Graph Anomaly Detection as Finite-Horizon Control: Training-Free Scoring via Empirical Bayes
arXiv:2609.38424v1 Announce Type: new Abstract: 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, reconstru…
Read the full story at arXiv cs.LG ↗
Timeline · 1 report
- 2026-10-01 04:00 · arXiv cs.LG
Graph Anomaly Detection as Finite-Horizon Control: Training-Free Scoring via Empirical Bayes