  Graph Contrastive Learning (GCL) has proven highly effective in promoting the
performance of Semi-Supervised Node Classification (SSNC). However, existing
GCL methods are generally transferred from other fields like CV or NLP, whose
underlying working mechanism remains under-explored. In this work, we first
deeply probe the working mechanism of GCL in SSNC, and find that the promotion
brought by GCL is severely unevenly distributed: the improvement mainly comes
from subgraphs with less annotated information, which is fundamentally
different from contrastive learning in other fields. However, existing GCL
methods generally ignore this uneven distribution of annotated information and
apply GCL evenly to the whole graph. To remedy this issue and further improve
GCL in SSNC, we propose the Topology InFormation gain-Aware Graph Contrastive
Learning (TIFA-GCL) framework that considers the annotated information
distribution across graph in GCL. Extensive experiments on six benchmark graph
datasets, including the enormous OGB-Products graph, show that TIFA-GCL can
bring a larger improvement than existing GCL methods in both transductive and
inductive settings. Further experiments demonstrate the generalizability and
interpretability of TIFA-GCL.
