Quantization-conditioned Normalizing Flows for Multi-granularity Graph Anomaly Detection

Multi-granularity Graph Anomaly Detection aims to design a unified framework for detecting abnormal patterns at node, edge, and subgraph granularities, occupying an important position in academia and industry. Existing methods usually focus on exploiting a unified representation space for different granularities, while overlooking the distinct characteristics and anomaly patterns across granularities, leading to poor identification ability. To address these limitations, a quantization-conditioned normalizing flow framework for multi-granularity GAD (QNMGAD) is proposed to tackle this challenge. On the one hand, QNMGAD introduces prototype quantization to capture discriminative semantics across granularities and utilizes residual patterns quantization to extract latent semantics within a specific granularity. On the other hand, QNMGAD introduces conditioned normalizing flows based on granularity prototypes to model the distribution of normal patterns and calculate density estimation to identify anomalies. Extensive experiments compared with state-of-the-art baselines show the superiority of QNMGAD.