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Functionability in complex networks: Leading nodes for the transition from structural to functional networks through remote asynchronization
Author(s) -
Gemma Rosell-Tarragó,
Albert Dı́az-Guilera
Publication year - 2020
Publication title -
chaos an interdisciplinary journal of nonlinear science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.971
H-Index - 113
eISSN - 1089-7682
pISSN - 1054-1500
DOI - 10.1063/1.5099621
Subject(s) - centrality , topology (electrical circuits) , node (physics) , kuramoto model , synchronization networks , complex network , computer science , network topology , perturbation (astronomy) , synchronization (alternating current) , frustration , mathematics , physics , computer network , quantum mechanics , combinatorics , world wide web , condensed matter physics
Complex networks are essentially heterogeneous not only in the basic properties of the constituent nodes, such as their degree, but also in the effects that these have on the global dynamical properties of the network. Networks of coupled identical phase oscillators are good examples for analyzing these effects, since an overall synchronized state can be considered a reference state. A small variation of intrinsic node parameters may cause the system to move away from synchronization, and a new phase-locked stationary state can be achieved. We propose a measure of phase dispersion that quantifies the functional response of the system to a given local perturbation. As a particular implementation, we propose a variation of the standard Kuramoto model in which the nodes of a complex network interact with their neighboring nodes, by including a node-dependent frustration parameter. The final stationary phase-locked state now depends on the particular frustration parameter at each node and also on the network topology. We exploit this scenario by introducing individual frustration parameters and measuring what their effect on the whole network is, measured in terms of the phase dispersion, which depends only on the topology of the network and on the choice of the particular node that is perturbed. This enables us to define a characteristic of the node, its functionability, that can be computed analytically in terms of the network topology. Finally, we provide a thorough comparison with other centrality measures.

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