Spectrally-Regularized Graph Network with Disruption-Aware Training for Out-of-Distribution Robust Traffic Forecasting
SRGNet is proposed, a spectrally-regularized graph network combining three targeted innovations: spectral normalization on all weight matrices to bound the Lipschitz constant, disruption-aware training augmentation synthesizing incident-like flow drops, and stochastic depth creating an implicit ensemble.