🤖 AI Summary
This work addresses the limitations of existing unsupervised graph anomaly detection methods, which often rely on homophily assumptions and thus underperform on heterophilous graphs. The authors propose NK-GAD, a novel framework that, for the first time, uncovers two key properties in attribute-heterophilous graphs: the convergence of attribute similarity distributions among connected nodes and distinctive patterns in spectral energy variation. Leveraging these insights, NK-GAD introduces a neighbor knowledge enhancement mechanism that employs a joint encoder to integrate information from both similar and dissimilar neighbors. The framework further incorporates neighbor reconstruction, central node aggregation, and dual attribute–structure decoders to enable collaborative reconstruction for effective anomaly detection. Evaluated on seven benchmark datasets, NK-GAD achieves an average AUC improvement of 3.29%, significantly outperforming current state-of-the-art methods.
📝 Abstract
Graph anomaly detection aims to identify irregular patterns in graph-structured data. Most unsupervised GNN-based methods rely on the homophily assumption that connected nodes share similar attributes. However, real-world graphs often exhibit attribute-level heterophily, where connected nodes have dissimilar attributes. Our analysis of attribute-level heterophily graphs reveals two phenomena indicating that current approaches are not practical for unsupervised graph anomaly detection: 1) attribute similarities between connected nodes show nearly identical distributions across different connected node pair types, and 2) anomalies cause consistent variation trends between the graph with and without anomalous edges in the low- and high-frequency components of the spectral energy distributions, while the mid-part exhibits more erratic variations. Based on these observations, we propose NK-GAD, a neighbor knowledge-enhanced unsupervised graph anomaly detection framework. NK-GAD integrates a joint encoder capturing both similar and dissimilar neighbor features, a neighbor reconstruction module modeling normal distributions, a center aggregation module refining node features, and dual decoders for reconstructing attributes and structures. Experiments on seven datasets show NK-GAD achieves an average 3.29\% AUC improvement.