Graph-based Deep Learning for Communication Networks: A Survey

TL;DR

This survey reviews the application of graph-based deep learning models like GCNs and GATs in various communication network types, highlighting recent advances, challenges, and future research directions.

Contribution

It is the first comprehensive survey focusing on graph-based deep learning methods applied to both wired and wireless communication networks.

Findings

Graph-based deep learning models are effective in modeling communication networks.

Various problem types in communication networks benefit from graph neural networks.

Future research directions include addressing current challenges and expanding applications.

Abstract

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.