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.

翻译：图异常检测旨在识别图数据中的不规则模式，近期研究探索了零样本通用图异常检测方法以实现对未见图数据集的泛化。然而，现有零样本方法大多忽略了不同异常模式之间固有的几何差异，严重限制了其跨域泛化能力。本研究发现，异常可检测性高度依赖于底层几何特性，将不同域的图嵌入到单一静态曲率空间会扭曲异常的结构特征。为应对单一曲率空间无法捕捉几何相关图异常模式的挑战，我们提出GAD-MoRE——一种基于黎曼专家混合架构的零样本可泛化图异常检测框架。具体而言，为确保每种异常模式在其最可检测的黎曼空间中被建模，GAD-MoRE采用一组专用黎曼专家网络，每个网络在特定曲率空间中运作。为实现原始节点特征与曲率特定异常特征的对齐，我们设计了异常感知多曲率特征对齐模块，将输入投影到并行黎曼空间以捕捉多样化几何特性。最后，为促进对未见模式的更好泛化，我们设计了基于记忆的动态路由机制，该机制根据历史相似异常重建性能自适应地将每个输入分配给最兼容的专家。在零样本设置下的大量实验表明，GAD-MoRE显著优于当前最先进的通用图异常检测基线模型，甚至超越了使用目标域标注数据进行少样本微调的强竞争方法。