🤖 AI Summary
This work addresses the limited generalization of existing graph anomaly detection models in cross-domain settings, which often stems from their reliance on source-domain-specific features and structural patterns. To overcome this challenge, the authors propose AlignGAD, a novel framework that enables zero-shot cross-domain graph anomaly detection without requiring any labeled or unlabeled data from the target domain. AlignGAD achieves this by aligning node features in the spectral domain to unify heterogeneous representations and normalize graph signals, constructing cluster-aware graph views, and aggregating anomaly evidence through multi-view node reconstruction discrepancies. Grounded in spectral graph theory, feature alignment, graph clustering, and multi-view self-supervised learning, AlignGAD demonstrates superior cross-domain generalization, significantly outperforming state-of-the-art methods across multiple real-world datasets.
📝 Abstract
Cross-domain graph anomaly detection (GAD) aims to identify abnormal nodes in unseen target graphs, showing strong potential in real-world applications with heterogeneous graph data. However, existing methods often depend on dataset-specific feature semantics and structural patterns, which limits their ability to generalize across different domains. To address this challenge, we propose AlignGAD, a zero-shot generalized graph anomaly detection framework. Our framework is built upon three key components: a Global Unification Module that aligns heterogeneous node features and normalizes graph signals in the spectral domain; a Clustering Module that constructs cluster-aware graph views to capture group-level abnormal patterns; and a Node Discrepancy Scoring Module that measures reconstruction discrepancy and aggregates anomaly evidence from different graph views. Experiments on multiple real-world datasets demonstrate the effectiveness of AlignGAD under the zero-shot GAD setting.