Multilayer network science: theory, methods, and applications
Abstract
Multilayer network science has emerged as a central framework for analysing interconnected and interdependent complex systems. Its relevance has grown substantially with the increasing availability of rich, heterogeneous data, which makes it possible to uncover and exploit the inherently multilayered organisation of many real-world networks. In this review, we summarise recent developments in the field. On the theoretical and methodological front, we outline core concepts and survey advances in community detection, dynamical processes, temporal networks, higher-order interactions, and machine-learning-based approaches. On the application side, we discuss progress across diverse domains, including interdependent infrastructures, spreading dynamics, computational social science, economic and financial systems, ecological and climate networks, science-of-science studies, network medicine, and network neuroscience. We conclude with a forward-looking perspective, emphasizing the need for standardised datasets and software, deeper integration of temporal and higher-order structures, and a transition toward genuinely predictive models of complex systems.
Cite
@article{arxiv.2511.23371,
title = {Multilayer network science: theory, methods, and applications},
author = {Alberto Aleta and Andreia Sofia Teixeira and Guilherme Ferraz de Arruda and Andrea Baronchelli and Alain Barrat and János Kertész and Albert Díaz-Guilera and Oriol Artime and Michele Starnini and Giovanni Petri and Márton Karsai and Siddharth Patwardhan and Kathryn Coronges and Ann McCranie and Alessandro Vespignani and Yamir Moreno and Santo Fortunato},
journal= {arXiv preprint arXiv:2511.23371},
year = {2026}
}