A Unified Uncertainty Representation for Graph Neural Networks via Doubly-Spectral Stochastic Expansion

Reliable deployment of graph neural networks requires calibration, out-of-distribution (OOD) detection, and robustness to distribution shift, yet existing methods address these needs with separate models and objectives. We model uncertain node embeddings as random graph signals: graph Fourier filt