Self-supervised learning has gradually emerged as a powerful technique for
graph representation learning. However, transferable, generalizable, and robust
representation learning on graph data still remains a challenge for
pre-training graph neural networks. In this paper, we propose a simple and
effective self-supervised pre-training strategy, named Pairwise Half-graph
Discrimination (PHD), that explicitly pre-trains a graph neural network at
graph-level. PHD is designed as a simple binary classification task to
discriminate whether two half-graphs come from the same source. Experiments
demonstrate that the PHD is an effective pre-training strategy that offers
comparable or superior performance on 13 graph classification tasks compared
with state-of-the-art strategies, and achieves notable improvements when
combined with node-level strategies. Moreover, the visualization of learned
representation revealed that PHD strategy indeed empowers the model to learn
graph-level knowledge like the molecular scaffold. These results have
established PHD as a powerful and effective self-supervised learning strategy
in graph-level representation learning.

本文提出了一种名为 PHD 的简单而有效的自监督预训练策略，通过在图级别上明确地预训练图神经网络，实现了对图数据的可传递、可推广和稳健的表征学习。结果表明，PHD 是一种有效的预训练策略，在与现有先进策略的比较中提供了相当或更优的性能，当与节点级别策略相结合时可实现显着的提高。