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Combinatorial algorithm for counting small induced graphs and orbits

Graphlet analysis is an approach to network analysis that is particularly popular in bioinformatics. We show how to set up a system of linear equations that relate the orbit counts and can be used in an algorithm that is significantly faster than the existing approaches based on direct enumeration o...

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Detalles Bibliográficos
Autores principales: Hočevar, Tomaž, Demšar, Janez
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5300269/
https://www.ncbi.nlm.nih.gov/pubmed/28182743
http://dx.doi.org/10.1371/journal.pone.0171428
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author Hočevar, Tomaž
Demšar, Janez
author_facet Hočevar, Tomaž
Demšar, Janez
author_sort Hočevar, Tomaž
collection PubMed
description Graphlet analysis is an approach to network analysis that is particularly popular in bioinformatics. We show how to set up a system of linear equations that relate the orbit counts and can be used in an algorithm that is significantly faster than the existing approaches based on direct enumeration of graphlets. The approach presented in this paper presents a generalization of the currently fastest method for counting 5-node graphlets in bioinformatics. The algorithm requires existence of a vertex with certain properties; we show that such vertex exists for graphlets of arbitrary size, except for complete graphs and a cycle with four nodes, which are treated separately. Empirical analysis of running time agrees with the theoretical results.
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spelling pubmed-53002692017-02-28 Combinatorial algorithm for counting small induced graphs and orbits Hočevar, Tomaž Demšar, Janez PLoS One Research Article Graphlet analysis is an approach to network analysis that is particularly popular in bioinformatics. We show how to set up a system of linear equations that relate the orbit counts and can be used in an algorithm that is significantly faster than the existing approaches based on direct enumeration of graphlets. The approach presented in this paper presents a generalization of the currently fastest method for counting 5-node graphlets in bioinformatics. The algorithm requires existence of a vertex with certain properties; we show that such vertex exists for graphlets of arbitrary size, except for complete graphs and a cycle with four nodes, which are treated separately. Empirical analysis of running time agrees with the theoretical results. Public Library of Science 2017-02-09 /pmc/articles/PMC5300269/ /pubmed/28182743 http://dx.doi.org/10.1371/journal.pone.0171428 Text en © 2017 Hočevar, Demšar http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Hočevar, Tomaž
Demšar, Janez
Combinatorial algorithm for counting small induced graphs and orbits
title Combinatorial algorithm for counting small induced graphs and orbits
title_full Combinatorial algorithm for counting small induced graphs and orbits
title_fullStr Combinatorial algorithm for counting small induced graphs and orbits
title_full_unstemmed Combinatorial algorithm for counting small induced graphs and orbits
title_short Combinatorial algorithm for counting small induced graphs and orbits
title_sort combinatorial algorithm for counting small induced graphs and orbits
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5300269/
https://www.ncbi.nlm.nih.gov/pubmed/28182743
http://dx.doi.org/10.1371/journal.pone.0171428
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