  Contrastive learning (CL) has proven highly effective in graph-based
semi-supervised learning (SSL), since it can efficiently supplement the limited
task information from the annotated nodes in graph. However, existing graph CL
(GCL) studies ignore the uneven distribution of task information across graph
caused by the graph topology and the selection of annotated nodes. They apply
CL to the whole graph evenly, which results in an incongruous combination of CL
and graph learning. To address this issue, we propose to apply CL in the graph
learning adaptively by taking the received task information of each node into
consideration. Firstly, we introduce Group PageRank to measure the node
information gain from graph and find that CL mainly works for nodes that are
topologically far away from the labeled nodes. We then propose our
Distance-wise Graph Contrastive Learning (DwGCL) method from two views:(1) From
the global view of the task information distribution across the graph, we
enhance the CL effect on nodes that are topologically far away from labeled
nodes; (2) From the personal view of each node's received information, we
measure the relative distance between nodes and then we adapt the sampling
strategy of GCL accordingly. Extensive experiments on five benchmark graph
datasets show that DwGCL can bring a clear improvement over previous GCL
methods. Our analysis on eight graph neural network with various types of
architecture and three different annotation settings further demonstrates the
generalizability of DwGCL.
