  This paper is an extended abstract of our original work published in KDD23,
where we won the best research paper award (Xiangguo Sun, Hong Cheng, Jia Li,
Bo Liu, and Jihong Guan. All in one: Multi-task prompting for graph neural
networks. KDD 23) The paper introduces a novel approach to bridging the gap
between pre-trained graph models and the diverse tasks they're applied to,
inspired by the success of prompt learning in NLP. Recognizing the challenge of
aligning pre-trained models with varied graph tasks (node level, edge level,
and graph level), which can lead to negative transfer and poor performance, we
propose a multi-task prompting method for graphs. This method involves unifying
graph and language prompt formats, enabling NLP's prompting strategies to be
adapted for graph tasks. By analyzing the task space of graph applications, we
reformulate problems to fit graph-level tasks and apply meta-learning to
improve prompt initialization for multiple tasks. Experiments show our method's
effectiveness in enhancing model performance across different graph tasks.
  Beyond the original work, in this extended abstract, we further discuss the
graph prompt from a bigger picture and provide some of the latest work toward
this area.
