GraphSteer: Soft Edge Reweighting via Informative Leiden Subgraphs for Graph Out-of-Distribution Generalization

Graph neural networks often exploit environment-specific correlations that do not remain stable under distribution shifts, leading to poor out-of-distribution (OOD) generalization. Existing graph OOD methods commonly rely on identifying invariant subgraphs or suppressing spurious structures, but such approaches may discard useful information when the distinction between relevant and irrelevant graph components is imperfect. We propose GraphSteer, a soft structural reweighting framework that partitions graphs into candidate subgraphs using Leiden community detection, which we extend to incorporate node-feature information. GraphSteer estimates subgraph informativeness from a subgraph classifier's predictive confidence and uses this information to reweight graph edges while preserving the full graph structure for downstream message passing. This soft structural intervention avoids irreversible structural deletion when subgraph identification is imperfect, while the resulting communities and informativeness scores provide an inspectable view of how GraphSteer emphasises different graph regions under covariate and concept shifts. Unlike many existing methods evaluated on specific graph domains or shift settings, GraphSteer is evaluated across image-derived, molecular, and synthetic graphs under both covariate and concept shifts, without requiring ground-truth environment labels. Experiments on GOOD-CMNIST, GOOD-HIV, and GOOD-Motif demonstrate competitive and robust OOD performance across these settings.