Multilayer network science: theory, methods, and applications
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arXiv
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| Main Authors: | , , , , , , , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866911639182245888 |
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| author | Aleta, Alberto Teixeira, Andreia Sofia de Arruda, Guilherme Ferraz Baronchelli, Andrea Barrat, Alain Kertész, János Díaz-Guilera, Albert Artime, Oriol Starnini, Michele Petri, Giovanni Karsai, Márton Patwardhan, Siddharth Coronges, Kathryn McCranie, Ann Vespignani, Alessandro Moreno, Yamir Fortunato, Santo |
| author_facet | Aleta, Alberto Teixeira, Andreia Sofia de Arruda, Guilherme Ferraz Baronchelli, Andrea Barrat, Alain Kertész, János Díaz-Guilera, Albert Artime, Oriol Starnini, Michele Petri, Giovanni Karsai, Márton Patwardhan, Siddharth Coronges, Kathryn McCranie, Ann Vespignani, Alessandro Moreno, Yamir Fortunato, Santo |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_23371 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Multilayer network science: theory, methods, and applications Aleta, Alberto Teixeira, Andreia Sofia de Arruda, Guilherme Ferraz Baronchelli, Andrea Barrat, Alain Kertész, János Díaz-Guilera, Albert Artime, Oriol Starnini, Michele Petri, Giovanni Karsai, Márton Patwardhan, Siddharth Coronges, Kathryn McCranie, Ann Vespignani, Alessandro Moreno, Yamir Fortunato, Santo Physics and Society 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. |
| title | Multilayer network science: theory, methods, and applications |
| topic | Physics and Society |
| url | https://arxiv.org/abs/2511.23371 |