All in One：面向图神经网络的多任务提示（扩展摘要）
All in One: Multi-Task Prompting for Graph Neural Networks (Extended Abstract)
AI总结：
该扩展摘要基于获KDD23最佳论文的图多任务提示研究，提出统一图与语言提示格式、重构任务为图级并结合元学习优化提示初始化的方法，提升多类图任务性能，还补充了领域最新进展。
摘要：
本文是我们发表于KDD23的原创工作的扩展摘要，该工作曾获最佳研究论文奖（Xiangguo Sun、Hong Cheng、Jia Li、Bo Liu与Jihong Guan，《All in one: Multi-task prompting for graph neural networks》，KDD 23）。受NLP领域提示学习成功应用的启发，本文提出一种新方法，以弥合预训练图模型与其所应用的多样化任务之间的差距。我们意识到，将预训练模型与节点级、边级、图级等各类图任务对齐存在挑战，可能导致负迁移与性能不佳的问题，因此提出了一种面向图的多任务提示方法。该方法统一了图与语言的提示格式，使得NLP的提示策略能够适配图任务。通过分析图应用的任务空间，我们将各类问题重构为图级任务，并应用元学习优化多任务的提示初始化。实验表明，该方法能够有效提升模型在不同图任务上的性能。除原有工作外，本扩展摘要还从更宏观的视角进一步探讨了图提示，并介绍了该领域的部分最新研究进展。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
