Selective Anomalous Information Filtering for Enhancing Unsupervised Graph Anomaly Detection

Unsupervised graph anomaly detection aims to identify irregular patterns in graph-structured data without relying on labeled anomalies. Graph neural networks (GNNs) have advanced GAD by learning effective graph representations through neighborhood aggregation. However, challenges such as anomalous information diffusion impede GNNs to accurately distinguish anomalies from normal ones. To bridge this gap, we propose a novel framework named Selective Anomalous information Filtering for Enhance unsupervised graph anomaly detection (SAFE). SAFE allows nodes to selectively filter anomalous information, preventing the spread of anomalous noise to normal nodes while allowing anomalies to assimilate features from their neighbors. This strategy enhances the reconstruction error disparity between normal and anomalous nodes, thereby improving the accuracy of anomaly detection. Extensive experiments on both synthetic and real-world datasets demonstrate the significant performance gains of SAFE over existing methods.