Graph Neural Networks for Routing Optimization: Challenges and Opportunities

Weiwei Jiang, Haoyu Han, Yang Zhang, Ji’an Wang, Miao He, Weixi Gu, Jianbin Mu () and Xirong Cheng ()
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Weiwei Jiang: School of Information and Communication Engineering, Beijing University of Posts and Telecommunications, Beijing 100876, China
Haoyu Han: School of Information and Communication Engineering, Beijing University of Posts and Telecommunications, Beijing 100876, China
Yang Zhang: School of Information and Communication Engineering, Beijing University of Posts and Telecommunications, Beijing 100876, China
Ji’an Wang: International School, Beijing University of Posts and Telecommunications, Beijing 100876, China
Miao He: Yanqi Lake Beijing Institute of Mathematical Sciences and Applications, Beijing 101408, China
Weixi Gu: China Academy of Industrial Internet, Beijing 100102, China
Jianbin Mu: College of Information Engineering, Zhejiang University of Technology, Hangzhou 310023, China
Xirong Cheng: School of Economics, Beijing Technology and Business University, Beijing 100048, China

Sustainability, 2024, vol. 16, issue 21, 1-34

Abstract: In this paper, we explore the emerging role of graph neural networks (GNNs) in optimizing routing for next-generation communication networks. Traditional routing protocols, such as OSPF or the Dijkstra algorithm, often fall short in handling the complexity, scalability, and dynamic nature of modern network environments, including unmanned aerial vehicle (UAV), satellite, and 5G networks. By leveraging their ability to model network topologies and learn from complex interdependencies between nodes and links, GNNs offer a promising solution for distributed and scalable routing optimization. This paper provides a comprehensive review of the latest research on GNN-based routing methods, categorizing them into supervised learning for network modeling, supervised learning for routing optimization, and reinforcement learning for dynamic routing tasks. We also present a detailed analysis of existing datasets, tools, and benchmarking practices. Key challenges related to scalability, real-world deployment, explainability, and security are discussed, alongside future research directions that involve federated learning, self-supervised learning, and online learning techniques to further enhance GNN applicability. This study serves as the first comprehensive survey of GNNs for routing optimization, aiming to inspire further research and practical applications in future communication networks.

Keywords: graph neural networks; routing optimization; distributed learning; supervised learning; reinforcement learning; dynamic networks; network topology; future networks (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2024
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