  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.
