针对异质图中被忽视的异性连接问题，提出新对比学习框架增强节点表示同质性。
Homophily-aware Heterogeneous Graph Contrastive Learning
- 通过边丢弃和多视角自表达机制增强图的同质性
- 在多个下游任务上优于现有方法，显著提升性能
- 适合处理真实世界中存在异性连接的异质图场景
异质图预训练（HGP）在多个领域表现优异，但真实世界异质图中存在的异性连接问题长期被忽略。为此，我们提出一种新型异质图对比学习框架HGMS，利用连接强度与多视角自表达来学习同质性节点表示。具体而言，设计了一种异质边丢弃增强策略以提升增强视图的同质性；同时引入多视角自表达学习方法推断节点间的同质性。实际中，提出两种求解自表达矩阵的方法，其结果作为额外增强视图提供同质性信息，并用于识别对比损失中的假负样本。大量实验表明，HGMS在不同下游任务上均表现出优越性。
原文摘要 · Abstract (English)
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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