ADA-GAD: Anomaly-Denoised Autoencoders for Graph Anomaly Detection
Junwei He,
Qianqian Xu,
Yangbangyan Jiang
et al.
Abstract:Graph anomaly detection is crucial for identifying nodes that deviate from regular behavior within graphs, benefiting various domains such as fraud detection and social network. Although existing reconstruction-based methods have achieved considerable success, they may face the Anomaly Overfitting and Homophily Trap problems caused by the abnormal patterns in the graph, breaking the assumption that normal nodes are often better reconstructed than abnormal ones. Our observations indicate that models trained on … Show more
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