NeighborDiv: 一种基于邻居多样性、无需训练的跨域通用图异常检测方法
NeighborDiv: Training-free Zero-shot Generalist Graph Anomaly Detection via Neighbor Diversity
AI总结：
本文提出NeighborDiv，一种无需训练的通用图异常检测方法，通过邻居多样性原理来检测异常，克服了传统方法在训练复杂度、数据依赖性和跨域泛化稳定性方面的不足，实验表明其在多个评估框架下均取得最佳性能。
摘要：
图异常检测（GAD）正逐渐转向通用图异常检测（GGAD）以实现跨域的'一揽子'检测，但现有GGAD方法主要依赖邻居一致性原则，陷入'节点到邻居一致性范式'的异常量化中。这些方法存在训练流程复杂、依赖大量训练数据、计算成本高以及跨域泛化不稳定等问题。为了解决这些限制，我们提出了NeighborDiv，一种基于邻居多样性的无需训练的通用图异常检测框架。偏离主流的'节点到邻居一致性范式'，我们转向'邻居到邻居多样性范式'，发现节点邻居集合的内部结构分散性是一种强大且独立的异常信号。我们通过邻居间特征相似性的方差来量化邻居多样性，捕捉节点如何组织其局部图环境，并独立于传统节点到邻居一致性框架。在两个标准的GGAD评估范式下进行的大量实验表明，NeighborDiv在单域独立训练（SDIT）下平均AUC提升了10.25%，平均AP提升了17.78%；在统一多域训练（UMDT）下，AUC和AP分别提升了6.89%和9.58%。值得注意的是，NeighborDiv在所有数据集上均无性能波动，消除了训练集依赖性，建立了一个轻量且高度实用的GGAD框架。
英文摘要：
Graph Anomaly Detection (GAD) is increasingly shifting to Generalist GAD (GGAD) for cross-domain "one-for-all" detection, but existing GGAD methods predominantly rely on the neighbor consistency principle, falling into the \textbf{Node-to-Neighbor Consistency Paradigm} for anomaly quantification. These methods suffer from complex training pipelines, heavy training data dependency, high computational costs, and unstable cross-domain generalization. To address these limitations, we propose NeighborDiv, a training-free generalist graph anomaly detection framework based on neighbor diversity. Departing from the dominant Node-to-Neighbor Consistency Paradigm, we shift the focus to the \textbf{Neighbor-to-Neighbor Diversity Paradigm}, and uncover that the internal structural dispersion of a node's neighbor set is a powerful, independently discriminative anomaly signal. We quantify neighbor diversity via the variance of inter-neighbor feature similarities, which captures how a node organizes its local graph environment, and operates independently of conventional node-to-neighbor consistency frameworks. Extensive experiments under two standard GGAD evaluation paradigms show NeighborDiv achieves state-of-the-art performance, with relative gains of 10.25% in average AUC and 17.78% in average AP over the second-best baseline under Single-Domain Independent Training (SDIT), and 6.89%/9.58% in AUC/AP under Unified Multi-Domain Training (UMDT), respectively. Notably, NeighborDiv yields zero performance volatility across all datasets, eliminating training-set dependency and establishing a lightweight and highly practical GGAD framework.
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。
