Recently, ''pre-training and fine-tuning'' has been adopted as a standard
workflow for many graph tasks since it can take general graph knowledge to
relieve the lack of graph annotations from each application. However, graph
tasks with node level, edge level, and graph level are far diversified, making
the pre-training pretext often incompatible with these multiple tasks. This gap
may even cause a ''negative transfer'' to the specific application, leading to
poor results. Inspired by the prompt learning in natural language processing
(NLP), which has presented significant effectiveness in leveraging prior
knowledge for various NLP tasks, we study the prompting topic for graphs with
the motivation of filling the gap between pre-trained models and various graph
tasks. In this paper, we propose a novel multi-task prompting method for graph
models. Specifically, we first unify the format of graph prompts and language
prompts with the prompt token, token structure, and inserting pattern. In this
way, the prompting idea from NLP can be seamlessly introduced to the graph
area. Then, to further narrow the gap between various graph tasks and
state-of-the-art pre-training strategies, we further study the task space of
various graph applications and reformulate downstream problems to the
graph-level task. Afterward, we introduce meta-learning to efficiently learn a
better initialization for the multi-task prompt of graphs so that our prompting
framework can be more reliable and general for different tasks. We conduct
extensive experiments, results from which demonstrate the superiority of our
method.

本文引入了提示方法来弥补预训练模型与各种图形任务之间的差距，并提出了一种新的用于图形模型的多任务提示方法。经过实验，结果表明我们的方法优越性。