In recent years, "pre-training and fine-tuning" has emerged as a promising approach in addressing the issues of label dependency and poor generalization performance in traditional GNNs. To reduce labeling requirement, the "pre-train, fine-tune" and "pre-train, prompt" paradigms have become increasingly common. In particular, prompt tuning is a popular alternative to "pre-training and fine-tuning" in natural language processing, which is designed to narrow the gap between pre-training and downstream objectives. However, existing study of prompting on graphs is still limited, lacking a framework that can accommodate commonly used graph pre-training methods and downstream tasks. In this paper, we propose a multi-view graph contrastive learning method as pretext and design a prompting tuning for it. Specifically, we first reformulate graph pre-training and downstream tasks into a common format. Second, we construct multi-view contrasts to capture relevant information of graphs by GNN. Third, we design a prompting tuning method for our multi-view graph contrastive learning method to bridge the gap between pretexts and downsteam tasks. Finally, we conduct extensive experiments on benchmark datasets to evaluate and analyze our proposed method.

翻译：近年来，“预训练与微调”已成为解决传统图神经网络中标签依赖性强和泛化性能差问题的有效途径。为降低标签需求，“预训练-微调”与“预训练-提示”范式日益普及。其中，提示调优作为自然语言处理中“预训练与微调”的替代方案，旨在缩小预训练目标与下游任务目标之间的差距。然而，现有关于图提示学习的研究仍存在局限性，缺乏能够兼容主流图预训练方法与下游任务的统一框架。本文提出一种以多视图图对比学习为预训练任务的方法，并为其设计提示调优策略。具体而言，我们首先将图预训练与下游任务重新表述为统一形式；其次，通过图神经网络构建多视图对比以捕获图的相关信息；再次，针对多视图图对比学习方法设计提示调优策略，以弥合预训练任务与下游任务之间的差距；最后，在基准数据集上开展大量实验以评估和分析所提方法。