A fast variable neighborhood search approach for multi-objective community detection

Community detection in social networks is becoming one of the key tasks in social network analysis, since it helps analyzing groups of users with similar interests. This task is also useful in different areas, such as biology (interactions of genes and proteins), psychology (diagnostic criteria), or...

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Detalles Bibliográficos
Autores: Perez-Pelo, Sergio, Sanchez-Oro, Jesús, Gonzalez-Pardo, Antonio, Duarte, Abraham
Tipo de recurso: artículo
Fecha de publicación:2021
País:España
Institución:Universidad Rey Juan Carlos
Repositorio:BURJC-Digital. Repositorio Institucional de la Universidad Rey Juan Carlos
OAI Identifier:oai:burjcdigital.urjc.es:10115/29126
Acceso en línea:https://hdl.handle.net/10115/29126
Access Level:acceso abierto
Palabra clave:Community Detection
Variable Neighborhood Search
Greedy Randomized Adaptive Search Procedure
Metaheuristic
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spelling A fast variable neighborhood search approach for multi-objective community detectionPerez-Pelo, SergioSanchez-Oro, JesúsGonzalez-Pardo, AntonioDuarte, AbrahamCommunity DetectionVariable Neighborhood SearchGreedy Randomized Adaptive Search ProcedureMetaheuristicCommunity detection in social networks is becoming one of the key tasks in social network analysis, since it helps analyzing groups of users with similar interests. This task is also useful in different areas, such as biology (interactions of genes and proteins), psychology (diagnostic criteria), or criminology (fraud detection). This paper presents a metaheuristic approach based on Variable Neighborhood Search (VNS) which leverages the combination of quality and diversity of a constructive procedure inspired in Greedy Randomized Adaptative Search Procedure (GRASP) for detecting communities in social networks. In this work, the community detection problem is modeled as a bi-objective optimization problem, where the two objective functions to be optimized are the Negative Ratio Association (NRA) and Ratio Cut (RC), two objectives that have already been proven to be in conflict. To evaluate the quality of the obtained solutions, we use the Normalized Mutual Information (NMI) metric for the instances under evaluation whose optimal solution is known, and modularity for those in which the optimal solution is unknown. Furthermore, we use metrics widely used in multiobjective optimization community to evaluate solutions, such as coverage, ϵ-indicator, hypervolume, and inverted generational distance. The obtained results outperform the state-of-the-art method for community detection over a set of real-life instances in both, quality and computing time.Applied Soft Computing (Elsevier)202420242021info:eu-repo/semantics/articleapplication/pdfhttps://hdl.handle.net/10115/29126reponame:BURJC-Digital. Repositorio Institucional de la Universidad Rey Juan Carlosinstname:Universidad Rey Juan CarlosInglésAttribution-NonCommercial-NoDerivs 4.0 Internationalhttps://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccessoai:burjcdigital.urjc.es:10115/291262026-06-24T12:48:17Z
dc.title.none.fl_str_mv A fast variable neighborhood search approach for multi-objective community detection
title A fast variable neighborhood search approach for multi-objective community detection
spellingShingle A fast variable neighborhood search approach for multi-objective community detection
Perez-Pelo, Sergio
Community Detection
Variable Neighborhood Search
Greedy Randomized Adaptive Search Procedure
Metaheuristic
title_short A fast variable neighborhood search approach for multi-objective community detection
title_full A fast variable neighborhood search approach for multi-objective community detection
title_fullStr A fast variable neighborhood search approach for multi-objective community detection
title_full_unstemmed A fast variable neighborhood search approach for multi-objective community detection
title_sort A fast variable neighborhood search approach for multi-objective community detection
dc.creator.none.fl_str_mv Perez-Pelo, Sergio
Sanchez-Oro, Jesús
Gonzalez-Pardo, Antonio
Duarte, Abraham
author Perez-Pelo, Sergio
author_facet Perez-Pelo, Sergio
Sanchez-Oro, Jesús
Gonzalez-Pardo, Antonio
Duarte, Abraham
author_role author
author2 Sanchez-Oro, Jesús
Gonzalez-Pardo, Antonio
Duarte, Abraham
author2_role author
author
author
dc.subject.none.fl_str_mv Community Detection
Variable Neighborhood Search
Greedy Randomized Adaptive Search Procedure
Metaheuristic
topic Community Detection
Variable Neighborhood Search
Greedy Randomized Adaptive Search Procedure
Metaheuristic
description Community detection in social networks is becoming one of the key tasks in social network analysis, since it helps analyzing groups of users with similar interests. This task is also useful in different areas, such as biology (interactions of genes and proteins), psychology (diagnostic criteria), or criminology (fraud detection). This paper presents a metaheuristic approach based on Variable Neighborhood Search (VNS) which leverages the combination of quality and diversity of a constructive procedure inspired in Greedy Randomized Adaptative Search Procedure (GRASP) for detecting communities in social networks. In this work, the community detection problem is modeled as a bi-objective optimization problem, where the two objective functions to be optimized are the Negative Ratio Association (NRA) and Ratio Cut (RC), two objectives that have already been proven to be in conflict. To evaluate the quality of the obtained solutions, we use the Normalized Mutual Information (NMI) metric for the instances under evaluation whose optimal solution is known, and modularity for those in which the optimal solution is unknown. Furthermore, we use metrics widely used in multiobjective optimization community to evaluate solutions, such as coverage, ϵ-indicator, hypervolume, and inverted generational distance. The obtained results outperform the state-of-the-art method for community detection over a set of real-life instances in both, quality and computing time.
publishDate 2021
dc.date.none.fl_str_mv 2021
2024
2024
dc.type.none.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv https://hdl.handle.net/10115/29126
url https://hdl.handle.net/10115/29126
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.rights.none.fl_str_mv Attribution-NonCommercial-NoDerivs 4.0 International
https://creativecommons.org/licenses/by-nc-nd/4.0/
info:eu-repo/semantics/openAccess
rights_invalid_str_mv Attribution-NonCommercial-NoDerivs 4.0 International
https://creativecommons.org/licenses/by-nc-nd/4.0/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Applied Soft Computing (Elsevier)
publisher.none.fl_str_mv Applied Soft Computing (Elsevier)
dc.source.none.fl_str_mv reponame:BURJC-Digital. Repositorio Institucional de la Universidad Rey Juan Carlos
instname:Universidad Rey Juan Carlos
instname_str Universidad Rey Juan Carlos
reponame_str BURJC-Digital. Repositorio Institucional de la Universidad Rey Juan Carlos
collection BURJC-Digital. Repositorio Institucional de la Universidad Rey Juan Carlos
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15.811543

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