Graph contrastive learning defines a contrastive task to pull similar instances close and push dissimilar instances away. It learns discriminative node embeddings without supervised labels, which has aroused increasing attention in the past few years. Nevertheless, existing methods of graph contrastive learning ignore the differences between diverse semantics existed in graphs, which learn coarse-grained node embeddings and lead to sub-optimal performances on downstream tasks. To bridge this gap, we propose a novel Fine-grained Semantics enhanced Graph Contrastive Learning (FSGCL) in this paper. Concretely, FSGCL first introduces a motif-based graph construction, which employs graph motifs to extract diverse semantics existed in graphs from the perspective of input data. Then, the semantic-level contrastive task is explored to further enhance the utilization of fine-grained semantics from the perspective of model training. Experiments on five real-world datasets demonstrate the superiority of our proposed FSGCL over state-of-the-art methods. To make the results reproducible, we will make our codes public on GitHub after this paper is accepted.

本文提出了一种新的细粒度语义增强的图对比学习方法（FSGCL），采用基于图形的结构构造，提取具有不同语义的图形，并从模型训练的角度进一步增强对细粒度语义的利用。实验表明，相对于现有的方法，所提出的 FSGCL 方法具有更好的性能。

对比图表示学习中的细粒度语义捕捉