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Module detection in complex networks using integer optimisation

BACKGROUND: The detection of modules or community structure is widely used to reveal the underlying properties of complex networks in biology, as well as physical and social sciences. Since the adoption of modularity as a measure of network topological properties, several methodologies for the disco...

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Autores principales: Xu, Gang, Bennett, Laura, Papageorgiou, Lazaros G, Tsoka, Sophia
Formato: Texto
Lenguaje:English
Publicado: BioMed Central 2010
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2993711/
https://www.ncbi.nlm.nih.gov/pubmed/21073720
http://dx.doi.org/10.1186/1748-7188-5-36
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author Xu, Gang
Bennett, Laura
Papageorgiou, Lazaros G
Tsoka, Sophia
author_facet Xu, Gang
Bennett, Laura
Papageorgiou, Lazaros G
Tsoka, Sophia
author_sort Xu, Gang
collection PubMed
description BACKGROUND: The detection of modules or community structure is widely used to reveal the underlying properties of complex networks in biology, as well as physical and social sciences. Since the adoption of modularity as a measure of network topological properties, several methodologies for the discovery of community structure based on modularity maximisation have been developed. However, satisfactory partitions of large graphs with modest computational resources are particularly challenging due to the NP-hard nature of the related optimisation problem. Furthermore, it has been suggested that optimising the modularity metric can reach a resolution limit whereby the algorithm fails to detect smaller communities than a specific size in large networks. RESULTS: We present a novel solution approach to identify community structure in large complex networks and address resolution limitations in module detection. The proposed algorithm employs modularity to express network community structure and it is based on mixed integer optimisation models. The solution procedure is extended through an iterative procedure to diminish effects that tend to agglomerate smaller modules (resolution limitations). CONCLUSIONS: A comprehensive comparative analysis of methodologies for module detection based on modularity maximisation shows that our approach outperforms previously reported methods. Furthermore, in contrast to previous reports, we propose a strategy to handle resolution limitations in modularity maximisation. Overall, we illustrate ways to improve existing methodologies for community structure identification so as to increase its efficiency and applicability.
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spelling pubmed-29937112010-12-23 Module detection in complex networks using integer optimisation Xu, Gang Bennett, Laura Papageorgiou, Lazaros G Tsoka, Sophia Algorithms Mol Biol Research BACKGROUND: The detection of modules or community structure is widely used to reveal the underlying properties of complex networks in biology, as well as physical and social sciences. Since the adoption of modularity as a measure of network topological properties, several methodologies for the discovery of community structure based on modularity maximisation have been developed. However, satisfactory partitions of large graphs with modest computational resources are particularly challenging due to the NP-hard nature of the related optimisation problem. Furthermore, it has been suggested that optimising the modularity metric can reach a resolution limit whereby the algorithm fails to detect smaller communities than a specific size in large networks. RESULTS: We present a novel solution approach to identify community structure in large complex networks and address resolution limitations in module detection. The proposed algorithm employs modularity to express network community structure and it is based on mixed integer optimisation models. The solution procedure is extended through an iterative procedure to diminish effects that tend to agglomerate smaller modules (resolution limitations). CONCLUSIONS: A comprehensive comparative analysis of methodologies for module detection based on modularity maximisation shows that our approach outperforms previously reported methods. Furthermore, in contrast to previous reports, we propose a strategy to handle resolution limitations in modularity maximisation. Overall, we illustrate ways to improve existing methodologies for community structure identification so as to increase its efficiency and applicability. BioMed Central 2010-11-12 /pmc/articles/PMC2993711/ /pubmed/21073720 http://dx.doi.org/10.1186/1748-7188-5-36 Text en Copyright ©2010 Xu et al; licensee BioMed Central Ltd. http://creativecommons.org/licenses/by/2.0 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research
Xu, Gang
Bennett, Laura
Papageorgiou, Lazaros G
Tsoka, Sophia
Module detection in complex networks using integer optimisation
title Module detection in complex networks using integer optimisation
title_full Module detection in complex networks using integer optimisation
title_fullStr Module detection in complex networks using integer optimisation
title_full_unstemmed Module detection in complex networks using integer optimisation
title_short Module detection in complex networks using integer optimisation
title_sort module detection in complex networks using integer optimisation
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2993711/
https://www.ncbi.nlm.nih.gov/pubmed/21073720
http://dx.doi.org/10.1186/1748-7188-5-36
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