While deep learning has achieved great success on various tasks, the task-specific model training notoriously relies on a large volume of labeled data. Recently, a new training paradigm of ``pre-train, prompt, predict'' has been proposed to improve model generalization ability with limited labeled data. The main idea is that, based on a pre-trained model, the prompting function uses a template to augment input samples with indicative context and reformalizes the target task to one of the pre-training tasks. In this survey, we provide a unique review of prompting methods from the graph perspective. Graph data has served as structured knowledge repositories in various systems by explicitly modeling the interaction between entities. Compared with traditional methods, graph prompting functions could induce task-related context and apply templates with structured knowledge. The pre-trained model is then adaptively generalized for future samples. In particular, we introduce the basic concepts of graph prompt learning, organize the existing work of designing graph prompting functions, and describe their applications and challenges to a variety of machine learning problems. This survey attempts to bridge the gap between structured graphs and prompt design to facilitate future methodology development.

翻译：尽管深度学习在各种任务上取得了巨大成功，但特定任务的模型训练通常依赖大量标注数据。近年来，一种"预训练-提示-预测"的新型训练范式被提出，旨在利用有限标注数据提升模型泛化能力。其核心思想是：基于预训练模型，提示函数通过模板为输入样本添加指示性上下文，并将目标任务重新形式化为预训练任务之一。本综述从图视角对提示方法进行独特回顾。图数据通过显式建模实体间交互，已成为各类系统中的结构化知识库。与传统方法相比，图提示函数能引入任务相关上下文，并应用结构化知识模板，使预训练模型能自适应地泛化至后续样本。具体而言，我们介绍了图提示学习的基本概念，梳理了现有图提示函数设计工作，并阐述其在多种机器学习问题中的应用与挑战。本综述旨在弥合结构化图与提示设计之间的鸿沟，推动未来方法学发展。