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Variational principle for scale-free network motifs

For scale-free networks with degrees following a power law with an exponent τ ∈ (2, 3), the structures of motifs (small subgraphs) are not yet well understood. We introduce a method designed to identify the dominant structure of any given motif as the solution of an optimization problem. The unique...

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Autores principales: Stegehuis, Clara, Hofstad, Remco van der, van Leeuwaarden, Johan S. H.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6494877/
https://www.ncbi.nlm.nih.gov/pubmed/31043621
http://dx.doi.org/10.1038/s41598-019-43050-8
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author Stegehuis, Clara
Hofstad, Remco van der
van Leeuwaarden, Johan S. H.
author_facet Stegehuis, Clara
Hofstad, Remco van der
van Leeuwaarden, Johan S. H.
author_sort Stegehuis, Clara
collection PubMed
description For scale-free networks with degrees following a power law with an exponent τ ∈ (2, 3), the structures of motifs (small subgraphs) are not yet well understood. We introduce a method designed to identify the dominant structure of any given motif as the solution of an optimization problem. The unique optimizer describes the degrees of the vertices that together span the most likely motif, resulting in explicit asymptotic formulas for the motif count and its fluctuations. We then classify all motifs into two categories: motifs with small and large fluctuations.
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spelling pubmed-64948772019-05-17 Variational principle for scale-free network motifs Stegehuis, Clara Hofstad, Remco van der van Leeuwaarden, Johan S. H. Sci Rep Article For scale-free networks with degrees following a power law with an exponent τ ∈ (2, 3), the structures of motifs (small subgraphs) are not yet well understood. We introduce a method designed to identify the dominant structure of any given motif as the solution of an optimization problem. The unique optimizer describes the degrees of the vertices that together span the most likely motif, resulting in explicit asymptotic formulas for the motif count and its fluctuations. We then classify all motifs into two categories: motifs with small and large fluctuations. Nature Publishing Group UK 2019-05-01 /pmc/articles/PMC6494877/ /pubmed/31043621 http://dx.doi.org/10.1038/s41598-019-43050-8 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
Stegehuis, Clara
Hofstad, Remco van der
van Leeuwaarden, Johan S. H.
Variational principle for scale-free network motifs
title Variational principle for scale-free network motifs
title_full Variational principle for scale-free network motifs
title_fullStr Variational principle for scale-free network motifs
title_full_unstemmed Variational principle for scale-free network motifs
title_short Variational principle for scale-free network motifs
title_sort variational principle for scale-free network motifs
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6494877/
https://www.ncbi.nlm.nih.gov/pubmed/31043621
http://dx.doi.org/10.1038/s41598-019-43050-8
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