Graph Anomaly Detection (GAD) has demonstrated great effectiveness in identifying unusual patterns within graph-structured data. However, while labeled anomalies are often scarce in emerging applications, existing supervised GAD approaches are either ineffective or not applicable when moved across graph domains due to distribution shifts and heterogeneous feature spaces. To address these challenges, we present AdaGraph-T3, a novel test-time training framework for cross-domain GAD. AdaGraph-T3 combines supervised and self-supervised learning during training while adapting to a new domain during test time using only self-supervised learning by leveraging a homophily-based affinity score that captures domain-invariant properties of anomalies. Our framework introduces four key innovations to cross-domain GAD: an effective self-supervision scheme, an attention-based mechanism that dynamically learns edge importance weights during message passing, domain-specific encoders for handling heterogeneous features, and class-aware regularization to address imbalance. Experiments across multiple cross-domain settings demonstrate that AdaGraph-T3 significantly outperforms existing approaches, achieving average improvements of over 6.6% in AUROC and 7.9% in AUPRC compared to the best competing model.

本研究解决了图异常检测（GAD）在新兴应用中标记异常稀缺的问题，现有的监督方法在分布转移和异构特征空间时效果欠佳。提出的AdaGraph-T3是一个创新的测试时训练框架，通过结合监督和自监督学习，利用同质性基础的亲和力得分在测试阶段适应新领域，显著提高了跨领域GAD的性能。实验结果显示，AdaGraph-T3在多个跨领域设置上相比于最佳竞争模型的AUROC和AUPRC分别取得了超过6.6%和7.9%的平均提升。 

跨分布域的图异常检测：自适应测试时表示学习