Cross-Domain Graph Anomaly Detection via Fusion of Dynamic Prototypes and Self-Supervised Transfer Learning

Cross-domain graph anomaly detection aims to identify anomalies in a target-domain graph with limited labels, by leveraging both normal and anomalous samples from a source-domain graph. However, significant discrepancies in graph structures and feature distributions between different domains hinder effective knowledge transfer and limit cross-domain generalization. Moreover, the scarcity of labeled data in the target domain often induces ambiguous decision boundaries and unstable predictions during training. To address these challenges, we propose a cross-domain graph anomaly detection method that integrates dynamic prototypes with self-supervised transfer learning. Specifically, semantic prototypes are first constructed from the normal and anomaly samples in the source domain, and a bidirectional guidance strategy is then employed to align these prototypes with target-domain nodes in a shared semantic space, thereby alleviating cross-domain distribution shifts. To further enhance the stability and semantic discriminability, an exponential moving average mechanism is introduced for dynamic prototype updating. Subsequently, entropy minimization is applied to target-domain node embeddings to improve feature discriminability and reduce decision boundary ambiguity. Finally, pseudo anomaly scores can be well obtained by combining source-domain model predictions with unsupervised clustering results on the target domain data. Accordingly, high-confidence samples are selected as week supervision signals to iteratively refine the model, thereby improving both detection accuracy and robustness. Experimental results demonstrate that our method consistently outperforms state-of-the-art approaches in various cross-domain graph anomaly detection tasks, while validating its effectiveness and stability in cross-domain transfer scenarios.