Invariant Subgraph Learning via Effective Environment Subgraph Augmentation for Generalizable Graph Neural Network
With the increasing prevalence of graph data in various practical applications, Graph Neural Networks (GNNs) have established themselves as essential tools for effective graph data processing. However, existing GNNs always perform well on in-distribution data, but exhibit significant performance degradation under distribution shifts. To improve GNN generalization, graph invariant learning aims to identify accurate invariant subgraphs but is constrained by limited diversity in environment subgraphs. Conversely, graph data augmentation focuses on enriching this diversity through environment subgraph augmentation, but its efficacy heavily depends on having accurate invariant subgraphs first. This creates a core paradox: acquiring accurate invariant subgraphs requires diverse data, whereas effective augmentation presupposes accurate invariant subgraphs. To address this issue, we propose IGESA, a method for learning accurate Invariant subGraph via effective Environment Subgraph Augmentation. IGESA introduces two key strategies: (1) For accurate invariant subgraph identification, we propose a precise invariant subgraph extraction strategy to refine the subgraph learning process. (2) For sufficiently diverse augmentations, we propose a cross-graph environment fusion strategy that combines sampled components from pair-wise distinct environment subgraphs to construct new subgraphs. These two strategies are collaboratively optimized to boost GNNs’ generalizability, and extensive experiments on benchmark datasets demonstrate their superiority over state-of-the-art methods.