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Life sciences · Preprint
arXiv · August 11, 2026
Posted before peer review. The findings may change or fail to hold.
ProTAGAD is a novel foundation model for detecting anomalies in text-attributed graphs by decoupling topological and textual feature learning, addressing a theoretical 'Blurred-Anomaly-Boundary' issue in prior coupled methods. The work reports state-of-the-art performance across 14 benchmark datasets and demonstrates cross-domain generalization; however, it is a preprint without peer review and provides no clinical validation or deployment outcomes.
Preprint. Intervention: ProTAGAD: a foundation model for text-attributed graph anomaly detection with decoupled topological and textual prototype banks. Compared with: Conventional GNN-based anomaly detectors with coupled message-passing schemes.
Method achieves state-of-the-art performance across 14 diverse benchmark datasets in cross-domain settings Ablation studies corroborate the prevalence of the Blurred-Anomaly-Boundary (BAB) issue in conventional coupled TAG anomaly detectors Decoupled prototype design effectively mitigates the BAB challenge by independently modelling structural normality and semantic consistency
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This is an unrefereed computational method paper on arXiv describing a machine-learning algorithm for anomaly detection; it reports benchmark performance but lacks clinical validation, patient data, or peer review.
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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.
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