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Do-it-yourself networks: a novel method of generating weighted networks
Network theory is finding applications in the life and social sciences for ecology, epidemiology, finance and social–ecological systems. While there are methods to generate specific types of networks, the broad literature is focused on generating unweighted networks. In this paper, we present a fram...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
The Royal Society Publishing
2017
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5717683/ https://www.ncbi.nlm.nih.gov/pubmed/29291108 http://dx.doi.org/10.1098/rsos.171227 |
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author | Shanafelt, D. W. Salau, K. R. Baggio, J. A. |
author_facet | Shanafelt, D. W. Salau, K. R. Baggio, J. A. |
author_sort | Shanafelt, D. W. |
collection | PubMed |
description | Network theory is finding applications in the life and social sciences for ecology, epidemiology, finance and social–ecological systems. While there are methods to generate specific types of networks, the broad literature is focused on generating unweighted networks. In this paper, we present a framework for generating weighted networks that satisfy user-defined criteria. Each criterion hierarchically defines a feature of the network and, in doing so, complements existing algorithms in the literature. We use a general example of ecological species dispersal to illustrate the method and provide open-source code for academic purposes. |
format | Online Article Text |
id | pubmed-5717683 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | The Royal Society Publishing |
record_format | MEDLINE/PubMed |
spelling | pubmed-57176832017-12-29 Do-it-yourself networks: a novel method of generating weighted networks Shanafelt, D. W. Salau, K. R. Baggio, J. A. R Soc Open Sci Mathematics Network theory is finding applications in the life and social sciences for ecology, epidemiology, finance and social–ecological systems. While there are methods to generate specific types of networks, the broad literature is focused on generating unweighted networks. In this paper, we present a framework for generating weighted networks that satisfy user-defined criteria. Each criterion hierarchically defines a feature of the network and, in doing so, complements existing algorithms in the literature. We use a general example of ecological species dispersal to illustrate the method and provide open-source code for academic purposes. The Royal Society Publishing 2017-11-22 /pmc/articles/PMC5717683/ /pubmed/29291108 http://dx.doi.org/10.1098/rsos.171227 Text en © 2017 The Authors. http://creativecommons.org/licenses/by/4.0/ Published by the Royal Society under the terms of the Creative Commons Attribution License http://creativecommons.org/licenses/by/4.0/, which permits unrestricted use, provided the original author and source are credited. |
spellingShingle | Mathematics Shanafelt, D. W. Salau, K. R. Baggio, J. A. Do-it-yourself networks: a novel method of generating weighted networks |
title | Do-it-yourself networks: a novel method of generating weighted networks |
title_full | Do-it-yourself networks: a novel method of generating weighted networks |
title_fullStr | Do-it-yourself networks: a novel method of generating weighted networks |
title_full_unstemmed | Do-it-yourself networks: a novel method of generating weighted networks |
title_short | Do-it-yourself networks: a novel method of generating weighted networks |
title_sort | do-it-yourself networks: a novel method of generating weighted networks |
topic | Mathematics |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5717683/ https://www.ncbi.nlm.nih.gov/pubmed/29291108 http://dx.doi.org/10.1098/rsos.171227 |
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