Granularity-aware hierarchical information alignment for multi-granularity Graph Anomaly Detection

Multi-granularity graph anomaly detection is a recently emerging issue that aims to design a unified solution for the detection of multi-granularity anomalies in graphs. Existing methods usually employ subgraph sampling and unified training objectives, achieving promising performance. However, they ignore hierarchical information in latent representations, making the latent-space evolution less discriminative. Moreover, they fail to explore interdependencies across granularities in specific detection tasks, which could further regularize the representations. To address these limitations, we propose a granularity-aware hierarchical information alignment framework for multi-granularity GAD (GaHIGAD). Specifically, GaHIGAD explores latent evolutionary information about hidden representations based on Information Bottleneck (IB) theory. In addition, GaHIGAD integrates cross-granularity representations to exploit latent consistency dependencies and achieve representation regularization. Extensive experiments on comprehensive public datasets demonstrate the effectiveness and advancement of GaHIGAD compared to state-of-the-art baselines.