A Zero-shot Generalized Graph Anomaly Detection Framework via Node Reconstruction
cs.LG, cs.AI
Submitted: 2026-06-10
Updated: 2026-08-29
Comments: PRICAI 2026
License: http://creativecommons.org/licenses/by/4.0/
The gist: 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.
Terminology
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.
Sources
- Spectral Networks and Locally Connected Networks on Graphs
- Boost then Convolve: Gradient Boosting Meets Graph Neural Networks
- Counterfactual Data Augmentation with Denoising Diffusion for Graph Anomaly Detection
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