标题： 图神经网络遇见无线通信：动机、应用和未来方向 显示英文标题

标题： Graph Neural Networks Meet Wireless Communications: Motivation, Applications, and Future Directions

摘要： 作为高效的图分析工具，图神经网络（GNNs）具有特殊的属性，特别适合无线通信的特性和要求，显示出在推进下一代无线通信方面的良好潜力。 本文旨在全面概述GNNs与无线通信之间的相互作用，包括用于无线通信的GNNs（GNN4Com）和为GNNs服务的无线通信（Com4GNN）。 特别是，我们讨论基于图形模型构建的GNN4Com，并介绍相应的激励机制下的Com4GNN。 我们还强调了潜在的研究方向，以促进GNNs在无线通信领域的未来研究工作。 显示英文摘要

摘要： As an efficient graph analytical tool, graph neural networks (GNNs) have special properties that are particularly fit for the characteristics and requirements of wireless communications, exhibiting good potential for the advancement of next-generation wireless communications. This article aims to provide a comprehensive overview of the interplay between GNNs and wireless communications, including GNNs for wireless communications (GNN4Com) and wireless communications for GNNs (Com4GNN). In particular, we discuss GNN4Com based on how graphical models are constructed and introduce Com4GNN with corresponding incentives. We also highlight potential research directions to promote future research endeavors for GNNs in wireless communications.