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How modular structure can simplify tasks on networks: parameterizing graph optimization by fast local community detection

By considering the task of finding the shortest walk through a Network, we find an algorithm for which the run time is not as O(2(n)), with n being the number of nodes, but instead scales with the number of nodes in a coarsened network. This coarsened network has a number of nodes related to the num...

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Detalles Bibliográficos
Autores principales: Bui-Xuan, Binh-Minh, Jones, Nick S.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: The Royal Society Publishing 2014
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4156142/
https://www.ncbi.nlm.nih.gov/pubmed/25294962
http://dx.doi.org/10.1098/rspa.2014.0224
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author Bui-Xuan, Binh-Minh
Jones, Nick S.
author_facet Bui-Xuan, Binh-Minh
Jones, Nick S.
author_sort Bui-Xuan, Binh-Minh
collection PubMed
description By considering the task of finding the shortest walk through a Network, we find an algorithm for which the run time is not as O(2(n)), with n being the number of nodes, but instead scales with the number of nodes in a coarsened network. This coarsened network has a number of nodes related to the number of dense regions in the original graph. Since we exploit a form of local community detection as a preprocessing, this work gives support to the project of developing heuristic algorithms for detecting dense regions in networks: preprocessing of this kind can accelerate optimization tasks on networks. Our work also suggests a class of empirical conjectures for how structural features of efficient networked systems might scale with system size.
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spelling pubmed-41561422014-10-08 How modular structure can simplify tasks on networks: parameterizing graph optimization by fast local community detection Bui-Xuan, Binh-Minh Jones, Nick S. Proc Math Phys Eng Sci Research Articles By considering the task of finding the shortest walk through a Network, we find an algorithm for which the run time is not as O(2(n)), with n being the number of nodes, but instead scales with the number of nodes in a coarsened network. This coarsened network has a number of nodes related to the number of dense regions in the original graph. Since we exploit a form of local community detection as a preprocessing, this work gives support to the project of developing heuristic algorithms for detecting dense regions in networks: preprocessing of this kind can accelerate optimization tasks on networks. Our work also suggests a class of empirical conjectures for how structural features of efficient networked systems might scale with system size. The Royal Society Publishing 2014-10-08 /pmc/articles/PMC4156142/ /pubmed/25294962 http://dx.doi.org/10.1098/rspa.2014.0224 Text en http://creativecommons.org/licenses/by/4.0/ © 2014 The Authors. 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 Research Articles
Bui-Xuan, Binh-Minh
Jones, Nick S.
How modular structure can simplify tasks on networks: parameterizing graph optimization by fast local community detection
title How modular structure can simplify tasks on networks: parameterizing graph optimization by fast local community detection
title_full How modular structure can simplify tasks on networks: parameterizing graph optimization by fast local community detection
title_fullStr How modular structure can simplify tasks on networks: parameterizing graph optimization by fast local community detection
title_full_unstemmed How modular structure can simplify tasks on networks: parameterizing graph optimization by fast local community detection
title_short How modular structure can simplify tasks on networks: parameterizing graph optimization by fast local community detection
title_sort how modular structure can simplify tasks on networks: parameterizing graph optimization by fast local community detection
topic Research Articles
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4156142/
https://www.ncbi.nlm.nih.gov/pubmed/25294962
http://dx.doi.org/10.1098/rspa.2014.0224
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