  Dynamic graphs are pervasive in the real world, modeling dynamic relations
between objects across various fields. For dynamic graph modeling, dynamic
graph neural networks (DGNNs) have emerged as a mainstream technique, which are
generally pre-trained on the link prediction task, leaving a significant gap
from the objectives of downstream tasks such as node classification. To bridge
the gap, prompt-based learning has gained traction on graphs. However, existing
efforts focus on static graphs, neglecting the evolution of dynamic graphs. In
this paper, we propose DyGPrompt, a novel pre-training and prompting framework
for dynamic graph modeling. First, we design dual prompts to address the gap in
both task objectives and dynamic variations across pre-training and downstream
tasks. Second, we recognize that node and time features mutually characterize
each other, and propose dual condition-nets to model the evolving node-time
patterns in downstream tasks. Finally, we thoroughly evaluate and analyze
DyGPrompt through extensive experiments on three public datasets.
