Graph-based Deep Learning for Communication Networks: A Survey

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.

Accepted by Elsevier Computer Communications. Github link: https://github.com/jwwthu/GNN-Communication-Networks

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Cited by in corpus (9)

• Graph Neural Network for Traffic Forecasting: A Survey
• Big Data for Traffic Estimation and Prediction: A Survey of Data and Tools
• Multidimensional Graph Neural Networks for Wireless Communications
• GNN-Geo: A Graph Neural Network-based Fine-grained IP geolocation Framework
• Graph-based Solutions with Residuals for Intrusion Detection: the Modified E-GraphSAGE and E-ResGAT Algorithms
• Network Calculus with Flow Prolongation -- A Feedforward FIFO Analysis enabled by ML
• Defending Network Intrusion Detection Systems Based on Graph Neural Networks Against Structural Adversarial Attacks
• From Simulation to Deep Learning: Survey on Network Performance Modeling Approaches
• RELiQ: Scalable Entanglement Routing via Reinforcement Learning in Quantum Networks