Invariant Structure Learning with Pre-trained Language Models for Spatio-temporal Graph
InvSTG-PLM, an invariance-aware spatiotemporal graph learning framework that grounds PLMs in invariant structures, is proposed and a deterministic robustness bound is justified, showing that mean–variance control upper bounds worst-case risk over simulated environments.