Self-Reinforced Graph Contrastive Learning

• URL: http://arxiv.org/abs/2505.13650v1
• Date: Mon, 19 May 2025 18:45:54 GMT
• Title: Self-Reinforced Graph Contrastive Learning
• Authors: Chou-Ying Hsieh, Chun-Fu Jang, Cheng-En Hsieh, Qian-Hui Chen, Sy-Yen Kuo,
• Abstract summary: We propose SRGCL (Self-Reinforced Graph Contrastive Learning), a novel framework to dynamically evaluate and select high-quality positive pairs.<n>In experiments on diverse graph-level classification tasks, SRGCL consistently outperforms state-of-the-art GCL methods.
• Score: 7.49025068464945
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Graphs serve as versatile data structures in numerous real-world domains-including social networks, molecular biology, and knowledge graphs-by capturing intricate relational information among entities. Among graph-based learning techniques, Graph Contrastive Learning (GCL) has gained significant attention for its ability to derive robust, self-supervised graph representations through the contrasting of positive and negative sample pairs. However, a critical challenge lies in ensuring high-quality positive pairs so that the intrinsic semantic and structural properties of the original graph are preserved rather than distorted. To address this issue, we propose SRGCL (Self-Reinforced Graph Contrastive Learning), a novel framework that leverages the model's own encoder to dynamically evaluate and select high-quality positive pairs. We designed a unified positive pair generator employing multiple augmentation strategies, and a selector guided by the manifold hypothesis to maintain the underlying geometry of the latent space. By adopting a probabilistic mechanism for selecting positive pairs, SRGCL iteratively refines its assessment of pair quality as the encoder's representational power improves. Extensive experiments on diverse graph-level classification tasks demonstrate that SRGCL, as a plug-in module, consistently outperforms state-of-the-art GCL methods, underscoring its adaptability and efficacy across various domains.

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