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，大量工作致力于设计图自监督方法以获取泛化预训练模型，并通过微调将预训练模型适配至下游任务。然而，预文本任务与下游图任务之间存在固有差异，这不仅无法充分发挥预训练模型的能力，甚至可能导致负迁移。与此同时，提示调优在自然语言处理领域通过保持预训练与微调的训练目标一致性而取得了显著成功。本文揭示了图提示调优面临的两项挑战：其一，图领域缺乏跨越多类预训练方法的强通用预训练任务；其二，为预训练与下游任务设计一致的训练目标存在困难。为克服上述障碍，我们提出名为SGL-PT的新型框架，该框架遵循“预训练-提示-预测”学习策略。具体而言，我们提出名为SGL的强通用预训练任务，该任务融合了生成式与对比式自监督图学习的互补优势；面向图分类任务，我们通过设计新型无动词提示函数统一预训练与微调过程，该函数将下游任务重构为与预文本任务相似的格式。实验结果表明，本方法在无监督设置下超越其他基线，且我们的提示调优方法在生物数据集上较微调方法能显著提升模型性能。