Graph anomaly detection as finite-horizon control: training-free scoring via empirical Bayes
Read the original at arxiv.org→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...
Original headline: "Graph Anomaly Detection as Finite-Horizon Control: Training-Free Scoring via Empirical Bayes"
Coverage timeline
- Oct 1, 04:00 UTC arXiv cs.LG lead source Graph Anomaly Detection as Finite-Horizon Control: Training-Free Scoring via Empirical Bayes