ABSTRACT
Anomaly detection on graphs plays a significant role in various domains, including cybersecurity, e-commerce, and financial fraud detection. However, existing methods on graph anomaly detection usually consider the view in a single scale of graphs, which results in their limited capability to capture the anomalous patterns from different perspectives. Towards this end, we introduce a novel graph anomaly detection framework, namely ANEMONE, to simultaneously identify the anomalies in multiple graph scales. Concretely, ANEMONE first leverages a graph neural network backbone encoder with multi-scale contrastive learning objectives to capture the pattern distribution of graph data by learning the agreements between instances at the patch and context levels concurrently. Then, our method employs a statistical anomaly estimator to evaluate the abnormality of each node according to the degree of agreement from multiple perspectives. Experiments on three benchmark datasets demonstrate the superiority of our method.
- Kaize Ding, Jundong Li, Rohit Bhanushali, and Huan Liu. 2019. Deep anomaly detection on attributed networks. In SDM. SIAM, 594--602.Google Scholar
- Ming Jin, Yizhen Zheng, Yuan-Fang Li, Chen Gong, Chuan Zhou, and Shirui Pan. 2021. Multi-Scale Contrastive Siamese Networks for Self-Supervised Graph Representation Learning. In IJCAI.Google Scholar
- Thomas N. Kipf and Max Welling. 2017. Semi-Supervised Classification with Graph Convolutional Networks. In ICLR. 1--14.Google Scholar
- Jundong Li, Harsh Dani, Xia Hu, and Huan Liu. 2017. Radar: Residual Analysis for Anomaly Detection in Attributed Networks. In IJCAI. 2152--2158. Google ScholarDigital Library
- Yuening Li, Xiao Huang, Jundong Li, Mengnan Du, and Na Zou. 2019. Specae: Spectral autoencoder for anomaly detection in attributed networks. In CIKM. 2233--2236. Google ScholarDigital Library
- Yixin Liu, Zhao Li, Shirui Pan, Chen Gong, Chuan Zhou, and George Karypis. 2021 a. Anomaly Detection on Attributed Networks via Contrastive Self-Supervised Learning. IEEE TNNLS (2021).Google Scholar
- Yixin Liu, Shirui Pan, Ming Jin, Chuan Zhou, Feng Xia, and Philip S. Yu. 2021 b. Graph Self-Supervised Learning: A Survey. arXiv preprint arXiv:2103.00111 (2021).Google Scholar
- Yixin Liu, Shirui Pan, Yu Guang Wang, Fei Xiong, Liang Wang, and Vincent Lee. 2021 c. Anomaly Detection in Dynamic Graphs via Transformer. arXiv preprint arXiv:2106.09876 (2021).Google Scholar
- Xiaoxiao Ma, Jia Wu, Shan Xue, Jian Yang, Quan Z Sheng, and Hui Xiong. 2021. A Comprehensive Survey on Graph Anomaly Detection with Deep Learning. arXiv preprint arXiv:2106.07178 (2021).Google Scholar
- Zhen Peng, Minnan Luo, Jundong Li, Huan Liu, and Qinghua Zheng. 2018. ANOMALOUS: A Joint Modeling Approach for Anomaly Detection on Attributed Networks. In IJCAI. 3513--3519. Google ScholarDigital Library
- Bryan Perozzi and Leman Akoglu. 2016. Scalable anomaly ranking of attributed neighborhoods. In SDM. SIAM, 207--215.Google Scholar
- Hanghang Tong, Christos Faloutsos, and Jia-Yu Pan. 2006. Fast random walk with restart and its applications. In ICDM. IEEE, 613--622. Google ScholarDigital Library
- William Fedus, William L. Hamilton, Pietro Liò, Yoshua Bengio, and R Devon Hjelm. 2019. Deep Graph Infomax. In ICLR.Google Scholar
- Zonghan Wu, Shirui Pan, Fengwen Chen, Guodong Long, Chengqi Zhang, and S Yu Philip. 2020. A comprehensive survey on graph neural networks. IEEE TNNLS, Vol. 32, 1 (2020), 4--24.Google Scholar
- Miao Zhang, Steven Su, Shirui Pan, Xiaojun Chang, Ehsan Abbasnejad, and Reza Haffari. 2021. iDARTS: Differentiable Architecture Search with Stochastic Implicit Gradients. arXiv preprint arXiv:2106.10784 (2021).Google Scholar
- Yizhen Zheng, V. Lee, Zonghan Wu, and Shirui Pan. 2021. Heterogeneous Graph Attention Network for Small and Medium-Sized Enterprises Bankruptcy Prediction. In PAKDD.Google Scholar
Index Terms
- ANEMONE: Graph Anomaly Detection with Multi-Scale Contrastive Learning
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