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.

本文介绍了一种平滑图对比学习模型（SGCL），它利用了扩充图的几何结构来在对比损失中注入与正负节点对相关的接近信息，从而显著规范化学习过程，并通过针对负样本的三种不同平滑技术调整对比损失中节点对的惩罚，以提高在大规模图上的可扩展性，通过在各种基准测试中的大量实验证明了该框架相对于最近的基准方法的优越性。

平滑图对比学习中的无缝相似性融合