Prototype-guided Topology Augmentation for Edge-Imbalanced Graph Domain Adaptation

Graph Domain Adaptation (GDA) transfers knowledge from labeled source graphs to unlabeled target graphs, alleviating label scarcity in real-world graph data. Class imbalance is prevalent in graphs and can lead to biased knowledge transfer in GDA. However, existing methods for class-imbalanced GDA primarily focus on imbalanced node counts across source classes, while overlooking edge imbalance. Empirically, we observe pronounced intra-domain edge imbalance and substantial differences in edge distributions across domains. Moreover, we reveal that larger cross-domain edge-distribution discrepancies are associated with lower GDA performance. Inspired by these findings, we propose ProTA, a plug-and-play topology augmentation framework for edge-imbalanced GDA. ProTA constructs topology-aware source prototypes through class-preserving diffusion to guide candidate edge selection. Augmentation budgets are allocated according to intra-domain edge deficits and cross-domain edge-distribution discrepancies. The selected edges strengthen relational support for minority-class nodes in both domains, reducing intra-domain edge imbalance and cross-domain edge distribution discrepancies. Experiments on 44 transfer tasks across four benchmarks demonstrate that ProTA improves the performance of six representative GDA models.