Graph Anomaly Detection via Feature Selection with Local Topological Residuals

Abstract

Graph anomaly detection (GAD) aims to identify nodes that exhibit significant deviations from expected structural or attribute patterns, and has garnered increasing attention in recent years. Recent approaches for GAD have predominantly focused on local inconsistency mining, which refers to the difficulty of establishing high similarity relationships between anomalous nodes and their neighbors. While local inconsistency mining requires the incorporation of topological information, the use of Graph Neural Networks (GNNs) to introduce such information tends to homogenize connected nodes, thereby causing the loss of local anomalous signals. To address this challenge, we propose LTRGAD, a two-stage GAD framework that performs feature selection based on local feature-topological residuals (LTR). By processing features separately, LTRGAD effectively introduces topological information while preserving the original local anomalous patterns, enabling more accurate local anomaly detection. Subsequently, global anomaly detection is conducted on the entire graph by leveraging the results from the local detection phase. Extensive experiments on seven benchmark datasets demonstrate the effectiveness of the proposed LTRGAD framework.