Graph Anomaly Detection with Bi-level Optimization
Author:
Affiliation:
1. Alibaba Group, Beijing, China
2. University of Science and Technology of China, Hefei, China
3. Academy of Cyber, Beijing, China
4. Beijing Electronic Science and Technology Institute, Beijing, China
Funder
National Natural Science Foundation of China
Publisher
ACM
Link
https://dl.acm.org/doi/pdf/10.1145/3589334.3645673
Reference69 articles.
1. Sami Abu-El-Haija, Bryan Perozzi, Amol Kapoor, Nazanin Alipourfard, Kristina Lerman, Hrayr Harutyunyan, Greg Ver Steeg, and Aram Galstyan. 2019. Mixhop: Higher-order graph convolutional architectures via sparsified neighborhood mixing. In ICML. 21--29.
2. Yoshua Bengio, Nicholas Lé onard, and Aaron C. Courville. 2013. Estimating or Propagating Gradients Through Stochastic Neurons for Conditional Computation. CoRR , Vol. abs/1308.3432 (2013).
3. Deyu Bo Xiao Wang Chuan Shi and Huawei Shen. 2021. Beyond low-frequency information in graph convolutional networks. In AAAI. 3950--3957.
4. Ziwei Chai Siqi You Yang Yang Shiliang Pu Jiarong Xu Haoyang Cai and Weihao Jiang. 2022. Can Abnormality be Detected by Graph Neural Networks?. In IJCAI. 1945--1951.
5. Sudhanshu Chanpuriya and Cameron Musco. 2022. Simplified Graph Convolution with Heterophily. In NeurIPS.
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