Node-Time Conditional Prompt Learning In Dynamic Graphs

• URL: http://arxiv.org/abs/2405.13937v7
• Date: Sun, 13 Oct 2024 03:40:08 GMT
• Title: Node-Time Conditional Prompt Learning In Dynamic Graphs
• Authors: Xingtong Yu, Zhenghao Liu, Xinming Zhang, Yuan Fang,
• Abstract summary: We propose DYGPROMPT, a novel pre-training and prompt learning framework for dynamic graph modeling. 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.
• Score: 14.62182210205324
• License:
• Abstract: 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.

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