Introduces a node-level spectral energy formulation and energy-aware message passing framework to detect camouflaged anomalies with decreased spectral variation in static and time-series graphs.
right-shift
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
cs.LG 1years
2026 1verdicts
UNVERDICTED 1representative citing papers
citing papers explorer
-
Modeling Spectral Energy Shifts in Spatio-Temporal Graph Anomaly Detection
Introduces a node-level spectral energy formulation and energy-aware message passing framework to detect camouflaged anomalies with decreased spectral variation in static and time-series graphs.