Growing neural gas as a memory mechanism of a heuristic to solve a community detection problem in networks
Autor(a) principal: | |
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Data de Publicação: | 2016 |
Outros Autores: | |
Tipo de documento: | Artigo de conferência |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UNIFESP |
Texto Completo: | http://repositorio.unifesp.br/handle/11600/49398 https://doi.org/10.1016/j.procs.2016.08.110 |
Resumo: | Iterative heuristics are commonly used to address combinatorial optimization problems. However, to meet both robustness and efficiency with these methods when their iterations are independent, it is necessary to consider a high number of iterations or to include local search-based strategies in them. Both approaches are very time-consuming and, consequently, not efficient for medium and large-scale instances of combinatorial optimization problems. In particular, the community detection problem in networks is well-known due to the instances with hundreds to thousands of vertices. In the literature, the heuristics to detect communities in networks that use a local search are those that achieve the partitions with the best solution values. Nevertheless, they are not suitable to tackle medium to large scale networks. This paper presents an adaptive heuristic, named GNGClus, that uses the neural network Growing Neural Gas to play the role of memory mechanism. The computational experiment with LFR networks indicates that the proposed strategy significantly outperformed the same solution method with no memory mechanism. In addition, GNGClus was very competitive with a version of the heuristic that employs an elite set of solutions to guide the solution search. (C) 2016 The Authors. Published by Elsevier B.V. |
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Santos, Camila Pereira [UNIFESP]Nascimento, Maria C. V. [UNIFESP]2019-01-21T10:29:48Z2019-01-21T10:29:48Z2016Procedia Computer Science. Amsterdam, v. 96, p. 485-494, 2016.1877-0509http://repositorio.unifesp.br/handle/11600/49398https://doi.org/10.1016/j.procs.2016.08.110WOS000383252400052.pdf10.1016/j.procs.2016.08.110WOS:000383252400052Iterative heuristics are commonly used to address combinatorial optimization problems. However, to meet both robustness and efficiency with these methods when their iterations are independent, it is necessary to consider a high number of iterations or to include local search-based strategies in them. Both approaches are very time-consuming and, consequently, not efficient for medium and large-scale instances of combinatorial optimization problems. In particular, the community detection problem in networks is well-known due to the instances with hundreds to thousands of vertices. In the literature, the heuristics to detect communities in networks that use a local search are those that achieve the partitions with the best solution values. Nevertheless, they are not suitable to tackle medium to large scale networks. This paper presents an adaptive heuristic, named GNGClus, that uses the neural network Growing Neural Gas to play the role of memory mechanism. The computational experiment with LFR networks indicates that the proposed strategy significantly outperformed the same solution method with no memory mechanism. In addition, GNGClus was very competitive with a version of the heuristic that employs an elite set of solutions to guide the solution search. (C) 2016 The Authors. Published by Elsevier B.V.Instituto de Ciência e Tecnologia, Universidade Federal de São Paulo (UNIFESP) Av. Cesare M. G. Lattes, 1201, Eugênio de Mello, São José dos Campos-SP, CEP: 12247-014, BrasilInstituto de Ciência e Tecnologia, Universidade Federal de São Paulo (UNIFESP) Av. Cesare M. G. Lattes, 1201, Eugênio de Mello, São José dos Campos-SP, CEP: 12247-014, BrasilWeb of Science485-494engFunpec-EditoraKnowledge-Based And Intelligent Information & Engineering Systems: Proceedings Of The 20th International Conference Kes-2016Growing Neural GasCommunity Detection In NetworksHeuristic MethodsGrowing neural gas as a memory mechanism of a heuristic to solve a community detection problem in networksinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/conferenceObjectinfo:eu-repo/semantics/openAccessreponame:Repositório Institucional da UNIFESPinstname:Universidade Federal de São Paulo (UNIFESP)instacron:UNIFESPORIGINALWOS000383252400052.pdfapplication/pdf229672${dspace.ui.url}/bitstream/11600/49398/1/WOS000383252400052.pdfa36b51692ce5684def0e1688d8a321e5MD51open accessTEXTWOS000383252400052.pdf.txtWOS000383252400052.pdf.txtExtracted texttext/plain31506${dspace.ui.url}/bitstream/11600/49398/8/WOS000383252400052.pdf.txtc460340e8aa9781e07e2ac6a20da89eaMD58open accessTHUMBNAILWOS000383252400052.pdf.jpgWOS000383252400052.pdf.jpgIM Thumbnailimage/jpeg6357${dspace.ui.url}/bitstream/11600/49398/10/WOS000383252400052.pdf.jpgfb9a6f09ab8735e0e253d616c2a79e4bMD510open access11600/493982023-06-05 19:12:16.789open accessoai:repositorio.unifesp.br:11600/49398Repositório InstitucionalPUBhttp://www.repositorio.unifesp.br/oai/requestopendoar:34652023-06-05T22:12:16Repositório Institucional da UNIFESP - Universidade Federal de São Paulo (UNIFESP)false |
dc.title.en.fl_str_mv |
Growing neural gas as a memory mechanism of a heuristic to solve a community detection problem in networks |
title |
Growing neural gas as a memory mechanism of a heuristic to solve a community detection problem in networks |
spellingShingle |
Growing neural gas as a memory mechanism of a heuristic to solve a community detection problem in networks Santos, Camila Pereira [UNIFESP] Growing Neural Gas Community Detection In Networks Heuristic Methods |
title_short |
Growing neural gas as a memory mechanism of a heuristic to solve a community detection problem in networks |
title_full |
Growing neural gas as a memory mechanism of a heuristic to solve a community detection problem in networks |
title_fullStr |
Growing neural gas as a memory mechanism of a heuristic to solve a community detection problem in networks |
title_full_unstemmed |
Growing neural gas as a memory mechanism of a heuristic to solve a community detection problem in networks |
title_sort |
Growing neural gas as a memory mechanism of a heuristic to solve a community detection problem in networks |
author |
Santos, Camila Pereira [UNIFESP] |
author_facet |
Santos, Camila Pereira [UNIFESP] Nascimento, Maria C. V. [UNIFESP] |
author_role |
author |
author2 |
Nascimento, Maria C. V. [UNIFESP] |
author2_role |
author |
dc.contributor.author.fl_str_mv |
Santos, Camila Pereira [UNIFESP] Nascimento, Maria C. V. [UNIFESP] |
dc.subject.eng.fl_str_mv |
Growing Neural Gas Community Detection In Networks Heuristic Methods |
topic |
Growing Neural Gas Community Detection In Networks Heuristic Methods |
description |
Iterative heuristics are commonly used to address combinatorial optimization problems. However, to meet both robustness and efficiency with these methods when their iterations are independent, it is necessary to consider a high number of iterations or to include local search-based strategies in them. Both approaches are very time-consuming and, consequently, not efficient for medium and large-scale instances of combinatorial optimization problems. In particular, the community detection problem in networks is well-known due to the instances with hundreds to thousands of vertices. In the literature, the heuristics to detect communities in networks that use a local search are those that achieve the partitions with the best solution values. Nevertheless, they are not suitable to tackle medium to large scale networks. This paper presents an adaptive heuristic, named GNGClus, that uses the neural network Growing Neural Gas to play the role of memory mechanism. The computational experiment with LFR networks indicates that the proposed strategy significantly outperformed the same solution method with no memory mechanism. In addition, GNGClus was very competitive with a version of the heuristic that employs an elite set of solutions to guide the solution search. (C) 2016 The Authors. Published by Elsevier B.V. |
publishDate |
2016 |
dc.date.issued.fl_str_mv |
2016 |
dc.date.accessioned.fl_str_mv |
2019-01-21T10:29:48Z |
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2019-01-21T10:29:48Z |
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Procedia Computer Science. Amsterdam, v. 96, p. 485-494, 2016. |
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http://repositorio.unifesp.br/handle/11600/49398 https://doi.org/10.1016/j.procs.2016.08.110 |
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1877-0509 |
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Procedia Computer Science. Amsterdam, v. 96, p. 485-494, 2016. 1877-0509 WOS000383252400052.pdf 10.1016/j.procs.2016.08.110 WOS:000383252400052 |
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http://repositorio.unifesp.br/handle/11600/49398 https://doi.org/10.1016/j.procs.2016.08.110 |
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eng |
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Knowledge-Based And Intelligent Information & Engineering Systems: Proceedings Of The 20th International Conference Kes-2016 |
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