In recent years, with the rapid enhancement of computing power, deep learning methods have been widely applied in wireless networks and achieved impressive performance. To effectively exploit the information of graph-structured data as well as contextual information, graph neural networks (GNNs) have been introduced to address a series of optimization problems of wireless networks. In this overview, we first illustrate the construction method of wireless communication graph for various wireless networks and simply introduce the progress of several classical paradigms of GNNs. Then, several applications of GNNs in wireless networks such as resource allocation and several emerging fields, are discussed in detail. Finally, some research trends about the applications of GNNs in wireless communication systems are discussed.

翻译：近年来,随着计算机能力的迅速增强,深层次学习方法被广泛应用于无线网络,并取得了令人印象深刻的成绩;为有效利用图表结构数据的信息以及背景信息,引入了图形神经网络,以解决无线网络的一系列优化问题;在此概述中,我们首先说明各种无线网络无线通信图的构建方法,只是介绍全球无线网络若干典型模式的进展;随后,详细讨论了全球无线网络在无线网络的一些应用,如资源分配和几个新兴领域;最后,讨论了关于全球无线网络在无线通信系统中应用的一些研究趋势。