MultiGPrompt for Multi-Task Pre-Training and Prompting on Graphs
Xingtong Yu,
Chang Zhou,
Yuan Fang
et al.
Abstract: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 availability of 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 so… Show more
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