Graph Contrastive Learning (GCL) has been an emerging solution for graph
self-supervised learning. Existing GCL methods always adopt the
binary-contrastive setting: making binary decisions (positive/negative pairs)
on the generated views, pulling positive (similar) pairs close and pushing
negative (dissimilar) pairs far away. Despite promising performances, two
critical issues arise: (i) the validity of view construction cannot be
guaranteed: graph perturbation may produce invalid views against semantics and
intrinsic topology of graph data; (ii) the binary-contrastive setting is
unreliable: for non-euclidean graph data, positive (similar) pairs and negative
(dissimilar) pairs are very difficult to be decided. The two problems further
raise the research question: Is the binary-contrastive setting really necessary
for graph contrastive learning? In this paper, we investigate this research
question and introduce a novel GCL paradigm, namely Graph Soft-Contrastive
Learning (GSCL), which can conduct graph self-supervise learning via
neighborhood ranking without relying on the binary-contrastive setting.
Specifically, GSCL is built upon the basic assumption of graph homophily that
connected neighbors are more similar than far-distant nodes. Then, under the
GSCL paradigm, 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. Extensive experimental results show that GSCL can achieve
competitive or even superior performance compared with current state-of-the-art
GCL methods. We expect our work can stimulate more research efforts to jump out
of the traditional binary-contrastive setting and conform to the inherent
characteristics and properties of graphs.

本文提出了一种新的图形无监督学习方法，名为 Graph Soft-Contrastive Learning (GSCL)，它能够通过邻域排序进行自我监督学习，而无需依赖于二元对比设置，并且还提出了 GSCL 所需的成对和列表式门控排名 infoNCE 损失函数，以保留邻域中的相对排序关系。