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A unifying framework for fast randomization of ecological networks with fixed (node) degrees

The Curveball algorithm is an efficient and unbiased procedure for randomizing bipartite networks (or their matrix counterpart) while preserving node degrees. Here we introduce two extensions of the procedure, making it capable to randomize also unimode directed and undirected networks. We provide f...

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Detalles Bibliográficos
Autores principales: Carstens, Corrie Jacobien, Berger, Annabell, Strona, Giovanni
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6072652/
https://www.ncbi.nlm.nih.gov/pubmed/30094204
http://dx.doi.org/10.1016/j.mex.2018.06.018
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author Carstens, Corrie Jacobien
Berger, Annabell
Strona, Giovanni
author_facet Carstens, Corrie Jacobien
Berger, Annabell
Strona, Giovanni
author_sort Carstens, Corrie Jacobien
collection PubMed
description The Curveball algorithm is an efficient and unbiased procedure for randomizing bipartite networks (or their matrix counterpart) while preserving node degrees. Here we introduce two extensions of the procedure, making it capable to randomize also unimode directed and undirected networks. We provide formal mathematical proofs that the two extensions, as the original Curveball, are fast and unbiased (i.e. they sample uniformly from the universe of possible network configurations). • We extend the Curveball algorithm to unimode directed and undirected networks. • As the original Curveball, extensions are fast and unbiased. • We provide Python and R code implementing the new procedures.
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spelling pubmed-60726522018-08-09 A unifying framework for fast randomization of ecological networks with fixed (node) degrees Carstens, Corrie Jacobien Berger, Annabell Strona, Giovanni MethodsX Agricultural and Biological Science The Curveball algorithm is an efficient and unbiased procedure for randomizing bipartite networks (or their matrix counterpart) while preserving node degrees. Here we introduce two extensions of the procedure, making it capable to randomize also unimode directed and undirected networks. We provide formal mathematical proofs that the two extensions, as the original Curveball, are fast and unbiased (i.e. they sample uniformly from the universe of possible network configurations). • We extend the Curveball algorithm to unimode directed and undirected networks. • As the original Curveball, extensions are fast and unbiased. • We provide Python and R code implementing the new procedures. Elsevier 2018-07-05 /pmc/articles/PMC6072652/ /pubmed/30094204 http://dx.doi.org/10.1016/j.mex.2018.06.018 Text en © 2018 The Authors http://creativecommons.org/licenses/by/4.0/ This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Agricultural and Biological Science
Carstens, Corrie Jacobien
Berger, Annabell
Strona, Giovanni
A unifying framework for fast randomization of ecological networks with fixed (node) degrees
title A unifying framework for fast randomization of ecological networks with fixed (node) degrees
title_full A unifying framework for fast randomization of ecological networks with fixed (node) degrees
title_fullStr A unifying framework for fast randomization of ecological networks with fixed (node) degrees
title_full_unstemmed A unifying framework for fast randomization of ecological networks with fixed (node) degrees
title_short A unifying framework for fast randomization of ecological networks with fixed (node) degrees
title_sort unifying framework for fast randomization of ecological networks with fixed (node) degrees
topic Agricultural and Biological Science
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6072652/
https://www.ncbi.nlm.nih.gov/pubmed/30094204
http://dx.doi.org/10.1016/j.mex.2018.06.018
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