Modeling Spectral Energy Shifts in Spatio-Temporal Graph Anomaly Detection

ArXi:2606.00304v1 Announce Type: new Graph anomaly detection methods aim to distinguish anomalous nodes. While prior methods characterize anomalies through increased variation in the spectral energy distributions, they overlook those that result in decreased variation, i.e., camouflaged anomalies that appear normal. We show that this type of anomaly persists across multiple datasets and remains undetectable by existing spectral approaches.