ProTAGAD: A Foundation Model for TAG Anomaly Detection with Decoupled Topological and Textual Prototypes

2026-08-11

AI Summary

This work addresses the blurred anomaly boundary (BAB) problem in existing textual attribute graph (TAG) anomaly detection methods, which arises from the tight coupling of topological structure and textual semantics during message passing, leading to poor detection of subtle anomalies and limited generalization. To overcome this, the authors propose a decoupled foundation model that introduces separate topology and text prototype banks to independently model structural normality and semantic consistency, thereby avoiding noise amplification inherent in multimodal fusion. By integrating decoupled aggregation with prototype learning, the method achieves precise anomaly discrimination and sets new state-of-the-art performance across 14 benchmark datasets for cross-domain anomaly detection. Ablation studies further confirm the prevalence of the BAB issue and the effectiveness of the proposed decoupling mechanism.

Abstract

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.

Problem

• Text-Attributed Graphs
• Graph Anomaly Detection
• Blurred-Anomaly-Boundary
• Cross-modality Coupling
• Foundation Model

Innovation

• decoupled prototypes
• text-attributed graphs
• anomaly detection
• graph foundation model
• Blurred-Anomaly-Boundary