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Recently, much exertion has been paid to design graph self-supervised methodsto obtain generalized pre-trained models, and adapt pre-trained models ontodownstream tasks through fine-tuning. However, there exists an inherent gapbetween pretext and downstream graph tasks, which insufficiently exerts theability of pre-trained models and even leads to negative transfer. Meanwhile,prompt tuning has seen emerging success in natural language processing byaligning pre-training and fine-tuning with consistent training objectives. Inthis paper, we identify the challenges for graph prompt tuning: The first isthe lack of a strong and universal pre-training task across sundry pre-trainingmethods in graph domain. The second challenge lies in the difficulty ofdesigning a consistent training objective for both pre-training and downstreamtasks. To overcome above obstacles, we propose a novel framework named SGL-PTwhich follows the learning strategy ``Pre-train, Prompt, and Predict''.Specifically, we raise a strong and universal pre-training task coined as SGLthat acquires the complementary merits of generative and contrastiveself-supervised graph learning. And aiming for graph classification task, weunify pre-training and fine-tuning by designing a novel verbalizer-freeprompting function, which reformulates the downstream task in a similar formatas pretext task. Empirical results show that our method surpasses otherbaselines under unsupervised setting, and our prompt tuning method can greatlyfacilitate models on biological datasets over fine-tuning methods.

SGL-PT: A Strong Graph Learner with Graph Prompt Tuning