When Semantics Defy Context: Semantic Anomaly Detection in Text-Attributed Graphs

Graph anomaly detection in text-attributed graphs (TAGs) seeks to identify suspicious entities using textual attributes and relational information. A key challenge is detecting semantic anomalies: nodes whose content is plausible in isolation but incompatible with their relational context. Existing methods that primarily model deviations in node attributes or graph structure can struggle to identify the semantic conflicts underlying such anomalies. Specifically, textual similarity can obscure semantic conflicts, while reliance on a single contextual scale conceals anomalies or misclassifies legitimate semantic differences. Without anomaly labels, constructing informative negative examples is also challenging because strong perturbations create obvious outliers, whereas weak ones preserve contextual compatibility. We propose the Context-Aware Anomaly Reasoning Network (CARNet), an unsupervised framework for assessing semantic compatibility between nodes and their contexts. CARNet compares attribute-based and structure-based community distributions to expose potential conflicts and integrates local and global evidence to infer the semantics expected from the context. Mixing attributes between semantically dissimilar nodes from different communities while preserving the target's relational context provides synthetic mismatches for self-supervised learning. Extensive experiments on five benchmark datasets demonstrate that CARNet achieves state-of-the-art or competitive performance, with further analyses supporting the effectiveness of its key components.