A combinatorial approach to graphlet counting

Abstract Motivation: Small-induced subgraphs called graphlets are emerging as a possible tool for exploration of global and local structure of networks and for analysis of roles of individual nodes. One of the obstacles to their wider use is the computational complexity of algorithms for their disco...

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Published in:Bioinformatics
Main Authors: Hočevar, Tomaž, Demšar, Janez
Format: Article in Journal/Newspaper
Language:English
Published: Oxford University Press (OUP) 2014
Subjects:
Online Access:http://dx.doi.org/10.1093/bioinformatics/btt717
https://academic.oup.com/bioinformatics/article-pdf/30/4/559/48917199/bioinformatics_30_4_559.pdf
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spelling croxfordunivpr:10.1093/bioinformatics/btt717 2024-06-23T07:55:58+00:00 A combinatorial approach to graphlet counting Hočevar, Tomaž Demšar, Janez 2014 http://dx.doi.org/10.1093/bioinformatics/btt717 https://academic.oup.com/bioinformatics/article-pdf/30/4/559/48917199/bioinformatics_30_4_559.pdf en eng Oxford University Press (OUP) Bioinformatics volume 30, issue 4, page 559-565 ISSN 1367-4811 1367-4803 journal-article 2014 croxfordunivpr https://doi.org/10.1093/bioinformatics/btt717 2024-06-11T04:18:25Z Abstract Motivation: Small-induced subgraphs called graphlets are emerging as a possible tool for exploration of global and local structure of networks and for analysis of roles of individual nodes. One of the obstacles to their wider use is the computational complexity of algorithms for their discovery and counting. Results: We propose a new combinatorial method for counting graphlets and orbit signatures of network nodes. The algorithm builds a system of equations that connect counts of orbits from graphlets with up to five nodes, which allows to compute all orbit counts by enumerating just a single one. This reduces its practical time complexity in sparse graphs by an order of magnitude as compared with the existing pure enumeration-based algorithms. Availability and implementation: Source code is available freely at http://www.biolab.si/supp/orca/orca.html. Contact: tomaz.hocevar@fri.uni-lj.si Supplementary information: Supplementary data are available at Bioinformatics online. Article in Journal/Newspaper Orca Oxford University Press Bioinformatics 30 4 559 565
institution Open Polar
collection Oxford University Press
op_collection_id croxfordunivpr
language English
description Abstract Motivation: Small-induced subgraphs called graphlets are emerging as a possible tool for exploration of global and local structure of networks and for analysis of roles of individual nodes. One of the obstacles to their wider use is the computational complexity of algorithms for their discovery and counting. Results: We propose a new combinatorial method for counting graphlets and orbit signatures of network nodes. The algorithm builds a system of equations that connect counts of orbits from graphlets with up to five nodes, which allows to compute all orbit counts by enumerating just a single one. This reduces its practical time complexity in sparse graphs by an order of magnitude as compared with the existing pure enumeration-based algorithms. Availability and implementation: Source code is available freely at http://www.biolab.si/supp/orca/orca.html. Contact: tomaz.hocevar@fri.uni-lj.si Supplementary information: Supplementary data are available at Bioinformatics online.
format Article in Journal/Newspaper
author Hočevar, Tomaž
Demšar, Janez
spellingShingle Hočevar, Tomaž
Demšar, Janez
A combinatorial approach to graphlet counting
author_facet Hočevar, Tomaž
Demšar, Janez
author_sort Hočevar, Tomaž
title A combinatorial approach to graphlet counting
title_short A combinatorial approach to graphlet counting
title_full A combinatorial approach to graphlet counting
title_fullStr A combinatorial approach to graphlet counting
title_full_unstemmed A combinatorial approach to graphlet counting
title_sort combinatorial approach to graphlet counting
publisher Oxford University Press (OUP)
publishDate 2014
url http://dx.doi.org/10.1093/bioinformatics/btt717
https://academic.oup.com/bioinformatics/article-pdf/30/4/559/48917199/bioinformatics_30_4_559.pdf
genre Orca
genre_facet Orca
op_source Bioinformatics
volume 30, issue 4, page 559-565
ISSN 1367-4811 1367-4803
op_doi https://doi.org/10.1093/bioinformatics/btt717
container_title Bioinformatics
container_volume 30
container_issue 4
container_start_page 559
op_container_end_page 565
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