Graph contrastive learning (GCL) aligns node representations by classifying node pairs into positives and negatives using a selection process that typically relies on establishing correspondences within two augmented graphs. The conventional GCL approaches incorporate negative samples uniformly in the contrastive loss, resulting in the equal treatment negative nodes, regardless of their proximity to the true positive. In this paper, we present a Smoothed Graph Contrastive Learning model (SGCL), which leverages the geometric structure of augmented graphs to inject proximity information associated with positive/negative pairs in the contrastive loss, thus significantly regularizing the learning process. The proposed SGCL adjusts the penalties associated with node pairs in the contrastive loss by incorporating three distinct smoothing techniques that result in proximity aware positives and negatives. To enhance scalability for large-scale graphs, the proposed framework incorporates a graph batch-generating strategy that partitions the given graphs into multiple subgraphs, facilitating efficient training in separate batches. Through extensive experimentation in the unsupervised setting on various benchmarks, particularly those of large scale, we demonstrate the superiority of our proposed framework against recent baselines.

翻译：图对比学习（GCL）通过将节点对分类为正样本和负样本（通常依赖于在两个增强图内建立对应关系的选择过程）来对齐节点表示。传统GCL方法在对比损失中均匀处理负样本，导致所有负节点（无论其与真正样本的邻近程度如何）受到同等对待。本文提出一种平滑图对比学习模型（SGCL），该模型利用增强图的几何结构将正负样本对相关的邻近信息注入对比损失，从而显著正则化学习过程。所提出的SGCL通过整合三种不同的平滑技术来调整对比损失中节点对的惩罚权重，从而生成具有邻近感知能力的正负样本。为增强在大规模图上的可扩展性，该框架引入了一种图批次生成策略，将给定图划分为多个子图，支持在独立批次中高效训练。通过在无监督设置下对多种基准数据集（特别针对大规模场景）进行广泛实验，我们证明了所提框架相较于最新基线方法的优越性。