零样本跨域图异常检测新框架，无需训练即可识别未知图中的异常节点。
A Zero-shot Generalized Graph Anomaly Detection Framework via Node Reconstruction
- 通过谱域对齐统一异构节点特征，实现跨域通用表示。
- 在多个真实数据集上达到领先性能，零样本下仍保持高准确率。
- 适合处理无标签、结构多样的实际图数据，如社交网络与知识图谱。
跨域图异常检测（GAD）旨在识别未见目标图中的异常节点，在异构图数据的实际应用中具有巨大潜力。然而，现有方法通常依赖特定数据集的特征语义和结构模式，限制了其跨域泛化能力。为此，我们提出 AlignGAD，一种零样本广义图异常检测框架。该框架包含三个核心组件：全局统一模块，在谱域对齐异构节点特征并归一化图信号；聚类模块，构建簇感知的图视图以捕捉群体级异常模式；节点差异评分模块，测量重构差异并聚合来自不同图视图的异常证据。在多个真实世界数据集上的实验表明，AlignGAD 在零样本 GAD 设置下表现优异。
原文摘要 · Abstract (English)
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.
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