  Traditional Graph Neural Network (GNN), as a graph representation learning
method, is constrained by label information. However, Graph Contrastive
Learning (GCL) methods, which tackle the label problem effectively, mainly
focus on the feature information of the global graph or small subgraph
structure (e.g., the first-order neighborhood). In the paper, we propose a
Local Structure-aware Graph Contrastive representation Learning method (LS-GCL)
to model the structural information of nodes from multiple views. Specifically,
we construct the semantic subgraphs that are not limited to the first-order
neighbors. For the local view, the semantic subgraph of each target node is
input into a shared GNN encoder to obtain the target node embeddings at the
subgraph-level. Then, we use a pooling function to generate the subgraph-level
graph embeddings. For the global view, considering the original graph preserves
indispensable semantic information of nodes, we leverage the shared GNN encoder
to learn the target node embeddings at the global graph-level. The proposed
LS-GCL model is optimized to maximize the common information among similar
instances at three various perspectives through a multi-level contrastive loss
function. Experimental results on five datasets illustrate that our method
outperforms state-of-the-art graph representation learning approaches for both
node classification and link prediction tasks.
