Incorporating Attributes and Multi-Scale Structures for Heterogeneous Graph Contrastive Learning

• URL: http://arxiv.org/abs/2503.13911v2
• Date: Thu, 10 Apr 2025 09:07:02 GMT
• Title: Incorporating Attributes and Multi-Scale Structures for Heterogeneous Graph Contrastive Learning
• Authors: Ruobing Jiang, Yacong Li, Haobing Liu, Yanwei Yu,
• Abstract summary: We propose a novel contrastive learning framework for heterogeneous graphs (ASHGCL)<n>ASHGCL incorporates three distinct views, each focusing on node attributes, high-order and low-order structural information, respectively.<n>We introduce an attribute-enhanced positive sample selection strategy that combines both structural information and attribute information.
• Score: 8.889313669713918
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Heterogeneous graphs (HGs) are composed of multiple types of nodes and edges, making it more effective in capturing the complex relational structures inherent in the real world. However, in real-world scenarios, labeled data is often difficult to obtain, which limits the applicability of semi-supervised approaches. Self-supervised learning aims to enable models to automatically learn useful features from data, effectively addressing the challenge of limited labeling data. In this paper, we propose a novel contrastive learning framework for heterogeneous graphs (ASHGCL), which incorporates three distinct views, each focusing on node attributes, high-order and low-order structural information, respectively, to effectively capture attribute information, high-order structures, and low-order structures for node representation learning. Furthermore, we introduce an attribute-enhanced positive sample selection strategy that combines both structural information and attribute information, effectively addressing the issue of sampling bias. Extensive experiments on four real-world datasets show that ASHGCL outperforms state-of-the-art unsupervised baselines and even surpasses some supervised benchmarks.

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