MultiGPrompt for Multi-Task Pre-Training and Prompting on Graphs

• URL: http://arxiv.org/abs/2312.03731v7
• Date: Mon, 26 Aug 2024 10:11:45 GMT
• Title: MultiGPrompt for Multi-Task Pre-Training and Prompting on Graphs
• Authors: Xingtong Yu, Chang Zhou, Yuan Fang, Xinming Zhang,
• Abstract summary: MultiGPrompt is a novel multi-task pre-training and prompting framework for graph representation learning. We propose a dual-prompt mechanism consisting of composed and open prompts to leverage task-specific and global pre-training knowledge.
• Score: 33.2696184519275
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Graphs can inherently model interconnected objects on the Web, thereby facilitating a series of Web applications, such as web analyzing and content recommendation. Recently, Graph Neural Networks (GNNs) have emerged as a mainstream technique for graph representation learning. However, their efficacy within an end-to-end supervised framework is significantly tied to the availabilityof task-specific labels. To mitigate labeling costs and enhance robustness in few-shot settings, pre-training on self-supervised tasks has emerged as a promising method, while prompting has been proposed to further narrow the objective gap between pretext and downstream tasks. Although there has been some initial exploration of prompt-based learning on graphs, they primarily leverage a single pretext task, resulting in a limited subset of general knowledge that could be learned from the pre-training data. Hence, in this paper, we propose MultiGPrompt, a novel multi-task pre-training and prompting framework to exploit multiple pretext tasks for more comprehensive pre-trained knowledge. First, in pre-training, we design a set of pretext tokens to synergize multiple pretext tasks. Second, we propose a dual-prompt mechanism consisting of composed and open prompts to leverage task-specific and global pre-training knowledge, to guide downstream tasks in few-shot settings. Finally, we conduct extensive experiments on six public datasets to evaluate and analyze MultiGPrompt.

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