  Graph-level contrastive learning, aiming to learn the representations for
each graph by contrasting two augmented graphs, has attracted considerable
attention. Previous studies usually simply assume that a graph and its
augmented graph as a positive pair, otherwise as a negative pair. However, it
is well known that graph structure is always complex and multi-scale, which
gives rise to a fundamental question: after graph augmentation, will the
previous assumption still hold in reality? By an experimental analysis, we
discover the semantic information of an augmented graph structure may be not
consistent as original graph structure, and whether two augmented graphs are
positive or negative pairs is highly related with the multi-scale structures.
Based on this finding, we propose a multi-scale subgraph contrastive learning
architecture which is able to characterize the fine-grained semantic
information. Specifically, we generate global and local views at different
scales based on subgraph sampling, and construct multiple contrastive
relationships according to their semantic associations to provide richer
self-supervised signals. Extensive experiments and parametric analyzes on eight
graph classification real-world datasets well demonstrate the effectiveness of
the proposed method.
