Cargando…

Universal framework for edge controllability of complex networks

Dynamical processes occurring on the edges in complex networks are relevant to a variety of real-world situations. Despite recent advances, a framework for edge controllability is still required for complex networks of arbitrary structure and interaction strength. Generalizing a previously introduce...

Descripción completa

Detalles Bibliográficos
Autores principales: Pang, Shao-Peng, Wang, Wen-Xu, Hao, Fei, Lai, Ying-Cheng
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5484715/
https://www.ncbi.nlm.nih.gov/pubmed/28652604
http://dx.doi.org/10.1038/s41598-017-04463-5
_version_ 1783245931341152256
author Pang, Shao-Peng
Wang, Wen-Xu
Hao, Fei
Lai, Ying-Cheng
author_facet Pang, Shao-Peng
Wang, Wen-Xu
Hao, Fei
Lai, Ying-Cheng
author_sort Pang, Shao-Peng
collection PubMed
description Dynamical processes occurring on the edges in complex networks are relevant to a variety of real-world situations. Despite recent advances, a framework for edge controllability is still required for complex networks of arbitrary structure and interaction strength. Generalizing a previously introduced class of processes for edge dynamics, the switchboard dynamics, and exploit- ing the exact controllability theory, we develop a universal framework in which the controllability of any node is exclusively determined by its local weighted structure. This framework enables us to identify a unique set of critical nodes for control, to derive analytic formulas and articulate efficient algorithms to determine the exact upper and lower controllability bounds, and to evaluate strongly structural controllability of any given network. Applying our framework to a large number of model and real-world networks, we find that the interaction strength plays a more significant role in edge controllability than the network structure does, due to a vast range between the bounds determined mainly by the interaction strength. Moreover, transcriptional regulatory networks and electronic circuits are much more strongly structurally controllable (SSC) than other types of real-world networks, directed networks are more SSC than undirected networks, and sparse networks are typically more SSC than dense networks.
format Online
Article
Text
id pubmed-5484715
institution National Center for Biotechnology Information
language English
publishDate 2017
publisher Nature Publishing Group UK
record_format MEDLINE/PubMed
spelling pubmed-54847152017-06-30 Universal framework for edge controllability of complex networks Pang, Shao-Peng Wang, Wen-Xu Hao, Fei Lai, Ying-Cheng Sci Rep Article Dynamical processes occurring on the edges in complex networks are relevant to a variety of real-world situations. Despite recent advances, a framework for edge controllability is still required for complex networks of arbitrary structure and interaction strength. Generalizing a previously introduced class of processes for edge dynamics, the switchboard dynamics, and exploit- ing the exact controllability theory, we develop a universal framework in which the controllability of any node is exclusively determined by its local weighted structure. This framework enables us to identify a unique set of critical nodes for control, to derive analytic formulas and articulate efficient algorithms to determine the exact upper and lower controllability bounds, and to evaluate strongly structural controllability of any given network. Applying our framework to a large number of model and real-world networks, we find that the interaction strength plays a more significant role in edge controllability than the network structure does, due to a vast range between the bounds determined mainly by the interaction strength. Moreover, transcriptional regulatory networks and electronic circuits are much more strongly structurally controllable (SSC) than other types of real-world networks, directed networks are more SSC than undirected networks, and sparse networks are typically more SSC than dense networks. Nature Publishing Group UK 2017-06-26 /pmc/articles/PMC5484715/ /pubmed/28652604 http://dx.doi.org/10.1038/s41598-017-04463-5 Text en © The Author(s) 2017 Open Access 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 license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license 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. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.
spellingShingle Article
Pang, Shao-Peng
Wang, Wen-Xu
Hao, Fei
Lai, Ying-Cheng
Universal framework for edge controllability of complex networks
title Universal framework for edge controllability of complex networks
title_full Universal framework for edge controllability of complex networks
title_fullStr Universal framework for edge controllability of complex networks
title_full_unstemmed Universal framework for edge controllability of complex networks
title_short Universal framework for edge controllability of complex networks
title_sort universal framework for edge controllability of complex networks
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5484715/
https://www.ncbi.nlm.nih.gov/pubmed/28652604
http://dx.doi.org/10.1038/s41598-017-04463-5
work_keys_str_mv AT pangshaopeng universalframeworkforedgecontrollabilityofcomplexnetworks
AT wangwenxu universalframeworkforedgecontrollabilityofcomplexnetworks
AT haofei universalframeworkforedgecontrollabilityofcomplexnetworks
AT laiyingcheng universalframeworkforedgecontrollabilityofcomplexnetworks