NeiGAD: Augmenting Graph Anomaly Detection via Spectral Neighbor Information

TL;DR

NeiGAD introduces a spectral graph analysis module that enhances graph anomaly detection by explicitly modeling neighbor interactions, leading to improved accuracy over existing methods.

Contribution

The paper proposes NeiGAD, a spectral neighbor information module for GAD, which explicitly captures local interactions and boosts detection performance.

Findings

NeiGAD improves detection accuracy on eight real-world datasets.

Spectral analysis of eigenvectors encodes local neighbor interactions.

Explicit neighbor modeling enhances anomaly detection effectiveness.

Abstract

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.