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.

本文提出了一种名为 SGL-PT 的新框架，其中采用 “预训练、提示和预测” 的学习策略，通过采用有生成和对比自监导的结构图（SGL）和基于口头指导的方式，将预训练和微调统一到了一起，从而在图分类任务方面取得了较好的成果。