AI RESEARCH
NeiGAD: Augmenting Graph Anomaly Detection via Spectral Neighbor Information
arXiv CS.AI
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ArXi:2603.28300v1 Announce Type: cross Graph anomaly detection (GAD) aims to identify irregular nodes or structures in attributed graphs. Neighbor information, which reflects both structural connectivity and attribute consistency with surrounding nodes, is essential for distinguishing anomalies from normal patterns. Although recent graph neural network (GNN)-based methods incorporate such information through message passing, they often fail to explicitly model its effect or interaction with attributes, limiting detection performance. This work.