Homophily-aware Heterogeneous Graph Contrastive Learning

• URL: http://arxiv.org/abs/2501.08538v1
• Date: Wed, 15 Jan 2025 02:56:50 GMT
• Title: Homophily-aware Heterogeneous Graph Contrastive Learning
• Authors: Haosen Wang, Chenglong Shi, Can Xu, Surong Yan, Pan Tang,
• Abstract summary: We propose a novel heterogeneous graph contrastive learning framework, termed HGMS, to learn homophilous node representations. Specifically, we design a heterogeneous edge dropping augmentation strategy that enhances the homophily of augmented views. In practice, we develop two approaches to solve the self-expressive matrix.
• Score: 23.38883104104888
• License:
• Abstract: 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.

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