  Graph instance contrastive learning has been proved as an effective task for
Graph Neural Network (GNN) pre-training. However, one key issue may seriously
impede the representative power in existing works: Positive instances created
by current methods often miss crucial information of graphs or even yield
illegal instances (such as non-chemically-aware graphs in molecular
generation). To remedy this issue, we propose to select positive graph
instances directly from existing graphs in the training set, which ultimately
maintains the legality and similarity to the target graphs. Our selection is
based on certain domain-specific pair-wise similarity measurements as well as
sampling from a hierarchical graph encoding similarity relations among graphs.
Besides, we develop an adaptive node-level pre-training method to dynamically
mask nodes to distribute them evenly in the graph. We conduct extensive
experiments on $13$ graph classification and node classification benchmark
datasets from various domains. The results demonstrate that the GNN models
pre-trained by our strategies can outperform those trained-from-scratch models
as well as the variants obtained by existing methods.
