通过谱分析增强图异常检测，提升识别精度
NeiGAD: Augmenting Graph Anomaly Detection via Spectral Neighbor Information
- 利用邻接矩阵特征向量捕捉局部邻居交互信息
- 在8个真实数据集上准确率超越现有方法
- 模块可即插即用，适合图神经网络异常检测场景
图异常检测（GAD）旨在识别属性图中的异常节点或结构。邻居信息反映节点的结构连接性与邻近节点的属性一致性，对区分异常与正常模式至关重要。尽管基于图神经网络（GNN）的方法通过消息传递融合此类信息，但常未能显式建模其作用或与属性的交互，限制了检测性能。本文提出NeiGAD，一种新型即插即用模块，通过谱图分析捕获邻居信息。理论分析表明，邻接矩阵的特征向量编码了局部邻居交互，并逐步放大异常信号。基于此，NeiGAD选取一组紧凑特征向量构建高效且判别性强的表示。在八个真实世界数据集上的实验显示，NeiGAD持续提升检测准确率，优于当前最优的GAD方法。结果验证了显式建模邻居信息的重要性及谱分析在异常检测中的有效性。代码已公开：https://github.com/huafeihuang/NeiGAD。
原文摘要 · Abstract (English)
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.
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