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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...

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Detalles Bibliográficos
Autores principales: Shanafelt, D. W., Salau, K. R., Baggio, J. A.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: The Royal Society Publishing 2017
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.
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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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