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Network Communities of Dynamical Influence
Fuelled by a desire for greater connectivity, networked systems now pervade our society at an unprecedented level that will affect it in ways we do not yet understand. In contrast, nature has already developed efficient networks that can instigate rapid response and consensus when key elements are s...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6879613/ https://www.ncbi.nlm.nih.gov/pubmed/31772210 http://dx.doi.org/10.1038/s41598-019-53942-4 |
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author | Clark, Ruaridh Punzo, Giuliano Macdonald, Malcolm |
author_facet | Clark, Ruaridh Punzo, Giuliano Macdonald, Malcolm |
author_sort | Clark, Ruaridh |
collection | PubMed |
description | Fuelled by a desire for greater connectivity, networked systems now pervade our society at an unprecedented level that will affect it in ways we do not yet understand. In contrast, nature has already developed efficient networks that can instigate rapid response and consensus when key elements are stimulated. We present a technique for identifying these key elements by investigating the relationships between a system’s most dominant eigenvectors. This approach reveals the most effective vertices for leading a network to rapid consensus when stimulated, as well as the communities that form under their dynamical influence. In applying this technique, the effectiveness of starling flocks was found to be due, in part, to the low outdegree of every bird, where increasing the number of outgoing connections can produce a less responsive flock. A larger outdegree also affects the location of the birds with the most influence, where these influentially connected birds become more centrally located and in a poorer position to observe a predator and, hence, instigate an evasion manoeuvre. Finally, the technique was found to be effective in large voxel-wise brain connectomes where subjects can be identified from their influential communities. |
format | Online Article Text |
id | pubmed-6879613 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-68796132019-12-05 Network Communities of Dynamical Influence Clark, Ruaridh Punzo, Giuliano Macdonald, Malcolm Sci Rep Article Fuelled by a desire for greater connectivity, networked systems now pervade our society at an unprecedented level that will affect it in ways we do not yet understand. In contrast, nature has already developed efficient networks that can instigate rapid response and consensus when key elements are stimulated. We present a technique for identifying these key elements by investigating the relationships between a system’s most dominant eigenvectors. This approach reveals the most effective vertices for leading a network to rapid consensus when stimulated, as well as the communities that form under their dynamical influence. In applying this technique, the effectiveness of starling flocks was found to be due, in part, to the low outdegree of every bird, where increasing the number of outgoing connections can produce a less responsive flock. A larger outdegree also affects the location of the birds with the most influence, where these influentially connected birds become more centrally located and in a poorer position to observe a predator and, hence, instigate an evasion manoeuvre. Finally, the technique was found to be effective in large voxel-wise brain connectomes where subjects can be identified from their influential communities. Nature Publishing Group UK 2019-11-26 /pmc/articles/PMC6879613/ /pubmed/31772210 http://dx.doi.org/10.1038/s41598-019-53942-4 Text en © The Author(s) 2019 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 Clark, Ruaridh Punzo, Giuliano Macdonald, Malcolm Network Communities of Dynamical Influence |
title | Network Communities of Dynamical Influence |
title_full | Network Communities of Dynamical Influence |
title_fullStr | Network Communities of Dynamical Influence |
title_full_unstemmed | Network Communities of Dynamical Influence |
title_short | Network Communities of Dynamical Influence |
title_sort | network communities of dynamical influence |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6879613/ https://www.ncbi.nlm.nih.gov/pubmed/31772210 http://dx.doi.org/10.1038/s41598-019-53942-4 |
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