  Though graph representation learning (GRL) has made significant progress, it
is still a challenge to extract and embed the rich topological structure and
feature information in an adequate way. Most existing methods focus on local
structure and fail to fully incorporate the global topological structure. To
this end, we propose a novel Structure-Preserving Graph Representation Learning
(SPGRL) method, to fully capture the structure information of graphs.
Specifically, to reduce the uncertainty and misinformation of the original
graph, we construct a feature graph as a complementary view via k-Nearest
Neighbor method. The feature graph can be used to contrast at node-level to
capture the local relation. Besides, we retain the global topological structure
information by maximizing the mutual information (MI) of the whole graph and
feature embeddings, which is theoretically reduced to exchanging the feature
embeddings of the feature and the original graphs to reconstruct themselves.
Extensive experiments show that our method has quite superior performance on
semi-supervised node classification task and excellent robustness under noise
perturbation on graph structure or node features.
