纠正来自未见样本的误报：在测试时适配图异常检测器
Correcting False Alarms from Unseen: Adapting Graph Anomaly Detectors at Test Time
AI总结：
针对图异常检测中测试时出现未见正常样本导致正态性偏移的问题，本文提出轻量级即插即用的测试时适配框架TUNE，通过图对齐器纠正语义混淆和聚合污染，显著提升预训练GAD模型的泛化能力。
摘要：
图异常检测（GAD）旨在检测图结构数据中的离群点，近年来受到越来越多的研究关注。然而，现有的GAD方法假设训练分布和测试分布相同，这在实践中很少成立。在现实场景中，部署期间可能出现未见但正常的样本，导致正态性偏移，从而降低在原始数据上训练的GAD模型的性能。通过实证分析，我们揭示性能下降源于：（1）语义混淆，即未见正常样本因其新颖模式而被误判为异常；（2）聚合污染，即已见正常节点的表示通过消息聚合被未见正常样本扭曲。尽管重新训练或微调GAD模型可能是应对上述挑战的潜在解决方案，但模型重新训练的高昂成本以及获取标注数据的困难往往使这种方法在实际应用中不切实际。为了弥合这一差距，我们提出了一个轻量级且即插即用的测试时适配框架，用于纠正GAD中的未见正常模式（TUNE）。为了解决语义混淆问题，采用图对齐器在图属性层面将偏移数据对齐到原始数据。此外，我们利用表示层面偏移的最小化作为监督信号来训练对齐器，该对齐器利用估计的聚合污染作为正态性偏移的关键指标。在10个真实世界数据集上的大量实验表明，TUNE显著增强了预训练GAD模型对合成和真实未见正常模式的泛化能力。
英文摘要：
Graph anomaly detection (GAD), which aims to detect outliers in graph-structured data, has received increasing research attention recently. However, existing GAD methods assume identical training and testing distributions, which is rarely valid in practice. In real-world scenarios, unseen but normal samples may emerge during deployment, leading to a normality shift that degrades the performance of GAD models trained on the original data. Through empirical analysis, we reveal that the degradation arises from (1) semantic confusion, where unseen normal samples are misinterpreted as anomalies due to their novel patterns, and (2) aggregation contamination, where the representations of seen normal nodes are distorted by unseen normals through message aggregation. While retraining or fine-tuning GAD models could be a potential solution to the above challenges, the high cost of model retraining and the difficulty of obtaining labeled data often render this approach impractical in real-world applications. To bridge the gap, we proposed a lightweight and plug-and-play Test-time adaptation framework for correcting Unseen Normal pattErns (TUNE) in GAD. To address semantic confusion, a graph aligner is employed to align the shifted data to the original one at the graph attribute level. Moreover, we utilize the minimization of representation-level shift as a supervision signal to train the aligner, which leverages the estimated aggregation contamination as a key indicator of normality shift. Extensive experiments on 10 real-world datasets demonstrate that TUNE significantly enhances the generalizability of pre-trained GAD models to both synthetic and real unseen normal patterns.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
