Analysing research collaboration through co-authorship networks in a big data environment: An efficient parallel approach
Autor(a) principal: | |
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Data de Publicação: | 2020 |
Outros Autores: | , , , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UNESP |
Texto Completo: | http://dx.doi.org/10.1504/IJCSE.2020.106061 http://hdl.handle.net/11449/201662 |
Resumo: | Bibliometry is the quantitative study of scientific productions and enables the characterisation of scientific collaboration networks. However, with the development of science and the increase of scientific production, large collaborative networks are formed, which makes it difficult to extract bibliometrics. In this context, this work presents an efficient parallel optimisation of three bibliometrics for co-authorship network analysis using multithread programming: transitivity, average distance, and diameter. Our experiments found that the time taken to calculate the transitivity value using the sequential approach grows 4.08 times faster than the parallel proposed approach when the size of co-authorship network grows. Similarly, the time taken to calculate the average distance and diameter values using the sequential approach grows 5.27 times faster than the parallel proposed approach when the size of co-authorship network grows. In addition, we report relevant values of speed up and efficiency for the developed algorithms. |
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Repositório Institucional da UNESP |
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Analysing research collaboration through co-authorship networks in a big data environment: An efficient parallel approachBibliometricsCo-authorship networkGraphsKnowledge extractionNoSQLParallel computingBibliometry is the quantitative study of scientific productions and enables the characterisation of scientific collaboration networks. However, with the development of science and the increase of scientific production, large collaborative networks are formed, which makes it difficult to extract bibliometrics. In this context, this work presents an efficient parallel optimisation of three bibliometrics for co-authorship network analysis using multithread programming: transitivity, average distance, and diameter. Our experiments found that the time taken to calculate the transitivity value using the sequential approach grows 4.08 times faster than the parallel proposed approach when the size of co-authorship network grows. Similarly, the time taken to calculate the average distance and diameter values using the sequential approach grows 5.27 times faster than the parallel proposed approach when the size of co-authorship network grows. In addition, we report relevant values of speed up and efficiency for the developed algorithms.Institute of Biosciences Humanities and Exact Sciences (IBILCE) São Paulo State University (UNESP) Campus São José Do Rio PretoFluminense Federal University (UFF)Institute of Biosciences Humanities and Exact Sciences (IBILCE) São Paulo State University (UNESP) Campus São José Do Rio PretoUniversidade Estadual Paulista (Unesp)Fluminense Federal University (UFF)Valêncio, Carlos Roberto [UNESP]De Freitas, José Carlos [UNESP]De Souza, Rogéria Cristiane Gratão [UNESP]Neves, Leandro Alves [UNESP]Zafalon, Geraldo Francisco Donegá [UNESP]Colombini, Angelo CesarTenório, William [UNESP]2020-12-12T02:38:28Z2020-12-12T02:38:28Z2020-01-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/article364-374http://dx.doi.org/10.1504/IJCSE.2020.106061International Journal of Computational Science and Engineering, v. 21, n. 3, p. 364-374, 2020.1742-71931742-7185http://hdl.handle.net/11449/20166210.1504/IJCSE.2020.1060612-s2.0-8508277382759146517545178640000-0002-7449-9022Scopusreponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengInternational Journal of Computational Science and Engineeringinfo:eu-repo/semantics/openAccess2021-10-23T10:10:56Zoai:repositorio.unesp.br:11449/201662Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462021-10-23T10:10:56Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false |
dc.title.none.fl_str_mv |
Analysing research collaboration through co-authorship networks in a big data environment: An efficient parallel approach |
title |
Analysing research collaboration through co-authorship networks in a big data environment: An efficient parallel approach |
spellingShingle |
Analysing research collaboration through co-authorship networks in a big data environment: An efficient parallel approach Valêncio, Carlos Roberto [UNESP] Bibliometrics Co-authorship network Graphs Knowledge extraction NoSQL Parallel computing |
title_short |
Analysing research collaboration through co-authorship networks in a big data environment: An efficient parallel approach |
title_full |
Analysing research collaboration through co-authorship networks in a big data environment: An efficient parallel approach |
title_fullStr |
Analysing research collaboration through co-authorship networks in a big data environment: An efficient parallel approach |
title_full_unstemmed |
Analysing research collaboration through co-authorship networks in a big data environment: An efficient parallel approach |
title_sort |
Analysing research collaboration through co-authorship networks in a big data environment: An efficient parallel approach |
author |
Valêncio, Carlos Roberto [UNESP] |
author_facet |
Valêncio, Carlos Roberto [UNESP] De Freitas, José Carlos [UNESP] De Souza, Rogéria Cristiane Gratão [UNESP] Neves, Leandro Alves [UNESP] Zafalon, Geraldo Francisco Donegá [UNESP] Colombini, Angelo Cesar Tenório, William [UNESP] |
author_role |
author |
author2 |
De Freitas, José Carlos [UNESP] De Souza, Rogéria Cristiane Gratão [UNESP] Neves, Leandro Alves [UNESP] Zafalon, Geraldo Francisco Donegá [UNESP] Colombini, Angelo Cesar Tenório, William [UNESP] |
author2_role |
author author author author author author |
dc.contributor.none.fl_str_mv |
Universidade Estadual Paulista (Unesp) Fluminense Federal University (UFF) |
dc.contributor.author.fl_str_mv |
Valêncio, Carlos Roberto [UNESP] De Freitas, José Carlos [UNESP] De Souza, Rogéria Cristiane Gratão [UNESP] Neves, Leandro Alves [UNESP] Zafalon, Geraldo Francisco Donegá [UNESP] Colombini, Angelo Cesar Tenório, William [UNESP] |
dc.subject.por.fl_str_mv |
Bibliometrics Co-authorship network Graphs Knowledge extraction NoSQL Parallel computing |
topic |
Bibliometrics Co-authorship network Graphs Knowledge extraction NoSQL Parallel computing |
description |
Bibliometry is the quantitative study of scientific productions and enables the characterisation of scientific collaboration networks. However, with the development of science and the increase of scientific production, large collaborative networks are formed, which makes it difficult to extract bibliometrics. In this context, this work presents an efficient parallel optimisation of three bibliometrics for co-authorship network analysis using multithread programming: transitivity, average distance, and diameter. Our experiments found that the time taken to calculate the transitivity value using the sequential approach grows 4.08 times faster than the parallel proposed approach when the size of co-authorship network grows. Similarly, the time taken to calculate the average distance and diameter values using the sequential approach grows 5.27 times faster than the parallel proposed approach when the size of co-authorship network grows. In addition, we report relevant values of speed up and efficiency for the developed algorithms. |
publishDate |
2020 |
dc.date.none.fl_str_mv |
2020-12-12T02:38:28Z 2020-12-12T02:38:28Z 2020-01-01 |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://dx.doi.org/10.1504/IJCSE.2020.106061 International Journal of Computational Science and Engineering, v. 21, n. 3, p. 364-374, 2020. 1742-7193 1742-7185 http://hdl.handle.net/11449/201662 10.1504/IJCSE.2020.106061 2-s2.0-85082773827 5914651754517864 0000-0002-7449-9022 |
url |
http://dx.doi.org/10.1504/IJCSE.2020.106061 http://hdl.handle.net/11449/201662 |
identifier_str_mv |
International Journal of Computational Science and Engineering, v. 21, n. 3, p. 364-374, 2020. 1742-7193 1742-7185 10.1504/IJCSE.2020.106061 2-s2.0-85082773827 5914651754517864 0000-0002-7449-9022 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
International Journal of Computational Science and Engineering |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
364-374 |
dc.source.none.fl_str_mv |
Scopus reponame:Repositório Institucional da UNESP instname:Universidade Estadual Paulista (UNESP) instacron:UNESP |
instname_str |
Universidade Estadual Paulista (UNESP) |
instacron_str |
UNESP |
institution |
UNESP |
reponame_str |
Repositório Institucional da UNESP |
collection |
Repositório Institucional da UNESP |
repository.name.fl_str_mv |
Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP) |
repository.mail.fl_str_mv |
|
_version_ |
1803649807585116160 |