Homophily-aware Heterogeneous Graph Contrastive Learning
This addresses a gap in heterogeneous graph learning for real-world applications, but it is incremental as it builds on existing contrastive learning methods.
The paper tackles the problem of heterophily in heterogeneous graph pre-training by proposing HGMS, a contrastive learning framework that uses edge dropping and multi-view self-expression to learn homophilous node representations, achieving superior performance in downstream tasks.
Heterogeneous graph pre-training (HGP) has demonstrated remarkable performance across various domains. However, the issue of heterophily in real-world heterogeneous graphs (HGs) has been largely overlooked. To bridge this research gap, we proposed a novel heterogeneous graph contrastive learning framework, termed HGMS, which leverages connection strength and multi-view self-expression to learn homophilous node representations. Specifically, we design a heterogeneous edge dropping augmentation strategy that enhances the homophily of augmented views. Moreover, we introduce a multi-view self-expressive learning method to infer the homophily between nodes. In practice, we develop two approaches to solve the self-expressive matrix. The solved self-expressive matrix serves as an additional augmented view to provide homophilous information and is used to identify false negatives in contrastive loss. Extensive experimental results demonstrate the superiority of HGMS across different downstream tasks.