolutional network and feature fusion for multi- view learning. Information Fusion, 95:109-119, 2023.","title":"Learnable graph convolutional network and feature fusion for multiview learning","authors":"Chen","year":"2023","venue":"Information Fusion","doi":null,"url":null,"internal_url":null},{"position":10,"raw":"[Duan et al., 2023] Jingcan Duan, Siwei Wang, Pei Zhang, En Zhu, Jingtao Hu, Hu Jin, Yue Liu, and Zhibin Dong. Graph anomaly detection via multi-scale contrastive learn- ing networks with augmented view. In AAAI, volume 37, pages 7459-7467, 2023.","title":"Graph anomaly detection via multi-scale contrastive learning networks with augmented view","authors":"Jingcan Duan, Siwei Wang, Pei Zhang, En Zhu, Jingtao Hu, Jin Hu, Yue Liu, Zhibin Dong","year":"2023","venue":"AAAI","doi":null,"url":null,"internal_url":null},{"position":11,"raw":"[Fey and Lenssen, 2019] Matthias Fey and Jan Eric Lenssen. Fast graph representation learning with pytorch geometric. arXiv preprint arXiv:1903.02428, 2019.","title":"Fast graph representation learning with pytorch geometric","authors":"Fey, Lenssen, Matthias Fey, Jan Eric Lenssen","year":"2019","venue":null,"doi":null,"url":null,"internal_url":null},{"position":12,"raw":"[Fu et al., 2022] Xingbo Fu, Binchi Zhang, Yushun Dong, Chen Chen, and Jundong Li. Federated graph machine learning: A survey of concepts, techniques, and applica- tions. ACM SIGKDD Explorations Newsletter, 24(2):32- 47, 2022.","title":"Federated graph machine learning: A survey of concepts, techniques, and applications","authors":"Fu","year":"2022","venue":"ACM SIGKDD Explorations Newsletter","doi":null,"url":null,"internal_url":null},{"position":13,"raw":"[Fu et al., 2024] Dazhi Fu, Zhao Zhang, and Jicong Fan. Dense projection for anomaly detection. In AAAI, vol- ume 38, pages 8398-8408, 2024.","title":"Dense projection for anomaly detection","authors":"Dazhi Fu, Zhao Zhang, Jicong Fan","year":"2024","venue":"AAAI","doi":null,"url":null,"internal_url":null},{"position":14,"raw":"[Goodfellow et al., 2014] Ian Goodfellow, Jean Pouget- Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio. Gen- erative adversarial nets. NeurIPS, 27, 2014.","title":"Generative adversarial nets","authors":"Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, Yoshua Bengio","year":"2014","venue":"NeurIPS","doi":null,"url":null,"internal_url":null},{"position":15,"raw":"[Hinton et al., 2015] Geoffrey Hinton, Oriol Vinyals, and Jeff Dean. Distilling the knowledge in a neural network. arXiv preprint arXiv:1503.02531, 2015.","title":"Distilling the knowledge in a neural network","authors":"Geoffrey Hinton, Oriol Vinyals, Jeff Dean","year":"2015","venue":null,"doi":null,"url":null,"internal_url":null},{"position":16,"raw":"[Huang et al., 2023] Wenke Huang, Guancheng Wan, Mang Ye, and Bo Du. Federated graph semantic and structural learning. In IJCAI, pages 139-143, 2023.","title":"Federated graph semantic and structural learning","authors":"Wenke Huang, Guancheng Wan, Mang Ye, Bo Du","year":"2023","venue":"IJCAI","doi":null,"url":null,"internal_url":"/proceedings/2023/426"},{"position":17,"raw":"[Kingma and Welling, 2013] Diederik P Kingma and Max Welling. Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114, 2013.","title":"Auto-encoding variational bayes","authors":"P Diederik, Max Kingma, Welling","year":"2013","venue":null,"doi":null,"url":null,"internal_url":null},{"position":18,"raw":"[Kipf and Welling, 2016] Thomas N Kipf and Max Welling. Variational graph auto-encoders. arXiv preprint arXiv:1611.07308, 2016.","title":"Variational graph auto-encoders","authors":"N Thomas, Max Kipf, Welling","year":"2016","venue":null,"doi":null,"url":null,"internal_url":null},{"position":19,"raw":"[Kipf and Welling, 2017] Thomas N Kipf and Max Welling. Semi-supervised classification with graph convolutional networks. In ICLR, 2017.","title":"Semi-supervised classification with graph convolutional networks","authors":"N Thomas, Max Kipf, Welling","year":"2017","venue":"ICLR","doi":null,"url":null,"internal_url":null},{"position":20,"raw":"[Li et al., 2020] Tian Li, Anit Kumar Sahu, Manzil Zaheer, Maziar Sanjabi, Ameet Talwalkar, and Virginia Smith. Federated optimization in heterogeneous networks. Pro- ceedings of Machine Learning and Systems, 2:429-450, 2020.","title":"Federated optimization in heterogeneous networks","authors":"Tian Li, Anit Kumar Sahu, Manzil Zaheer, Maziar Sanjabi, Ameet Talwalkar, Virginia Smith","year":"2020","venue":"Proceedings of Machine Learning and Systems","doi":null,"url":null,"internal_url":null},{"position":21,"raw":"[Li et al., 2021a] Qinbin Li, Bingsheng He, and Dawn Song. Model-contrastive federated learning. In CVPR, pages 10713-10722, 2021.","title":"Bingsheng He, and Dawn Song. Model-contrastive federated learning","authors":"Li","year":"2021","venue":"CVPR","doi":null,"url":null,"internal_url":null},{"position":22,"raw":"[Li et al., 2021b] Qinbin Li, Zeyi Wen, Zhaomin Wu, Sixu Hu, Naibo Wang, Yuan Li, Xu Liu, and Bingsheng He. A survey on federated learning systems: Vision, hype and reality for data privacy and protection. IEEE T-KDE, 2021.","title":"A survey on federated learning systems: Vision, hype and reality for data privacy and protection","authors":"Li","year":"2021","venue":"IEEE T-KDE","doi":null,"url":null,"internal_url":null},{"position":23,"raw":"[Li et al., 2021c] Rui Li, Xin Yuan, Mohsen Radfar, Pe- ter Marendy, Wei Ni, Terrence J O'Brien, and Pablo M Casillas-Espinosa. Graph signal processing, graph neu- ral network and graph learning on biological data: a sys- tematic review. IEEE Reviews in Biomedical Engineering, 16:109-135, 2021.","title":"Graph signal processing, graph neural network and graph learning on biological data: a systematic review","authors":"Li","year":"2021","venue":"IEEE Reviews in Biomedical Engineering","doi":null,"url":null,"internal_url":null},{"position":24,"raw":"[Li et al., 2023a] Xingfeng Li, Zhenwen Ren, Quansen Sun, and Zhi Xu. Auto-weighted tensor schatten p-norm for robust multi-view graph clustering. Pattern Recognition, 134:109083, 2023.","title":"Auto-weighted tensor schatten p-norm for robust multi-view graph clustering","authors":"Li","year":"2023","venue":"Pattern Recognition","doi":null,"url":null,"internal_url":null},{"position":25,"raw":"[Li et al., 2023b] Xingfeng Li, Yinghui Sun, Quansen Sun, Zhenwen Ren, and Yuan Sun. Cross-view graph matching guided anchor alignment for incomplete multi-view clus- tering. Information Fusion, 100:101941, 2023.","title":"Cross-view graph matching guided anchor alignment for incomplete multi-view clustering","authors":"Li","year":"2023","venue":"Information Fusion","doi":null,"url":null,"internal_url":null},{"position":26,"raw":"[Liu et al., 2022] Rui Liu, Pengwei Xing, Zichao Deng, An- ran Li, Cuntai Guan, and Han Yu. Federated graph neu- ral networks: Overview, techniques and challenges. arXiv preprint arXiv:2202.07256, 2022.","title":"Federated graph neural networks: Overview, techniques and challenges","authors":"Rui Liu, Pengwei Xing, Zichao Deng, Anran Li, Cuntai Guan, Han Yu","year":"2022","venue":null,"doi":null,"url":null,"internal_url":null},{"position":27,"raw":"[Liu et al., 2023] Yixin Liu, Kaize Ding, Qinghua Lu, Fuyi Li, Leo Yu Zhang, and Shirui Pan. Towards self- interpretable graph-level anomaly detection. NeurIPS, 36, 2023.","title":"Towards selfinterpretable graph-level anomaly detection","authors":"Yixin Liu, Kaize Ding, Qinghua Lu, Fuyi Li, Leo Yu Zhang, Shirui Pan","year":"2023","venue":"NeurIPS","doi":null,"url":null,"internal_url":null},{"position":28,"raw":"[Ma et al., 2021] Xiaoxiao Ma, Jia Wu, Shan Xue, Jian Yang, Chuan Zhou, Quan Z Sheng, Hui Xiong, and Le- man Akoglu. A comprehensive survey on graph anomaly detection with deep learning. IEEE T-KDE, 2021.","title":"A comprehensive survey on graph anomaly detection with deep learning","authors":"Xiaoxiao Ma, Jia Wu, Shan Xue, Jian Yang, Chuan Zhou, Z Quan, Hui Sheng, Leman Xiong, Akoglu","year":"2021","venue":"IEEE T-KDE","doi":null,"url":null,"internal_url":null},{"position":29,"raw":"[Ma et al., 2022] Rongrong Ma, Guansong Pang, Ling Chen, and Anton van den Hengel. Deep graph-level anomaly de- tection by glocal knowledge distillation. In WSDM, pages 704-714, 2022.","title":"Deep graph-level anomaly detection by glocal knowledge distillation","authors":"Rongrong Ma, Guansong Pang, Ling Chen, Anton Van Den, Hengel","year":"2022","venue":"WSDM","doi":null,"url":null,"internal_url":null},{"position":30,"raw":"[Ma et al., 2023] Xiaoxiao Ma, Jia Wu, Jian Yang, and Quan Z Sheng. Towards graph-level anomaly detection via deep evolutionary mapping. In SIGKDD, pages 1631- 1642, 2023.","title":"Towards graph-level anomaly detection via deep evolutionary mapping","authors":"Xiaoxiao Ma, Jia Wu, Jian Yang, Quan, Sheng","year":"2023","venue":"SIGKDD","doi":null,"url":null,"internal_url":null},{"position":31,"raw":"[McMahan et al., 2017] Brendan McMahan, Eider Moore, Daniel Ramage, Seth Hampson, and Blaise Aguera y Ar- cas. Communication-efficient learning of deep networks from decentralized data. In AISTATS, pages 1273-1282.","title":"Communication-efficient learning of deep networks from decentralized data","authors":"Brendan Mcmahan, Eider Moore, Daniel Ramage, Seth Hampson, Blaise Aguera Y Arcas","year":null,"venue":"AISTATS","doi":null,"url":null,"internal_url":null},{"position":32,"raw":"PMLR, 2017.","title":null,"authors":"Pmlr","year":"2017","venue":null,"doi":null,"url":null,"internal_url":null},{"position":33,"raw":"[Pang et al., 2021] Guansong Pang, Chunhua Shen, Long- bing Cao, and Anton Van Den Hengel. Deep learning for anomaly detection: A review. ACM Computing Surveys (CSUR), 54(2):1-38, 2021.","title":"Deep learning for anomaly detection: A review","authors":"Pang","year":"2021","venue":"ACM Computing Surveys (CSUR)","doi":null,"url":null,"internal_url":null},{"position":34,"raw":"[Park et al., 2019] Wonpyo Park, Dongju Kim, Yan Lu, and Minsu Cho. Relational knowledge distillation. In CVPR, pages 3967-3976, 2019.","title":"Relational knowledge distillation","authors":"Park","year":"2019","venue":"CVPR","doi":null,"url":null,"internal_url":null},{"position":35,"raw":"[Pinelli et al., 2023] Fabio Pinelli, Gabriele Tolomei, and Giovanni Trappolini. Flirt: Federated learning for infor- mation retrieval. In SIGIR, pages 3472-3475, 2023.","title":"Flirt: Federated learning for information retrieval","authors":"Fabio Pinelli, Gabriele Tolomei, Giovanni Trappolini","year":"2023","venue":"SIGIR","doi":null,"url":null,"internal_url":null},{"position":36,"raw":"[Qiao et al., 2023] Dong Qiao, Chris Ding, and Jicong Fan. Federated spectral clustering via secure similarity recon- struction. In NeurIPS, 2023.","title":"Federated spectral clustering via secure similarity reconstruction","authors":"Dong Qiao, Chris Ding, Jicong Fan","year":"2023","venue":"NeurIPS","doi":null,"url":null,"internal_url":null},{"position":37,"raw":"[Qiu et al., 2022] Chen Qiu, Marius Kloft, Stephan Mandt, and Maja Rudolph. Raising the bar in graph-level anomaly detection. In IJCAI, pages 2196-2203, 2022.","title":"Raising the bar in graph-level anomaly detection","authors":"Chen Qiu, Marius Kloft, Stephan Mandt, Maja Rudolph","year":"2022","venue":"IJCAI","doi":null,"url":null,"internal_url":"/proceedings/2022/305"},{"position":38,"raw":"[Ruff et al., 2018] Lukas Ruff, Robert Vandermeulen, Nico Goernitz, Lucas Deecke, Shoaib Ahmed Siddiqui, Alexan- der Binder, Emmanuel Müller, and Marius Kloft. Deep one-class classification. In ICML, pages 4393-4402.","title":"Deep one-class classification","authors":"Lukas Ruff, Robert Vandermeulen, Nico Goernitz, Lucas Deecke, Ahmed Shoaib, Alexander Siddiqui, Emmanuel Binder, Marius Müller, Kloft","year":null,"venue":"ICML","doi":null,"url":null,"internal_url":null},{"position":39,"raw":"PMLR, 2018.","title":null,"authors":"Pmlr","year":"2018","venue":null,"doi":null,"url":null,"internal_url":null},{"position":40,"raw":"[Sun and Fan, 2024] Yan Sun and Jicong Fan. Mmd graph kernel: Effective metric learning for graphs via maximum mean discrepancy. In ICLR, 2024.","title":"Mmd graph kernel: Effective metric learning for graphs via maximum mean discrepancy","authors":"Yan Sun, Jicong Fan","year":"2024","venue":"ICLR","doi":null,"url":null,"internal_url":null},{"position":41,"raw":"[Sun et al., 2023a] Yuan Sun, Zhenwen Ren, Peng Hu, Dezhong Peng, and Xu Wang. Hierarchical consensus hashing for cross-modal retrieval. IEEE T-MM, 2023.","title":"Hierarchical consensus hashing for cross-modal retrieval","authors":"Sun","year":"2023","venue":"IEEE T-MM","doi":null,"url":null,"internal_url":null},{"position":42,"raw":"[Sun et al., 2023b] Yuan Sun, Xu Wang, Dezhong Peng, Zhenwen Ren, and Xiaobo Shen. Hierarchical hashing learning for image set classification. IEEE T-IP, 32:1732- 1744, 2023.","title":"Hierarchical hashing learning for image set classification","authors":"Sun","year":"2023","venue":"IEEE T-IP","doi":null,"url":null,"internal_url":null},{"position":43,"raw":"[Sun et al., 2024] Ziheng Sun, Chris Ding, and Jicong Fan. Lovász principle for unsupervised graph representation learning. NeurIPS, 36, 2024.","title":"Ziheng Sun, Chris Ding, and Jicong Fan. Lovász principle for unsupervised graph representation learning","authors":"Sun","year":"2024","venue":"NeurIPS","doi":null,"url":null,"internal_url":null},{"position":44,"raw":"[Tan et al., 2023] Yue Tan, Yixin Liu, Guodong Long, Jing Jiang, Qinghua Lu, and Chengqi Zhang. Federated learn- ing on non-iid graphs via structural knowledge sharing. In AAAI, volume 37, pages 9953-9961, 2023.","title":"Federated learning on non-iid graphs via structural knowledge sharing","authors":"Yue Tan, Yixin Liu, Guodong Long, Jing Jiang, Qinghua Lu, Chengqi Zhang","year":"2023","venue":"AAAI","doi":null,"url":null,"internal_url":null},{"position":45,"raw":"[Van der Maaten and Hinton, 2008] Laurens Van der Maaten and Geoffrey Hinton. Visualizing data using t-sne. JMLR, 9(11):2579-2605, 2008.","title":"Visualizing data using t-sne","authors":"Van Der Maaten, Laurens Hinton, Geoffrey Van Der Maaten, Hinton","year":"2008","venue":"JMLR","doi":null,"url":null,"internal_url":null},{"position":46,"raw":"[Wang et al., 2022] Zhen Wang, Weirui Kuang, Yuexiang Xie, Liuyi Yao, Yaliang Li, Bolin Ding, and Jingren Zhou. Federatedscope-gnn: Towards a unified, comprehensive and efficient package for federated graph learning. In SIGKDD, pages 4110-4120, 2022.","title":"Federatedscope-gnn: Towards a unified, comprehensive and efficient package for federated graph learning","authors":"Zhen Wang, Weirui Kuang, Yuexiang Xie, Liuyi Yao, Yaliang Li, Bolin Ding, Jingren Zhou","year":"2022","venue":"SIGKDD","doi":null,"url":null,"internal_url":null},{"position":47,"raw":"[Wu et al., 2024a] Zhihao Wu, Zhaoliang Chen, Shide Du, Sujia Huang, and Shiping Wang. Graph convolutional network with elastic topology. Pattern Recognition, page 110364, 2024.","title":"Graph convolutional network with elastic topology","authors":"Wu","year":"2024","venue":"Pattern Recognition","doi":null,"url":null,"internal_url":null},{"position":48,"raw":"[Wu et al., 2024b] Zhihao Wu, Zhao Zhang, and Jicong Fan. Graph convolutional kernel machine versus graph convo- lutional networks. NeurIPS, 36, 2024.","title":"Graph convolutional kernel machine versus graph convolutional networks","authors":"Wu","year":"2024","venue":"NeurIPS","doi":null,"url":null,"internal_url":null},{"position":49,"raw":"[Xie et al., 2021] Han Xie, Jing Ma, Li Xiong, and Carl Yang. Federated graph classification over non-iid graphs. NeurIPS, 34:18839-18852, 2021.","title":"Federated graph classification over non-iid graphs","authors":"Han Xie, Jing Ma, Li Xiong, Carl Yang","year":"2021","venue":"NeurIPS","doi":null,"url":null,"internal_url":null},{"position":50,"raw":"[Xu et al., 2019] Keyulu Xu, Weihua Hu, Jure Leskovec, and Stefanie Jegelka. How powerful are graph neural net- works? In ICLR, 2019.","title":"How powerful are graph neural networks?","authors":"Keyulu Xu, Weihua Hu, Jure Leskovec, Stefanie Jegelka","year":"2019","venue":"ICLR","doi":null,"url":null,"internal_url":null},{"position":51,"raw":"[Zhang et al., 2021] Huanding Zhang, Tao Shen, Fei Wu, Mingyang Yin, Hongxia Yang, and Chao Wu. Fed- erated graph learning-a position paper. arXiv preprint arXiv:2105.11099, 2021.","title":"Federated graph learning-a position paper","authors":"Huanding Zhang, Tao Shen, Fei Wu, Mingyang Yin, Hongxia Yang, Chao Wu","year":"2021","venue":null,"doi":null,"url":null,"internal_url":null},{"position":52,"raw":"[Zhang et al., 2022] Chao Zhang, Huaxiong Li, Caihua Chen, Xiuyi Jia, and Chunlin Chen. Low-rank tensor reg- ularized views recovery for incomplete multiview cluster- ing. IEEE T-NNLS, 2022.","title":"Low-rank tensor regularized views recovery for incomplete multiview clustering","authors":"Chao Zhang, Huaxiong Li, Caihua Chen, Xiuyi Jia, Chunlin Chen","year":"2022","venue":"IEEE T-NNLS","doi":null,"url":null,"internal_url":null},{"position":53,"raw":"[Zhang et al., 2023] Lei Zhang, Lele Fu, Tong Wang, Chuan Chen, and Chuanfu Zhang. Mutual information-driven multi-view clustering. In CIKM, pages 3268-3277, 2023.","title":"Mutual information-driven multi-view clustering","authors":"Lei Zhang, Lele Fu, Tong Wang, Chuan Chen, Chuanfu Zhang","year":"2023","venue":"CIKM","doi":null,"url":null,"internal_url":null},{"position":54,"raw":"[Zhang et al., 2024a] Lei Zhang, Lele Fu, Chen Liu, Zhao Yang, Jinghua Yang, Zibin Zheng, and Chuan Chen. To- wards few-label vertical federated learning. ACM T-KDD, 2024.","title":"Towards few-label vertical federated learning","authors":"Zhang","year":"2024","venue":"ACM T-KDD","doi":null,"url":null,"internal_url":null},{"position":55,"raw":"[Zhang et al., 2024b] Yunhe Zhang, Yan Sun, Jinyu Cai, and Jicong Fan. Deep orthogonal hypersphere compression for anomaly detection. In ICLR, 2024.","title":"Deep orthogonal hypersphere compression for anomaly detection","authors":"Zhang","year":"2024","venue":"ICLR","doi":null,"url":null,"internal_url":null},{"position":56,"raw":"[Zhao and Akoglu, 2021] Lingxiao Zhao and Leman Akoglu. On using classification datasets to evaluate graph outlier detection: Peculiar observations and new insights. Big Data, 2021.","title":"On using classification datasets to evaluate graph outlier detection: Peculiar observations and new insights","authors":"Akoglu Zhao, Lingxiao Zhao, Leman Akoglu","year":"2021","venue":"Big Data","doi":null,"url":null,"internal_url":null},{"position":57,"raw":"[Zhao et al., 2023] Yue Zhao, Guoqing Zheng, Subhabrata Mukherjee, Robert McCann, and Ahmed Awadallah. Ad- moe: Anomaly detection with mixture-of-experts from noisy labels. In AAAI, volume 37, pages 4937-4945, 2023.","title":"Admoe: Anomaly detection with mixture-of-experts from noisy labels","authors":"Yue Zhao, Guoqing Zheng, Subhabrata Mukherjee, Robert Mccann, Ahmed Awadallah","year":"2023","venue":"AAAI","doi":null,"url":null,"internal_url":null},{"position":58,"raw":"[Zheng et al., 2021] Yu Zheng, Ming Jin, Yixin Liu, Lianhua Chi, Khoa T Phan, and Yi-Ping Phoebe Chen. Generative and contrastive self-supervised learning for graph anomaly detection. IEEE T-KDE, 2021.","title":"Generative and contrastive self-supervised learning for graph anomaly detection","authors":"Yu Zheng, Ming Jin, Yixin Liu, Lianhua Chi, Yi-Ping Phoebe Khoa T Phan, Chen","year":"2021","venue":"IEEE T-KDE","doi":null,"url":null,"internal_url":null}],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}