Graph-based Deep Learning for Communication Networks: A Survey
It provides a comprehensive overview for researchers and practitioners in communication networks, but it is incremental as it synthesizes existing work rather than presenting new methods.
This survey reviews the application of graph-based deep learning models, such as graph convolutional and attention networks, to tackle various problems in communication networks like wireless, wired, and software-defined networks, highlighting their state-of-the-art performance and organizing existing research with a public GitHub repository for updates.
Communication networks are important infrastructures in contemporary society. There are still many challenges that are not fully solved and new solutions are proposed continuously in this active research area. In recent years, to model the network topology, graph-based deep learning has achieved the state-of-the-art performance in a series of problems in communication networks. In this survey, we review the rapidly growing body of research using different graph-based deep learning models, e.g. graph convolutional and graph attention networks, in various problems from different types of communication networks, e.g. wireless networks, wired networks, and software defined networks. We also present a well-organized list of the problem and solution for each study and identify future research directions. To the best of our knowledge, this paper is the first survey that focuses on the application of graph-based deep learning methods in communication networks involving both wired and wireless scenarios. To track the follow-up research, a public GitHub repository is created, where the relevant papers will be updated continuously.