Recent years have witnessed a thriving growth of computing facilities connected at the network edge, cultivating edge networks as a fundamental infrastructure for supporting miscellaneous intelligent services.Meanwhile, Artificial Intelligence (AI) frontiers have extrapolated to the graph domain and promoted Graph Intelligence (GI). Given the inherent relation between graphs and networks, the interdiscipline of graph learning and edge networks, i.e., Edge GI or EGI, has revealed a novel interplay between them -- GI aids in optimizing edge networks, while edge networks facilitate GI model deployment. Driven by this delicate closed-loop, EGI is recognized as a promising solution to fully unleash the potential of edge computing power and is garnering growing attention. Nevertheless, research on EGI remains nascent, and there is a soaring demand within both the communications and AI communities for a dedicated venue to share recent advancements. To this end, this paper promotes the concept of EGI, explores its scope and core principles, and conducts a comprehensive survey concerning recent research efforts on this emerging field. Specifically, this paper introduces and discusses: 1) fundamentals of edge computing and graph learning,2) emerging techniques centering on the closed loop between graph intelligence and edge networks, and 3) open challenges and research opportunities of future EGI. By bridging the gap across communication, networking, and graph learning areas, we believe that this survey can garner increased attention, foster meaningful discussions, and inspire further research ideas in EGI.

翻译：近年来，网络边缘连接的计算设施蓬勃发展，使边缘网络成为支撑各类智能服务的基础设施。与此同时，人工智能（AI）前沿已拓展至图领域，并推动了图智能（GI）的发展。鉴于图与网络之间的内在关联，图学习与边缘网络的交叉学科——即边缘图智能（EGI）——揭示了两者之间一种新颖的互动关系：图智能有助于优化边缘网络，而边缘网络则促进图智能模型的部署。在这一精妙的闭环驱动下，EGI被视为充分释放边缘计算潜力的有前景的解决方案，并正获得越来越多的关注。然而，EGI的研究仍处于起步阶段，通信与AI领域对分享该领域最新进展的专门平台的需求日益增长。为此，本文提出EGI的概念，探讨其范畴与核心原则，并对这一新兴领域的最新研究成果进行全面综述。具体而言，本文介绍并讨论了：1）边缘计算与图学习的基础知识；2）围绕图智能与边缘网络之间闭环的新兴技术；3）未来EGI面临的开放挑战与研究机遇。通过弥合通信、网络与图学习领域之间的鸿沟，我们相信本综述能够引起更多关注，促进有意义的讨论，并激发EGI领域的进一步研究思路。