Graph contrastive learning (GCL) has been an emerging solution for graph self-supervised learning. The core principle of GCL is to reduce the distance between samples in the positive view, but increase the distance between samples in the negative view. While achieving promising performances, current GCL methods still suffer from two limitations: (1) uncontrollable validity of augmentation, that graph perturbation may produce invalid views against semantics and feature-topology correspondence of graph data; and (2) unreliable binary contrastive justification, that the positiveness and negativeness of the constructed views are difficult to be determined for non-euclidean graph data. To tackle the above limitations, we propose a new contrastive learning paradigm for graphs, namely Graph Soft-Contrastive Learning (GSCL), that conducts contrastive learning in a finer-granularity via ranking neighborhoods without any augmentations and binary contrastive justification. GSCL is built upon the fundamental assumption of graph proximity that connected neighbors are more similar than far-distant nodes. Specifically, we develop pair-wise and list-wise Gated Ranking infoNCE Loss functions to preserve the relative ranking relationship in the neighborhood. Moreover, as the neighborhood size exponentially expands with more hops considered, we propose neighborhood sampling strategies to improve learning efficiency. The extensive experimental results show that our proposed GSCL can consistently achieve state-of-the-art performances on various public datasets with comparable practical complexity to GCL.

翻译：图形对比对比学习(GCL)是图形自我监督学习的新兴解决方案。 GCL的核心原则是减少正面观点样本之间的距离,但增加负面观点样本之间的距离。 在取得有希望的性能的同时,当前的GCL方法仍然受到两个限制:(1) 增强性无法控制的有效性, 图形扰动可能会产生无效的观点, 与图形数据的语义和地貌对比; (2) 不可靠的二进制对比解释, 构建的视图的正性和复杂性难以确定用于非euclide的图形数据。 为了应对上述限制, 我们为图表提出了一个新的对比性学习模式, 即“ 软- Contrastial 学习( GSCL) ”, 通过不增加任何增强性和二进制对比性对比性对应图形数据来进行对比性学习。 GSCL的建立基于图形接近性基本假设, 连接邻居比远远远偏近。 具体地, 我们开发双对式和列表化的排列式列表式排序 InfoNCE 损失( GSCL) 功能, 持续提升我们所考虑的深度排序, 展示的地理环境,, 不断提升我们所研究的地理环境, 展示的排序, 提升 提升 提高, 提高我们所提议 的地理区段 。