Steering complex networks toward desired dynamics

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dc.contributor.author Gutiérrez Díez, Ricardo
dc.contributor.author Materassi, Massimo
dc.contributor.author Focardi, Stefano
dc.contributor.author Boccaletti, Stefano
dc.date.accessioned 2021-03-09T10:01:26Z
dc.date.available 2021-03-09T10:01:26Z
dc.date.issued 2020-11-27
dc.identifier.bibliographicCitation Scientific reports, 10, 20744, Nov. 2020, 8 pp.
dc.identifier.issn 2045-2322
dc.identifier.uri http://hdl.handle.net/10016/32085
dc.description.abstract We consider networks of dynamical units that evolve in time according to different laws, and are coupled to each other in highly irregular ways. Studying how to steer the dynamics of such systems towards a desired evolution is of great practical interest in many areas of science, as well as providing insight into the interplay between network structure and dynamical behavior. We propose a pinning protocol for imposing specific dynamic evolutions compatible with the equations of motion on a networked system. The method does not impose any restrictions on the local dynamics, which may vary from node to node, nor on the interactions between nodes, which may adopt in principle any nonlinear mathematical form and be represented by weighted, directed or undirected links. We first explore our method on small synthetic networks of chaotic oscillators, which allows us to unveil a correlation between the ordered sequence of pinned nodes and their topological influence in the network. We then consider a 12-species trophic web network, which is a model of a mammalian food web. By pinning a relatively small number of species, one can make the system abandon its spontaneous evolution from its (typically uncontrolled) initial state towards a target dynamics, or periodically control it so as to make the populations evolve within stipulated bounds. The relevance of these findings for environment management and conservation is discussed.
dc.description.sponsorship The authors acknowledge the computing resources and technical support provided by CRESCO/ENEAGRID High Performance Computing infrastructure (funded by ENEA, the Italian National Agency for New Technologies, Energy and Sustainable Economic Development and by Italian and European research programmes) and its staff. R.G. acknowledges financial support from MINECO, Spain (FIS2017-84151-P).
dc.format.extent 8
dc.language.iso eng
dc.publisher Springer Nature
dc.rights © The Author(s) 2020.
dc.rights This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.
dc.rights Atribución 3.0 España
dc.rights.uri http://creativecommons.org/licenses/by/3.0/es/
dc.subject.other Complex networks
dc.subject.other Ecological networks
dc.subject.other Nonlinear phenomena
dc.title Steering complex networks toward desired dynamics
dc.type article
dc.subject.eciencia Matemáticas
dc.identifier.doi https://doi.org/10.1038/s41598-020-77663-1
dc.rights.accessRights openAccess
dc.relation.projectID Gobierno de España. FIS2017-84151-P
dc.type.version publishedVersion
dc.identifier.publicationfirstpage 1
dc.identifier.publicationissue 20744
dc.identifier.publicationlastpage 8
dc.identifier.publicationtitle Scientific Reports
dc.identifier.publicationvolume 10
dc.identifier.uxxi AR/0000025875
dc.contributor.funder Ministerio de Economía y Competitividad (España)
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