Breaking Semantic Barriers: A Zero-Shot Generalized Framework for Graph Anomaly Detection

Cross-domain graph anomaly detection (GAD) aims to identify nodes that significantly deviate from normal patterns in unseen target domains, showing great potential in applications such as multimedia content security and financial risk control. However, existing methods often rely on semantic information trained on individual datasets, which makes it difficult to capture node commonalities across domains and limits generalization in complex multimedia environments. To address these challenges, we propose Zero-GAD, a universal Zero-shot Graph Anomaly Detection framework tailored for cross-domain scenarios. Zero-GAD leverages a novel de-semanticized strategy to train a unified detection model that can be directly applied to unseen domains without retraining or fine-tuning. The framework is built upon two key components: (1) a Global Information Unification Module, which projects graph data into the spectral domain and performs normalization to align the energy distribution in the frequency space; and (2) a Node-Neutralized Discrepancy Scoring Module that leverages the discrepancy between the original and reconstructed node representations to produce effective anomaly scores. Extensive experiments show that Zero-GAD achieves superior accuracy compared to existing models under a GAD setting.