  The pre-training on the graph neural network model can learn the general
features of large-scale networks or networks of the same type by
self-supervised methods, which allows the model to work even when node labels
are missing. However, the existing pre-training methods do not take network
evolution into consideration. This paper proposes a pre-training method on
dynamic graph neural networks (PT-DGNN), which uses dynamic attributed graph
generation tasks to simultaneously learn the structure, semantics, and
evolution features of the graph. The method includes two steps: 1) dynamic
sub-graph sampling, and 2) pre-training with dynamic attributed graph
generation task. Comparative experiments on three realistic dynamic network
datasets show that the proposed method achieves the best results on the link
prediction fine-tuning task.
