A Survey of Graph Prompting Methods: Techniques, Applications, and Challenges
This is an incremental survey paper that organizes existing work on graph prompting for researchers in machine learning and AI.
This survey reviews graph prompting methods, which augment prompting functions with graph knowledge to address the challenge of designing prompts in complex tasks, aiming to bridge the gap between graphs and prompt design for future methodology development.
The recent "pre-train, prompt, predict training" paradigm has gained popularity as a way to learn generalizable models with limited labeled data. The approach involves using a pre-trained model and a prompting function that applies a template to input samples, adding indicative context and reformulating target tasks as the pre-training task. However, the design of prompts could be a challenging and time-consuming process in complex tasks. The limitation can be addressed by using graph data, as graphs serve as structured knowledge repositories by explicitly modeling the interaction between entities. In this survey, we review prompting methods from the graph perspective, where prompting functions are augmented with graph knowledge. 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 future challenges. This survey will bridge the gap between graphs and prompt design to facilitate future methodology development.