Wasserstein Distance-Based Multisource Heterogeneous Graph Adaptation for Cross-Network Node Classification

Cross-network node classification seeks to leverage labeled source networks to assist node classification in an unlabeled target network. However, existing heterogeneous graph adaptation methods often rely on restrictive assumptions, such as the presence of a single source network or strong correlations between source and target nodes, which rarely hold in practice. To address this, we propose a novel Wasserstein distance-based multisource heterogeneous graph adaptation framework (WMHGA), which aims to learn transferable node representations across networks in order to improve the accuracy of node classification tasks. Specifically, we propose a Wasserstein distance-based heterogeneous graph adaptation approach to learn node representations that are invariant to domain variations. Then, we propose two Wasserstein distance-based knowledge distillation approaches to identify more valuable samples from the source graph and learn label-discriminative node representations of these samples for knowledge transfer. In addition, we devise a Wasserstein distance-based aggregated prediction to prioritize highly relevant source nodes while suppressing irrelevant ones, thereby ensuring more accurate node classification in the target network. Extensive experiments have been conducted on three real-world datasets, demonstrating that the proposed WMHGA model outperforms the state-of-the-art baselines.