  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.
