  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
capability 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.
