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Robustness of Network Controllability with Respect to Node Removals Based on In-Degree and Out-Degree

Network controllability and its robustness have been widely studied. However, analytical methods to calculate network controllability with respect to node in- and out-degree targeted removals are currently lacking. This paper develops methods, based on generating functions for the in- and out-degree...

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Detalles Bibliográficos
Autores principales: Wang, Fenghua, Kooij, Robert E.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10137408/
https://www.ncbi.nlm.nih.gov/pubmed/37190444
http://dx.doi.org/10.3390/e25040656
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author Wang, Fenghua
Kooij, Robert E.
author_facet Wang, Fenghua
Kooij, Robert E.
author_sort Wang, Fenghua
collection PubMed
description Network controllability and its robustness have been widely studied. However, analytical methods to calculate network controllability with respect to node in- and out-degree targeted removals are currently lacking. This paper develops methods, based on generating functions for the in- and out-degree distributions, to approximate the minimum number of driver nodes needed to control directed networks, during node in- and out-degree targeted removals. By validating the proposed methods on synthetic and real-world networks, we show that our methods work reasonably well. Moreover, when the fraction of the removed nodes is below 10% the analytical results of random removals can also be used to predict the results of targeted node removals.
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spelling pubmed-101374082023-04-28 Robustness of Network Controllability with Respect to Node Removals Based on In-Degree and Out-Degree Wang, Fenghua Kooij, Robert E. Entropy (Basel) Article Network controllability and its robustness have been widely studied. However, analytical methods to calculate network controllability with respect to node in- and out-degree targeted removals are currently lacking. This paper develops methods, based on generating functions for the in- and out-degree distributions, to approximate the minimum number of driver nodes needed to control directed networks, during node in- and out-degree targeted removals. By validating the proposed methods on synthetic and real-world networks, we show that our methods work reasonably well. Moreover, when the fraction of the removed nodes is below 10% the analytical results of random removals can also be used to predict the results of targeted node removals. MDPI 2023-04-14 /pmc/articles/PMC10137408/ /pubmed/37190444 http://dx.doi.org/10.3390/e25040656 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Wang, Fenghua
Kooij, Robert E.
Robustness of Network Controllability with Respect to Node Removals Based on In-Degree and Out-Degree
title Robustness of Network Controllability with Respect to Node Removals Based on In-Degree and Out-Degree
title_full Robustness of Network Controllability with Respect to Node Removals Based on In-Degree and Out-Degree
title_fullStr Robustness of Network Controllability with Respect to Node Removals Based on In-Degree and Out-Degree
title_full_unstemmed Robustness of Network Controllability with Respect to Node Removals Based on In-Degree and Out-Degree
title_short Robustness of Network Controllability with Respect to Node Removals Based on In-Degree and Out-Degree
title_sort robustness of network controllability with respect to node removals based on in-degree and out-degree
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10137408/
https://www.ncbi.nlm.nih.gov/pubmed/37190444
http://dx.doi.org/10.3390/e25040656
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