Generate and Filter: A GNN-Based Approach for Graph Anomaly Detection

Graph anomaly detection (GAD) aims to distinguish anomalies from the majority of normal nodes in graph-structured data. Due to its extensive real-world applications, GAD has garnered increasing attention from both academia and industry. Recently, Graph Neural Networks (GNNs) have been integrated into GAD frameworks, yielding promising results by effectively characterizing structural information. However, existing GNN based methods suffer from several critical limitations: (I) the difficulty of learning discriminative representations for anomalies in the feature space; (II) the structural sparsity caused by the lack of essential connections between anomalies; and (III) the camouflage effect resulting from redundant edges between anomalies and normal nodes. To address these challenges, we propose a novel framework, Generate and Filter graph learning for Graph Anomaly Detection (GFGAD). Specifically, GFGAD first generates a diverse set of synthetic anomalies with enriched feature and structural information to balance the data distribution. Subsequently, these generated anomalies are strategically connected to original ones to compensate for missing structural patterns, while a filtering mechanism is employed to eliminate redundant connections and mitigate camouflage. Extensive experiments on several benchmark datasets demonstrate that GFGAD significantly outperforms state-of-the-art baselines.