Graph Neural Networks in Computer Networks: A Survey

Abstract

Traditional methods of analyzing and processing computer networks have difficulty dealing with their rapidly increasing scalability, structure, and traffic complexity due to the rapid development of information technology. Graph neural networks (GNNs), an emerging deep learning technology, adapt to graph-structured data and relationships. They offer new mechanisms and solutions to real-world problems. This paper provides a structured, unified synthesis of current research in this interdisciplinary field. It reviews the basic concepts and models of GNNs, highlights their current state in major computer network management and optimization scenarios, and discusses typical applications, challenges, open issues, and future research directions.