  Graph representation learning has attracted a surge of interest recently,
whose target at learning discriminant embedding for each node in the graph.
Most of these representation methods focus on supervised learning and heavily
depend on label information. However, annotating graphs are expensive to obtain
in the real world, especially in specialized domains (i.e. biology), as it
needs the annotator to have the domain knowledge to label the graph. To
approach this problem, self-supervised learning provides a feasible solution
for graph representation learning. In this paper, we propose a Multi-Level
Graph Contrastive Learning (MLGCL) framework for learning robust representation
of graph data by contrasting space views of graphs. Specifically, we introduce
a novel contrastive view - topological and feature space views. The original
graph is first-order approximation structure and contains uncertainty or error,
while the $k$NN graph generated by encoding features preserves high-order
proximity. Thus $k$NN graph generated by encoding features not only provide a
complementary view, but is more suitable to GNN encoder to extract discriminant
representation. Furthermore, we develop a multi-level contrastive mode to
preserve the local similarity and semantic similarity of graph-structured data
simultaneously. Extensive experiments indicate MLGCL achieves promising results
compared with the existing state-of-the-art graph representation learning
methods on seven datasets.
