自增强图对比学习
Self-Reinforced Graph Contrastive Learning
AI总结：
针对图对比学习中正样本对质量难以保障的问题，提出自增强图对比学习框架SRGCL，通过编码器动态评估筛选正样本对，在图级分类任务上性能优于现有最优方法。
摘要：
图作为一种通用数据结构，通过捕捉实体间复杂的关联信息，在社交网络、分子生物学、知识图谱等众多现实领域中得到广泛应用。在基于图的学习技术中，图对比学习（Graph Contrastive Learning, GCL）因能通过正负样本对的对比学习得到鲁棒的自监督图表示而受到广泛关注。然而，该领域的一个关键挑战是如何保证正样本对的质量，使其保留原始图的内在语义与结构属性而非造成信息扭曲。针对这一问题，我们提出SRGCL（自增强图对比学习）——一种利用模型自身编码器动态评估并筛选高质量正样本对的新型框架。我们设计了采用多种增强策略的统一正样本对生成器，以及由流形假设引导、用于维持隐空间底层几何结构的选择器。通过引入正样本对选择的概率机制，SRGCL会随着编码器表征能力的提升，迭代优化其对样本对质量的评估。在多种图级分类任务上开展的大量实验表明，SRGCL作为即插即用模块，性能始终优于当前最优的GCL方法，凸显了其在不同领域的适配性与有效性。
英文摘要：
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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