AI 中文总结
本文提出ProTAGAD基础模型，通过解耦拓扑与文本原型构建双原型库，缓解传统方法的异常边界模糊问题，在14个基准数据集的跨域场景中实现最先进TAG异常检测性能。
AI 中文摘要
文本属性图（TAG）兼具丰富的文本内容与拓扑结构，已成为覆盖大语言模型安全、社交网络审核、网络威胁识别等现实场景异常检测的通用基础。与传统图异常检测（GAD）主要依赖结构异常不同，TAG异常检测需同时利用拓扑模式与细粒度文本语义来捕捉细微的异常行为。当前基于图神经网络（GNN）的异常检测器采用整体消息传递方案，在传播过程中不加区分地融合结构邻近性与文本语义，导致深度跨模态耦合。这种耦合会放大噪声，模糊微妙的异常信号，直接引发异常边界模糊（BAB）问题，使正常与异常的决策边界难以分离，而这一挑战对需要强跨域泛化能力的图基础模型而言更为突出。为弥合这一差距，本文提出一种新型TAG异常检测基础模型，具备解耦的拓扑和文本原型。该框架构建双原型库，分别独立建模结构正态性与语义一致性，有效隔离在耦合聚合过程中会被稀释的异常线索。在14个不同基准数据集上开展的大量实验表明，本文方法在跨域场景中始终达到最先进性能。值得注意的是，消融研究进一步证实了传统耦合TAG异常检测器中BAB问题的普遍性，并表明本文的解耦原型设计可有效缓解该挑战。
英文摘要
Text-Attributed Graphs (TAGs), endowed with abundant textual content along with topological structures, have emerged as a versatile backbone for real-world anomaly detection spanning large language model security, social network moderation, and cyber threat identification. Unlike conventional Graph Anomaly Detection (GAD), which relies primarily on structural irregularities, TAG anomaly detection must jointly leverage both topological patterns and fine-grained textual semantics to capture nuanced anomalous behaviors. The current GNN-based anomaly detectors adopt holistic message-passing schemes that indiscriminately fuse structural proximity and textual semantics during propagation, leading to deep cross-modality coupling. This entanglement acts as a noise amplifier, obscuring subtle anomalous signals and directly giving rise to the Blurred-Anomaly-Boundary (BAB) issue by rendering normal-anomalous decision boundaries poorly separable. This challenge is further amplified for graph foundation models that require robust cross-domain generalization. To bridge this gap, we introduce a novel foundation model for TAG anomaly detection featuring decoupled topological and textual prototypes. Our framework constructs dual prototype banks to independently model structural normality and semantic consistency, effectively isolating anomaly cues that are otherwise diluted during coupled aggregation. Extensive experiments across 14 diverse benchmark datasets demonstrate that our method consistently achieves state-of-the-art performance in cross-domain settings. Notably, the ablation studies further corroborate the prevalence of the BAB issue in conventional coupled TAG anomaly detectors, and show that our decoupled prototype design effectively mitigates this challenge.
