CR-Aug: Community Risk-Guided Adaptive Augmentation for Semi-supervised Graph Anomaly Detection

Abstract

Recently, semi-supervised graph anomaly detection (GAD) has garnered increasing attention under a challenging setting where only a limited number of normal nodes are labeled during training. To better exploit the limited normal supervision and compensate for the absence of real anomaly labels, existing methods often adopt a single, uniform anomaly modeling and pseudo-anomaly generation strategy applied across the entire graph, while overlooking the inherent heterogeneity among communities in graph data. Consequently, the generated pseudo-anomalies exhibit limited diversity and specificity, failing to represent the complex distributions of real-world anomalies. To address this challenge, we propose CR-Aug, a novel Community Risk-Guided Adaptive Augmentation framework, which is designed to overcome this limitation by leveraging community-specific prior knowledge more effectively. It comprises two core components: Community Risk Profiling (CRP) and Risk-Guided Synthesis (RGS). Specifically, CRP quantifies community-level risks by measuring the affinity discrepancy between the labeled normal subset and the overall community. Guided by the derived risk scores, RGS then dynamically adapts the generation process through risk-weighted sampling and adaptive mixing. This strategy facilitates the synthesis of diverse pseudo-anomalies, thereby providing the classifier with more discriminative supervisory signals. Extensive experiments on multiple benchmark datasets demonstrate that CR-Aug significantly outperforms state-of-the-art semi-supervised GAD methods, validating the effectiveness of incorporating community-level risk profiles.