Dynamic graph data mining has gained popularity in recent years due to the
rich information contained in dynamic graphs and their widespread use in the
real world. Despite the advances in dynamic graph neural networks (DGNNs), the
rich information and diverse downstream tasks have posed significant
difficulties for the practical application of DGNNs in industrial scenarios. To
this end, in this paper, we propose to address them by pre-training and present
the Contrastive Pre-Training Method for Dynamic Graph Neural Networks (CPDG).
CPDG tackles the challenges of pre-training for DGNNs, including generalization
and long-short term modeling capability, through a flexible structural-temporal
subgraph sampler along with structural-temporal contrastive pre-training
schemes. Extensive experiments conducted on both large-scale research and
industrial dynamic graph datasets show that CPDG outperforms existing methods
in dynamic graph pre-training for various downstream tasks under three transfer
settings.

本文提出了一种针对动态图神经网络的对比预训练方法 (CPDG)，通过灵活的结构 - 时间子图采样器和结构 - 时间对比预训练机制，解决了动态图神经网络在预训练中面临的泛化和长短期建模能力等挑战，并在大规模研究和工业动态图数据集上展开广泛实验，证明了 CPDG 在三种迁移设置下优于现有方法的动态图预训练能力。