  Recently, graph neural networks (GNNs) have been shown powerful capacity at
modeling structural data. However, when adapted to downstream tasks, it usually
requires abundant task-specific labeled data, which can be extremely scarce in
practice. A promising solution to data scarcity is to pre-train a transferable
and expressive GNN model on large amounts of unlabeled graphs or coarse-grained
labeled graphs. Then the pre-trained GNN is fine-tuned on downstream datasets
with task-specific fine-grained labels. In this paper, we present a novel Graph
Matching based GNN Pre-Training framework, called GMPT. Focusing on a pair of
graphs, we propose to learn structural correspondences between them via neural
graph matching, consisting of both intra-graph message passing and inter-graph
message passing. In this way, we can learn adaptive representations for a given
graph when paired with different graphs, and both node- and graph-level
characteristics are naturally considered in a single pre-training task. The
proposed method can be applied to fully self-supervised pre-training and
coarse-grained supervised pre-training. We further propose an approximate
contrastive training strategy to significantly reduce time/memory consumption.
Extensive experiments on multi-domain, out-of-distribution benchmarks have
demonstrated the effectiveness of our approach. The code is available at:
https://github.com/RUCAIBox/GMPT.
