The exponential increase in Internet of Things (IoT) devices coupled with 6G pushing towards higher data rates and connected devices has sparked a surge in data. Consequently, harnessing the full potential of data-driven machine learning has become one of the important thrusts. In addition to the advancement in wireless technology, it is important to efficiently use the resources available and meet the users' requirements. Graph Neural Networks (GNNs) have emerged as a promising paradigm for effectively modeling and extracting insights which inherently exhibit complex network structures due to its high performance and accuracy, scalability, adaptability, and resource efficiency. There is a lack of a comprehensive survey that focuses on the applications and advances GNN has made in the context of IoT and Next Generation (NextG) networks. To bridge that gap, this survey starts by providing a detailed description of GNN's terminologies, architecture, and the different types of GNNs. Then we provide a comprehensive survey of the advancements in applying GNNs for IoT from the perspective of data fusion and intrusion detection. Thereafter, we survey the impact GNN has made in improving spectrum awareness. Next, we provide a detailed account of how GNN has been leveraged for networking and tactical systems. Through this survey, we aim to provide a comprehensive resource for researchers to learn more about GNN in the context of wireless networks, and understand its state-of-the-art use cases while contrasting to other machine learning approaches. Finally, we also discussed the challenges and wide range of future research directions to further motivate the use of GNN for IoT and NextG Networks.

翻译：物联网（IoT）设备的指数级增长，加之6G技术推动着更高数据速率和更多连接设备的发展，引发了数据量的激增。因此，充分利用数据驱动机器学习的潜力已成为重要的研究方向之一。除了无线技术的进步，高效利用可用资源并满足用户需求也至关重要。图神经网络（GNNs）因其高性能、高准确性、可扩展性、适应性和资源效率，已成为一种有前景的范式，能有效建模和提取那些本身具有复杂网络结构的数据中的洞见。目前缺乏一个全面综述，专注于GNN在物联网和下一代（NextG）网络背景下的应用与进展。为弥补这一空白，本综述首先详细阐述了GNN的术语、架构以及不同类型的GNN。然后，我们从数据融合和入侵检测的角度，全面综述了GNN在物联网应用中的进展。此后，我们综述了GNN在提升频谱感知方面的影响。接着，我们详细阐述了GNN在网络和战术系统中的利用方式。通过本综述，我们旨在为研究人员提供一个全面的资源，以了解更多关于无线网络背景下的GNN知识，理解其最先进的用例，并与其他机器学习方法进行对比。最后，我们还讨论了挑战和广泛的未来研究方向，以进一步推动GNN在物联网和下一代网络中的应用。