PEMS-BAY

Graph-Conditioned Mixture of Graph Neural Network Experts for Traffic Forecasting

arXiv:2605.30486v1 Announce Type: new Abstract: Spatio-temporal forecasting on sensor graphs is commonly tackled with a single backbone architecture applied uniformly across all nodes, although graph regions can exhibit different dynamics. Road segments differ in functional class, structure, and traffic behavior, suggesting that node-wise expert specialization can be useful. We propose GC-MoE, a graph-conditioned mixture of experts framework that assigns each node a personalized combination...