Zero-shot Generalizable Graph Anomaly Detection with Mixture of Riemannian Experts

• URL: http://arxiv.org/abs/2602.06859v2
• Date: Mon, 09 Feb 2026 06:35:36 GMT
• Title: Zero-shot Generalizable Graph Anomaly Detection with Mixture of Riemannian Experts
• Authors: Xinyu Zhao, Qingyun Sun, Jiayi Luo, Xingcheng Fu, Jianxin Li,
• Abstract summary: Graph Anomaly Detection (GAD) aims to identify irregular patterns in graph data.<n>Existing zero-shot GAD methods largely ignore intrinsic geometric differences across diverse anomaly patterns.<n>We propose GAD-MoRE, a novel framework for zero-shot Generalizable Graph Anomaly Detection.
• Score: 33.295461492913354
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Graph Anomaly Detection (GAD) aims to identify irregular patterns in graph data, and recent works have explored zero-shot generalist GAD to enable generalization to unseen graph datasets. However, existing zero-shot GAD methods largely ignore intrinsic geometric differences across diverse anomaly patterns, substantially limiting their cross-domain generalization. In this work, we reveal that anomaly detectability is highly dependent on the underlying geometric properties and that embedding graphs from different domains into a single static curvature space can distort the structural signatures of anomalies. To address the challenge that a single curvature space cannot capture geometry-dependent graph anomaly patterns, we propose GAD-MoRE, a novel framework for zero-shot Generalizable Graph Anomaly Detection with a Mixture of Riemannian Experts architecture. Specifically, to ensure that each anomaly pattern is modeled in the Riemannian space where it is most detectable, GAD-MoRE employs a set of specialized Riemannian expert networks, each operating in a distinct curvature space. To align raw node features with curvature-specific anomaly characteristics, we introduce an anomaly-aware multi-curvature feature alignment module that projects inputs into parallel Riemannian spaces, enabling the capture of diverse geometric characteristics. Finally, to facilitate better generalization beyond seen patterns, we design a memory-based dynamic router that adaptively assigns each input to the most compatible expert based on historical reconstruction performance on similar anomalies. Extensive experiments in the zero-shot setting demonstrate that GAD-MoRE significantly outperforms state-of-the-art generalist GAD baselines, and even surpasses strong competitors that are few-shot fine-tuned with labeled data from the target domain.

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