Prediction of new scientific collaborations through multiplex networks

The establishment of new collaborations among scientists fertilizes the scientific environment, fostering novel discoveries. Understanding the dynamics driving the development of scientific collaborations is thus crucial to characterize the structure and evolution of science. In this work, we levera...

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Detalles Bibliográficos
Autores: Tuninetti, Marta, Aleta, Alberto, Paolotti, Daniela, Moreno, Yamir, Starnini, Michele
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2021
País:España
Institución:Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)
Repositorio:Recercat. Dipósit de la Recerca de Catalunya
OAI Identifier:oai:recercat.cat:10230/72503
Acceso en línea:https://hdl.handle.net/10230/72503
http://dx.doi.org/10.1140/epjds/s13688-021-00282-x
Access Level:acceso abierto
Palabra clave:Scientific collaboration networks
Computational social science
Link prediction
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spelling Prediction of new scientific collaborations through multiplex networksTuninetti, MartaAleta, AlbertoPaolotti, DanielaMoreno, YamirStarnini, MicheleScientific collaboration networksComputational social scienceLink predictionThe establishment of new collaborations among scientists fertilizes the scientific environment, fostering novel discoveries. Understanding the dynamics driving the development of scientific collaborations is thus crucial to characterize the structure and evolution of science. In this work, we leverage the information included in publication records and reconstruct a categorical multiplex networks to improve the prediction of new scientific collaborations. Specifically, we merge different bibliographic sources to quantify the prediction potential of scientific credit, represented by citations, and common interests, measured by the usage of common keywords. We compare several link prediction algorithms based on different dyadic and triadic interactions among scientists, including a recently proposed metric that fully exploits the multiplex representation of scientific networks. Our work paves the way for a deeper understanding of the dynamics driving scientific collaborations, and validates a new algorithm that can be readily applied to link prediction in systems represented as multiplex networks.Springer2026202620212026info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfapplication/pdfhttps://hdl.handle.net/10230/72503http://dx.doi.org/10.1140/epjds/s13688-021-00282-xreponame:Recercat. Dipósit de la Recerca de Catalunyainstname:Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)InglésEPJ Data Science. 2021;10(1):25This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.http://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccessoai:recercat.cat:10230/725032026-05-29T05:05:01Z
dc.title.none.fl_str_mv Prediction of new scientific collaborations through multiplex networks
title Prediction of new scientific collaborations through multiplex networks
spellingShingle Prediction of new scientific collaborations through multiplex networks
Tuninetti, Marta
Scientific collaboration networks
Computational social science
Link prediction
title_short Prediction of new scientific collaborations through multiplex networks
title_full Prediction of new scientific collaborations through multiplex networks
title_fullStr Prediction of new scientific collaborations through multiplex networks
title_full_unstemmed Prediction of new scientific collaborations through multiplex networks
title_sort Prediction of new scientific collaborations through multiplex networks
dc.creator.none.fl_str_mv Tuninetti, Marta
Aleta, Alberto
Paolotti, Daniela
Moreno, Yamir
Starnini, Michele
author Tuninetti, Marta
author_facet Tuninetti, Marta
Aleta, Alberto
Paolotti, Daniela
Moreno, Yamir
Starnini, Michele
author_role author
author2 Aleta, Alberto
Paolotti, Daniela
Moreno, Yamir
Starnini, Michele
author2_role author
author
author
author
dc.subject.none.fl_str_mv Scientific collaboration networks
Computational social science
Link prediction
topic Scientific collaboration networks
Computational social science
Link prediction
description The establishment of new collaborations among scientists fertilizes the scientific environment, fostering novel discoveries. Understanding the dynamics driving the development of scientific collaborations is thus crucial to characterize the structure and evolution of science. In this work, we leverage the information included in publication records and reconstruct a categorical multiplex networks to improve the prediction of new scientific collaborations. Specifically, we merge different bibliographic sources to quantify the prediction potential of scientific credit, represented by citations, and common interests, measured by the usage of common keywords. We compare several link prediction algorithms based on different dyadic and triadic interactions among scientists, including a recently proposed metric that fully exploits the multiplex representation of scientific networks. Our work paves the way for a deeper understanding of the dynamics driving scientific collaborations, and validates a new algorithm that can be readily applied to link prediction in systems represented as multiplex networks.
publishDate 2021
dc.date.none.fl_str_mv 2021
2026
2026
2026
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv https://hdl.handle.net/10230/72503
http://dx.doi.org/10.1140/epjds/s13688-021-00282-x
url https://hdl.handle.net/10230/72503
http://dx.doi.org/10.1140/epjds/s13688-021-00282-x
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv EPJ Data Science. 2021;10(1):25
dc.rights.none.fl_str_mv http://creativecommons.org/licenses/by/4.0/
info:eu-repo/semantics/openAccess
rights_invalid_str_mv http://creativecommons.org/licenses/by/4.0/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv Springer
publisher.none.fl_str_mv Springer
dc.source.none.fl_str_mv reponame:Recercat. Dipósit de la Recerca de Catalunya
instname:Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)
instname_str Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)
reponame_str Recercat. Dipósit de la Recerca de Catalunya
collection Recercat. Dipósit de la Recerca de Catalunya
repository.name.fl_str_mv
repository.mail.fl_str_mv
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