Multilayer Network Science: from Cells to Societies

Abstract

Networks are convenient mathematical models to represent the structure of complex systems, from cells to societies. In the past decade, multilayer network science -- the branch of the field dealing with units interacting in multiple distinct ways, simultaneously -- was demonstrated to be an effective modeling and analytical framework for a wide spectrum of empirical systems, from biopolymer networks (such as interactome and metabolomes) to neuronal networks (such as connectomes), from social networks to urban and transportation networks. In this Element, a decade after the publication of one of the most seminal papers on this topic, we review the most salient features of multilayer network science, covering both theoretical aspects and direct applications to real-world coupled/interdependent systems, from the point of view of multilayer structure, dynamics, and function. We discuss potential frontiers for this topic and the corresponding challenges in the field for the future.

arXiv commentPublished in Cambridge Elements. Cambridge University Press; 2022

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• Robustness and resilience of complex networks
• From the origin of life to pandemics: Emergent phenomena in complex systems
• Descriptive vs. inferential community detection in networks: pitfalls, myths, and half-truths
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• Information propagation in multilayer systems with higher-order interactions across timescales
• Introduction to correlation networks: Interdisciplinary approaches beyond thresholding
• Gillespie algorithms for stochastic multiagent dynamics in populations and network
• Node-layer duality in networked systems
• Identifying Central Nodes in Multiplex Networks by Embracing Layer-Specific Heterogeneity via DomiRank
• Multiplexity amplifies geometry in networks