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.

翻译：跨域图异常检测旨在识别未见过目标图中的异常节点，在具有异构图数据的实际应用中展现出巨大潜力。然而，现有方法通常依赖于数据集特定的特征语义与结构模式，这限制了其跨不同领域的泛化能力。为解决这一挑战，我们提出AlignGAD——一种零样本泛化图异常检测框架。该框架包含三个关键组件：全局统一模块，用于对齐异构图节点特征并在频谱域中归一化图信号；聚类模块，用于构建聚类感知的图视图以捕捉群体级别的异常模式；以及节点差异评分模块，用于衡量重构差异并聚合来自不同图视图的异常证据。在多个真实数据集上的实验证明了AlignGAD在零样本图异常检测场景下的有效性。