  Artificial General Intelligence (AGI) has revolutionized numerous fields, yet
its integration with graph data, a cornerstone in our interconnected world,
remains nascent. This paper presents a pioneering survey on the emerging domain
of graph prompts in AGI, addressing key challenges and opportunities in
harnessing graph data for AGI applications. Despite substantial advancements in
AGI across natural language processing and computer vision, the application to
graph data is relatively underexplored. This survey critically evaluates the
current landscape of AGI in handling graph data, highlighting the distinct
challenges in cross-modality, cross-domain, and cross-task applications
specific to graphs. Our work is the first to propose a unified framework for
understanding graph prompt learning, offering clarity on prompt tokens, token
structures, and insertion patterns in the graph domain. We delve into the
intrinsic properties of graph prompts, exploring their flexibility,
expressiveness, and interplay with existing graph models. A comprehensive
taxonomy categorizes over 100 works in this field, aligning them with
pre-training tasks across node-level, edge-level, and graph-level objectives.
Additionally, we present, ProG, a Python library, and an accompanying website,
to support and advance research in graph prompting. The survey culminates in a
discussion of current challenges and future directions, offering a roadmap for
research in graph prompting within AGI. Through this comprehensive analysis, we
aim to catalyze further exploration and practical applications of AGI in graph
data, underlining its potential to reshape AGI fields and beyond. ProG and the
website can be accessed by
\url{https://github.com/WxxShirley/Awesome-Graph-Prompt}, and
\url{https://github.com/sheldonresearch/ProG}, respectively.
