Dual Prototype-Enhanced Contrastive Framework for Class-Imbalanced Graph Domain Adaptation
Xin Ma, Yifan Wang, Siyu Yi, Wei Ju, Junyu Luo, Yusheng Zhao, Xiao Luo, Jiancheng Lv
Graph transfer learning, especially in unsupervised domain adaptation, focuses on transferring knowledge from a label-rich source domain graph to an unlabeled target domain graph. However, most existing methods assume a balanced label distribution in the source graph, often inconsistent with real-world scenarios where node labels are class-imbalanced. This presents significant challenges for graph transfer learning, as it is impacted by biased knowledge from the source graph and substantial domain distribution shifts. Given the effectiveness of class-specific prototypes in capturing domain semantics, in this paper, we propose a dual-branch prototype-enhanced contrastive framework for class-imbalanced graph domain adaptation. Specifically, we introduce a dual-branch graph encoder to capture both local and global information, generating class-specific prototypes from a distilled anchor set. Then, a prototype-enhance contrastive learning framework is introduced to address the aforementioned challenges. On the one hand, we encourage class alignment between the two branches based on constructed prototypes to mitigate the impact of class imbalance. On the other hand, we generate pseudo-labels for the target domain and align sample pairs across domains that share similar semantics to reduce domain discrepancies. Extensive experimental results show that our ImGDA outperforms the state-of-the-art methods across multiple datasets and settings.