Recently, much exertion has been paid to design graph self-supervised methods to obtain generalized pre-trained models, and adapt pre-trained models onto downstream tasks through fine-tuning. However, there exists an inherent gap between pretext and downstream graph tasks, which insufficiently exerts the ability of pre-trained models and even leads to negative transfer. Meanwhile, prompt tuning has seen emerging success in natural language processing by aligning pre-training and fine-tuning with consistent training objectives. In this paper, we identify the challenges for graph prompt tuning: The first is the lack of a strong and universal pre-training task across sundry pre-training methods in graph domain. The second challenge lies in the difficulty of designing a consistent training objective for both pre-training and downstream tasks. To overcome above obstacles, we propose a novel framework named SGL-PT which follows the learning strategy ``Pre-train, Prompt, and Predict''. Specifically, we raise a strong and universal pre-training task coined as SGL that acquires the complementary merits of generative and contrastive self-supervised graph learning. And aiming for graph classification task, we unify pre-training and fine-tuning by designing a novel verbalizer-free prompting function, which reformulates the downstream task in a similar format as pretext task. Empirical results show that our method surpasses other baselines under unsupervised setting, and our prompt tuning method can greatly facilitate models on biological datasets over fine-tuning methods.

翻译：近期，大量研究致力于设计图自监督方法以获取通用预训练模型，并通过微调将预训练模型适配至下游任务。然而，预训练与下游任务间存在固有鸿沟，导致预训练模型能力无法充分发挥，甚至引发负迁移。与此同时，提示调优通过保持预训练与微调阶段训练目标的一致性，已在自然语言处理领域取得显著成功。本文识别了图提示调优面临的挑战：其一，图领域缺乏横跨各类预训练方法的强通用预训练任务；其二，设计预训练与下游任务一致训练目标存在困难。为克服上述障碍，我们提出遵循"预训练-提示-预测"学习策略的新型框架SGL-PT。具体而言，我们提出名为SGL的强通用预训练任务，融合生成式与对比式自监督图学习的互补优势。针对图分类任务，我们通过设计无词汇器的提示函数统一预训练与微调，使下游任务格式与预训练任务保持一致。实验结果表明，我们的方法在无监督设置下超越其他基线，且提示调优方法在生物数据集上的效果显著优于微调方法。