Unified Graph Anomaly Detection with Target-Context Evidence Reconstruction

In graph-structured data, anomalous entities at different granularity levels (i.e., nodes, edges, and graphs) are often inherently interconnected. Motivated by such dependencies, unified graph anomaly detection (GAD) has recently emerged as a new research direction, aiming to detect multi-level anomalies within a single model. Although existing unified GAD approaches integrate multi-level detection tasks into a single framework, they typically convert detection objects at different granularities into a fixed subgraph format, overlooking the granularity-specific information required for anomaly detection at different levels. Moreover, existing methods still rely on level-specific detection heads or task-dependent references to support anomaly detection at different granularities, limiting knowledge integration throughout the end-to-end detection pipeline and preventing full task unification. To address these limitations, we propose Unified Target-context Reformulation And Contextual Evidence Reconstruction (UniTRACER) for unified GAD. To preserve granularity-specific information with a general task formulation, we introduce a target-context joint modeling paradigm and emphasize context importance based on the specific detection target. Moreover, to consolidate multi-level anomaly detection under a unified detection objective, we characterize the target-context relation and importance as shared detection evidence among levels, and then reframe unified GAD as an evidence reconstruction task. Together, these designs enable UniTRACER to retain granularity-specific anomaly cues while facilitating end-to-end knowledge sharing through a common task formulation and detection objective. Comprehensive experiments on 17 datasets demonstrate that UniTRACER significantly outperforms state-of-the-art granularity-specific and unified GAD methods.