  Graph contrastive learning (GCL) has recently emerged as an effective
learning paradigm to alleviate the reliance on labelling information for graph
representation learning. The core of GCL is to maximise the mutual information
between the representation of a node and its contextual representation (i.e.,
the corresponding instance with similar semantic information) summarised from
the contextual scope (e.g., the whole graph or 1-hop neighbourhood). This
scheme distils valuable self-supervision signals for GCL training. However,
existing GCL methods still suffer from limitations, such as the incapacity or
inconvenience in choosing a suitable contextual scope for different datasets
and building biased contrastiveness. To address aforementioned problems, we
present a simple self-supervised learning method termed Unifying Graph
Contrastive Learning with Flexible Contextual Scopes (UGCL for short). Our
algorithm builds flexible contextual representations with tunable contextual
scopes by controlling the power of an adjacency matrix. Additionally, our
method ensures contrastiveness is built within connected components to reduce
the bias of contextual representations. Based on representations from both
local and contextual scopes, UGCL optimises a very simple contrastive loss
function for graph representation learning. Essentially, the architecture of
UGCL can be considered as a general framework to unify existing GCL methods. We
have conducted intensive experiments and achieved new state-of-the-art
performance in six out of eight benchmark datasets compared with
self-supervised graph representation learning baselines. Our code has been
open-sourced.
