DyGPrompt: Learning Feature and Time Prompts on Dynamic Graphs
<|reference_start|>DyGPrompt: Learning Feature and Time Prompts on Dynamic Graphs: Dynamic graphs capture evolving interactions between entities, such as in social networks, online learning platforms, and crowdsourcing projects. For dynamic graph modeling, dynamic graph neural networks (DGNNs) have emerged as a mainstream technique. However, they 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, but most existing efforts focus on static graphs, neglecting the evolution of dynamic graphs. In this paper, we propose DYGPROMPT, a novel pre-training and prompt learning framework for dynamic graph modeling. First, we design dual prompts to address the gap in both task objectives and temporal 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 four public datasets.<|reference_end|>
@article{yu2024node-time,
    title={Node-Time Conditional Prompt Learning In Dynamic Graphs},
    author={Xingtong Yu, Zhenghao Liu, Xinming Zhang, Yuan Fang},
    journal={arXiv preprint arXiv:2405.13937},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.13937},
    primaryClass={cs.LG}
}