  Recent studies show that graph convolutional network (GCN) often performs
worse for low-degree nodes, exhibiting the so-called structural unfairness for
graphs with long-tailed degree distributions prevalent in the real world. Graph
contrastive learning (GCL), which marries the power of GCN and contrastive
learning, has emerged as a promising self-supervised approach for learning node
representations. How does GCL behave in terms of structural fairness?
Surprisingly, we find that representations obtained by GCL methods are already
fairer to degree bias than those learned by GCN. We theoretically show that
this fairness stems from intra-community concentration and inter-community
scatter properties of GCL, resulting in a much clear community structure to
drive low-degree nodes away from the community boundary. Based on our
theoretical analysis, we further devise a novel graph augmentation method,
called GRAph contrastive learning for DEgree bias (GRADE), which applies
different strategies to low- and high-degree nodes. Extensive experiments on
various benchmarks and evaluation protocols validate the effectiveness of the
proposed method.
