[19] Dmitry Krotov and John J. Hopfield. Large associative memory problem in neurobiology and machine learning. In International Conference on Learning Representations, 2021.
[20] Fei Tang and Michael Kopp. A remark on a paper of krotov and hopfield [arxiv: 2008.06996]. arXiv preprint arXiv:2105.15034, 2021.
[21] Dmitry Krotov. Hierarchical associative memory. arXiv preprint arXiv:2107.06446, 2021.
[22] Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei. Imagenet: A large-scale hierarchical image database. In 2009 IEEE conference on computer vision and pattern recognition, pages 248–255. IEEE, 2009.
[23] Matthew D Zeiler and Rob Fergus. Visualizing and understanding convolutional networks. In European conference on computer vision, pages 818-833. Springer, 2014.
[24] Jianheng Tang, Jiajin Li, Ziqi Gao, and Jia Li. Rethinking graph neural networks for anomaly detection. arXiv preprint arXiv:2205.15508, 2022.
[25] Thomas N Kipf and Max Welling. Semi-supervised classification with graph convolutional networks. arXiv preprint arXiv:1609.02907, 2016.
[26] Kaize Ding, Jundong Li, Rohit Bhanushali, and Huan Liu. Deep anomaly detection on attributed networks. In Proceedings of the 2019 SIAM International Conference on Data Mining, pages 594-602. SIAM, 2019.
[27] Zhen Peng, Minnan Luo, Jundong Li, Luguo Xue, and Qinghua Zheng. A deep multi-view framework for anomaly detection on attributed networks. IEEE Transactions on Knowledge and Data Engineering, 2020.
[28] Felix Wu, Amauri Souza, Tianyi Zhang, Christopher Fifty, Tao Yu, and Kilian Weinberger. Simplifying graph convolutional networks. In International conference on machine learning, pages 6861-6871. PMLR, 2019.
[29] Shebuti Rayana and Leman Akoglu. Collective opinion spam detection: Bridging review networks and metadata. In Proceedings of the 21th acm sigkdd international conference on knowledge discovery and data mining, pages 985-994, 2015.
[30] Julian John McAuley and Jure Leskovec. From amateurs to connoisseurs: modeling the evolution of user expertise through online reviews. In Proceedings of the 22nd international conference on World Wide Web, pages 897-908, 2013.
[31] Zhiwei Liu, Yingtong Dou, Philip S Yu, Yutong Deng, and Hao Peng. Alleviating the inconsistency problem of applying graph neural network to fraud detection. In Proceedings of the 43rd international ACM SIGIR conference on research and development in information retrieval, pages 1569-1572, 2020.
[32] Yingtong Dou, Zhiwei Liu, Li Sun, Yutong Deng, Hao Peng, and Philip S Yu. Enhancing graph neural network-based fraud detectors against camouflaged fraudsters. In Proceedings of the 29th ACM International Conference on Information & Knowledge Management, pages 315-324, 2020.
[33] 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.
[34] Vijay Prakash Dwivedi and Xavier Bresson. A generalization of transformer networks to graphs. arXiv preprint arXiv:2012.09699, 2020.
[35] Jesse Davis and Mark Goadrich. The relationship between precision-recall and roc curves. In Proceedings of the 23rd international conference on Machine learning, pages 233-240, 2006.
[36] Christopher Morris, Nils M. Kriege, Franka Bause, Kristian Kersting, Petra Mutzel, and Marion Neumann. Tudataset: A collection of benchmark datasets for learning with graphs. CoRR, abs/2007.08663, 2020.