Multilayer network science: theory, methods, and applications

TL;DR

This review summarizes recent advances in multilayer network science, covering theoretical concepts, methods, and diverse applications across multiple scientific domains.

Contribution

It provides a comprehensive overview of recent developments, highlighting core concepts, methodological progress, and application areas in multilayer network analysis.

Findings

Advances in community detection and dynamical processes in multilayer networks.

Progress in modeling temporal networks and higher-order interactions.

Application of multilayer networks in social, ecological, and medical systems.

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.