TIEGGAD: TARGET-DOMAIN INFORMATION ENHANCEMENT FOR GENERALIST GRAPH ANOMALY DETECTION

Generalist Graph Anomaly Detection (GGAD) aims to detect anomalies in unseen target graphs using a detector trained on source graphs. However, cross-graph discrepancies may limit the detector’s cross-graph generalization ability, leaving anomaly-relevant information in the target domain insufficiently captured. We summarize this bottleneck as Target-domain Anomaly Information Deficiency (TAID). To mitigate TAID, we propose Target-domain Information Enhancement for Generalist Graph Anomaly Detection (TIEGGAD), a multi-scale structural and cross-view hypergraph enhancement framework for zero-shot GGAD. To strengthen structural modeling and complement semantic information, we construct a degree-guided multi-scale structural representation to capture structural deviations across different neighborhood scales. Furthermore, to refine the overall representation using target-domain information, we introduce a training-free cross-view hypergraph module that enables bidirectional information exchange between the semantic and structural views. Finally, parameterized vector-level integration is used to combine semantic, structural, and hypergraph-enhanced representations into a unified representation. The proposed framework requires neither target-domain labels nor retraining. Experiments on multiple real-world graph datasets demonstrate stable and competitive performance for zero-shot cross-domain node anomaly detection, while ablation and mechanism analyses further verify the effectiveness of multi-scale structural modeling and cross-view hypergraph enhancement.