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Graph Anomaly Detection as Finite-Horizon Control: Training-Free Scoring via Empirical Bayes
Node-level graph anomaly detection (GAD) identifies nodes whose attributes and interactions deviate from dominant graph regularities. Existing GAD models encode normality and anomaly scoring indirectly through architectures, message passing, reconstruction or contrastive objectives, and tuned score
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