Year: 2022
Type: article
Abstract: Graph neural networks (GNN) have shown outstanding applications in fields where data is essentially represented as graphs (e.g., chemistry, biology, and recommendation systems). In this vein, communication networks comprise many fundamental components that are naturally represented in a graph-structured manner (e.g., topology, routing, and signal interference). This position article presents GNNs as a fundamental tool for modeling, control, and management of communication networks. GNNs represent a new generation of data-driven models that can accurately learn and reproduce the complex behaviors behind real-world networks. As a result, these models can be applied to a wide variety of networking use cases, such as planning, online optimization, or troubleshooting. The main advantage of GNNs over traditional neural networks lies in their unprecedented generalization capabilities when applied to other networks and configurations unseen during training. This is a critical feature for achieving practical data-driven solutions for networking. This article starts with a brief tutorial on GNNs and some potential applications to communication networks. Then it presents two state-of-the-art GNN models respectively applied to wired and wireless networks. Lastly, it delves into the key open challenges and opportunities yet to be explored in this novel research area.
Source: IEEE Network
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
Funders: Università degli Studi di Siena, University of Exeter, Chinese Academy of Sciences, Generalitat de Catalunya, Institució Catalana de Recerca i Estudis Avançats, Tsinghua University, Minzu University of China, Institute of Computing Technology, Chinese Academy of Sciences, Agencia Estatal de Investigación, European Social Fund
OpenAlex ID: W4289821985