康奈尔大学提出RINSE方法，解决了图异常检测中的领域迁移问题，在8个目标图上表现最佳。
RINSE是一种梯度自由的目标时间框架，在8个未见过的目标图上实现了最高的平均AUPRC。该方法通过识别低残差目标节点子集，构建修剪后的目标感知正常性模型。RINSE结合了可靠性门控排序融合和编码器集成，无需目标标签、梯度或针对每个目标的调整。
RINSE: Robust Target-Time Normality Estimation for Zero-Shot Graph Anomaly Detection
Zero-shot graph anomaly detection seeks to deploy a detector trained on source graphs to unseen, unlabeled targets, yet domain shift can make source-derived notions of normality unreliable. We introduce RINSE (Robust Iterative Normality Self-Estimation), a gradient-free target-time framework that keeps the source-trained detector fixed while sequentially estimating target normality, representation calibration, and evidence reliability from the target graph. Its core idea is to identify a reliable subset of low-residual target nodes, use them to construct a trimmed target-aware normality model, and combine complementary anomaly evidence through reliability-gated rank fusion and encoder ensembling. Across eight unseen target graphs, RINSE achieves the highest average AUPRC among the evaluated methods under two separate preprocessing protocols, while block ablations and sensitivity analyses support the combined design. These results support robust target-time estimation as a practical approach to generalist graph anomaly detection without target labels, gradients, or per-target tuning.