Test-Time Graph Anomaly Detection via Shifted Augmentation with Dynamic Objective Scheduling

Graph anomaly detection (GAD) is critical to many real-world systems. However, supervised GAD models often degrade under distribution shifts due to the closed-world training assumption. Test-time adaptation (TTA) offers a promising solution by updating pre-trained models with unlabeled test data. Yet, applying TTA to GAD poses a unique dilemma. High-confidence samples provide reliable pseudo-labels, but they are scarce and biased toward low-shift regions. Low-confidence samples better reflect target shifts, but they are too noisy for direct cross-entropy optimization and often require label-free objectives that are misaligned with anomaly discrimination. To address this dilemma, we propose SADOS, a Shifted Augmentation-based Dynamic Objective Scheduling framework for test-time GAD. SADOS turns unreliable target samples into drift-carrying signals for reliable supervised adaptation. It distills prototypes from confidence-stratified unreliable samples and injects them into reliable samples to generate label-preserving yet shift-oriented augmentations. This expands coverage over shifted target regions. In parallel, SADOS dynamically schedules the adaptation objective. It uses contrastive learning to capture early-stage drift signals, and gradually anneals its weight so that classification-oriented anomaly detection dominates later adaptation. Extensive experiments demonstrate that SADOS robustly improves test-time GAD under distribution shifts.