From Small to Large: Cross-Scale Graph Domain Adaptation via Local-Global Structural Alignment

Graph domain adaptation (GDA) aims to transfer knowledge from a labeled source graph to an unlabeled target graph. Existing GDA methods typically assume that the source graph provides sufficiently complete information for reliable cross-domain alignment. However, this assumption is often unrealistic in practice, where only a small sampled source graph may be available due to privacy, annotation, storage, or data-collection constraints. In this paper, we study Cross-Scale Graph Domain Adaptation (CSGDA), where adaptation is performed from a limited-scale source graph to a larger target graph. Our empirical results show that CSGDA exhibits larger local and global discrepancies than conventional GDA, as the source graph provides only limited local structures and limited coverage of the global structure. To address this challenge, we propose a local-global structural alignment framework that, at the local level, augments neighborhood structures to compensate for limited local structural information. At the global level, it extracts global structural patterns and aligns source and target distributions. An adaptive GCN-MLP module then integrates the compensated local structures and extracted global patterns for domain alignment and target prediction. Experiments across multiple graph adaptation benchmarks and sampling ratios demonstrate that our method consistently improves adaptation in the presence of severe source-graph incompleteness.