  We study self-supervised learning on graphs using contrastive methods. A
general scheme of prior methods is to optimize two-view representations of
input graphs. In many studies, a single graph-level representation is computed
as one of the contrastive objectives, capturing limited characteristics of
graphs. We argue that contrasting graphs in multiple subspaces enables graph
encoders to capture more abundant characteristics. To this end, we propose a
group contrastive learning framework in this work. Our framework embeds the
given graph into multiple subspaces, of which each representation is prompted
to encode specific characteristics of graphs. To learn diverse and informative
representations, we develop principled objectives that enable us to capture the
relations among both intra-space and inter-space representations in groups.
Under the proposed framework, we further develop an attention-based representor
function to compute representations that capture different substructures of a
given graph. Built upon our framework, we extend two current methods into
GroupCL and GroupIG, equipped with the proposed objective. Comprehensive
experimental results show our framework achieves a promising boost in
performance on a variety of datasets. In addition, our qualitative results show
that features generated from our representor successfully capture various
specific characteristics of graphs.
