NeiGAD: Augmenting Graph Anomaly Detection via Spectral Neighbor Information
This addresses the limitation of existing GNN-based methods in graph anomaly detection for applications like fraud detection or network security, though it is incremental as it builds on spectral analysis techniques.
The paper tackled the problem of graph anomaly detection by introducing NeiGAD, a plug-and-play module that uses spectral graph analysis to explicitly model neighbor information, resulting in improved detection accuracy that outperforms state-of-the-art methods on eight real-world datasets.
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 introduces NeiGAD, a novel plug-and-play module that captures neighbor information through spectral graph analysis. Theoretical insights demonstrate that eigenvectors of the adjacency matrix encode local neighbor interactions and progressively amplify anomaly signals. Based on this, NeiGAD selects a compact set of eigenvectors to construct efficient and discriminative representations. Experiments on eight real-world datasets show that NeiGAD consistently improves detection accuracy and outperforms state-of-the-art GAD methods. These results demonstrate the importance of explicit neighbor modeling and the effectiveness of spectral analysis in anomaly detection. Code is available at: https://github.com/huafeihuang/NeiGAD.