Universality in network dynamics

By Baruch Barzel, Albert-László Barabási. Nature Physics, 2013

371 citations recorded by the source corpus.

Despite significant advances in characterizing the structural properties of complex networks, a mathematical framework that uncovers the universal properties of the interplay between the topology and the dynamics of complex systems continues to elude us. Here we develop a self-consistent theory of dynamical perturbations in complex systems, allowing us to systematically separate the contribution of the network topology and dynamics. The formalism covers a broad range of steady-state dynamical processes and offers testable predictions regarding the system’s response to perturbations and the development of correlations. It predicts several distinct universality classes whose characteristics can be derived directly from the continuum equation governing the system’s dynamics and which are validated on several canonical network-based dynamical systems, from biochemical dynamics to epidemic spreading. Finally, we collect experimental data pertaining to social and biological systems, demonstrating that we can accurately uncover their universality class even in the absence of an appropriate continuum theory that governs the system’s dynamics. Models for the topology or dynamics of various networks abound, but until now, there has been no single universal framework for complex networks that can separate factors contributing to the topology and dynamics of networks across biological and social systems.

Subfields: Experimental and Cognitive Psychology, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/232edced63eb01545cdad3aae37021ad8e9f949a

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In Experimental and Cognitive Psychology

In Statistical and Nonlinear Physics

Citations in the corpus

Universal resilience patterns in complex networks (2016, background): “Here resilience increases with βeff, as the larger is βeff, the deeper is the system into the active state, and farther from the critical transition at βeff c (equation (12)).”
Detecting subnetwork-level dynamic correlations (2017, background): “However, the expression levels of the biological units are the outcome of complex biological regulatory networks, the links of which may be turned on and off in response to certain biological conditions (Barzel and Barabasi, 2013; Ideker and Krogan, 2012; Luscombe et al., 2004; Ocone et al., 2013).”
Network resilience (2020, background): “In a multi-dimensional system, the dynamics of each component is not only depend on the self-dynamics, but also related to the interactions between the components and its interacting partners [242, 52].”