Normality Learning-based Graph Anomaly Detection via Multi-Scale Contrastive Learning
Jingcan Duan,
Pei Zhang,
Siwei Wang
et al.
Abstract:Graph anomaly detection (GAD) has attracted increasing attention in machine learning and data mining. Recent works have mainly focused on how to capture richer information to improve the quality of node embeddings for GAD. Despite their significant advances in detection performance, there is still a relative dearth of research on the properties of the task. GAD aims to discern the anomalies that deviate from most nodes. However, the model is prone to learn the pattern of normal samples which make up the majori… Show more
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