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{this https URL}, and
\url{this https URL}, respectively.

人工智能总体智能（AGI）已经在许多领域取得了革命性的成就，然而其与图数据的整合，在我们相互连接的世界中仍然尚未充分开发。本文对图智能的新兴领域进行了开创性的调查，探讨了利用图数据进行 AGI 应用的关键挑战和机遇。尽管在自然语言处理和计算机视觉领域已经取得了实质性的进展，但在图数据方面的应用还未得到充分开发。本调查对当前 AGI 处理图数据的现状进行了批判性评估，突出了在图领域中特定于跨模态、跨领域和跨任务应用的不同挑战。我们的工作首次提出了一个统一的框架，用于理解图提示学习，并明确了图领域中的提示标记、标记结构和插入模式。我们深入探讨了图提示的内在特性，探讨了其灵活性、表达能力和与现有图模型的相互作用。我们系统地对该领域的 100 多项工作进行分类，将它们与节点级、边级和图级目标的预训练任务相对应。此外，我们还提供了一个名为 ProG 的 Python 库和一个配套的网站，以支持和推动图提示的研究。本调查总结了当前挑战和未来方向的讨论，为 AGI 中的图提示研究提供了路线图。通过这个全面的分析，我们的目标是激发对 AGI 在图数据中进一步探索和实际应用的兴趣，凸显其重塑 AGI 领域及其他领域的潜力。ProG 库和网站可以通过相应的 URL 进行访问。