Benchmarking seeding strategies for spreading processes in social networks: an interplay between infuencers, topologies and sizes

The explosion of network science has permitted an understanding of how the structure of social networks affects the dynamics of social contagion. In community-based interventions with spill-over effects, identifying influential spreaders may be harnessed to increase the spreading efficiency of socia...

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Autores: Montes, Felipe, Jaramillo, Ana María, Meisel, Jose D., Díaz Guilera, Albert, Valdivia, Juan A., Sarmiento, Olga L., Zarama, Roberto
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2020
País:España
Institución:Universidad de Barcelona
Repositorio:Dipòsit Digital de la UB
OAI Identifier:oai:diposit.ub.edu:2445/158847
Acceso en línea:https://hdl.handle.net/2445/158847
Access Level:acceso abierto
Palabra clave:Anàlisi de xarxes (Planificació)
Algorismes
Xarxes socials
Network analysis (Planning)
Algorithms
Social networks
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spelling Benchmarking seeding strategies for spreading processes in social networks: an interplay between infuencers, topologies and sizesMontes, FelipeJaramillo, Ana MaríaMeisel, Jose D.Díaz Guilera, AlbertValdivia, Juan A.Sarmiento, Olga L.Zarama, RobertoAnàlisi de xarxes (Planificació)AlgorismesXarxes socialsNetwork analysis (Planning)AlgorithmsSocial networksThe explosion of network science has permitted an understanding of how the structure of social networks affects the dynamics of social contagion. In community-based interventions with spill-over effects, identifying influential spreaders may be harnessed to increase the spreading efficiency of social contagion, in terms of time needed to spread all the largest connected component of the network. Several strategies have been proved to be efficient using only data and simulation-based models in specific network topologies without a consensus of an overall result. Hence, the purpose of this paper is to benchmark the spreading efficiency of seeding strategies related to network structural properties and sizes. We simulate spreading processes on empirical and simulated social networks within a wide range of densities, clustering coefficients, and sizes. We also propose three new decentralized seeding strategies that are structurally different from well-known strategies: community hubs, ambassadors, and random hubs. We observe that the efficiency ranking of strategies varies with the network structure. In general, for sparse networks with community structure, decentralized influencers are suitable for increasing the spreading efficiency. By contrast, when the networks are denser, centralized influencers outperform. These results provide a framework for selecting efficient strategies according to different contexts in which social networks emerge.Nature Publishing Group2020info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfhttps://hdl.handle.net/2445/158847Articles publicats en revistes (Física de la Matèria Condensada)reponame:Dipòsit Digital de la UBinstname:Universidad de BarcelonaInglésReproducció del document publicat a: https://doi.org/10.1038/s41598-020-60239-4Scientific Reports, 2020, vol. 10, num. 1, p. 3666https://doi.org/10.1038/s41598-020-60239-4cc-by (c) Montes, Felipe et al., 2020http://creativecommons.org/licenses/by/3.0/esinfo:eu-repo/semantics/openAccessoai:diposit.ub.edu:2445/1588472026-05-27T06:46:51Z
dc.title.none.fl_str_mv Benchmarking seeding strategies for spreading processes in social networks: an interplay between infuencers, topologies and sizes
title Benchmarking seeding strategies for spreading processes in social networks: an interplay between infuencers, topologies and sizes
spellingShingle Benchmarking seeding strategies for spreading processes in social networks: an interplay between infuencers, topologies and sizes
Montes, Felipe
Anàlisi de xarxes (Planificació)
Algorismes
Xarxes socials
Network analysis (Planning)
Algorithms
Social networks
title_short Benchmarking seeding strategies for spreading processes in social networks: an interplay between infuencers, topologies and sizes
title_full Benchmarking seeding strategies for spreading processes in social networks: an interplay between infuencers, topologies and sizes
title_fullStr Benchmarking seeding strategies for spreading processes in social networks: an interplay between infuencers, topologies and sizes
title_full_unstemmed Benchmarking seeding strategies for spreading processes in social networks: an interplay between infuencers, topologies and sizes
title_sort Benchmarking seeding strategies for spreading processes in social networks: an interplay between infuencers, topologies and sizes
dc.creator.none.fl_str_mv Montes, Felipe
Jaramillo, Ana María
Meisel, Jose D.
Díaz Guilera, Albert
Valdivia, Juan A.
Sarmiento, Olga L.
Zarama, Roberto
author Montes, Felipe
author_facet Montes, Felipe
Jaramillo, Ana María
Meisel, Jose D.
Díaz Guilera, Albert
Valdivia, Juan A.
Sarmiento, Olga L.
Zarama, Roberto
author_role author
author2 Jaramillo, Ana María
Meisel, Jose D.
Díaz Guilera, Albert
Valdivia, Juan A.
Sarmiento, Olga L.
Zarama, Roberto
author2_role author
author
author
author
author
author
dc.subject.none.fl_str_mv Anàlisi de xarxes (Planificació)
Algorismes
Xarxes socials
Network analysis (Planning)
Algorithms
Social networks
topic Anàlisi de xarxes (Planificació)
Algorismes
Xarxes socials
Network analysis (Planning)
Algorithms
Social networks
description The explosion of network science has permitted an understanding of how the structure of social networks affects the dynamics of social contagion. In community-based interventions with spill-over effects, identifying influential spreaders may be harnessed to increase the spreading efficiency of social contagion, in terms of time needed to spread all the largest connected component of the network. Several strategies have been proved to be efficient using only data and simulation-based models in specific network topologies without a consensus of an overall result. Hence, the purpose of this paper is to benchmark the spreading efficiency of seeding strategies related to network structural properties and sizes. We simulate spreading processes on empirical and simulated social networks within a wide range of densities, clustering coefficients, and sizes. We also propose three new decentralized seeding strategies that are structurally different from well-known strategies: community hubs, ambassadors, and random hubs. We observe that the efficiency ranking of strategies varies with the network structure. In general, for sparse networks with community structure, decentralized influencers are suitable for increasing the spreading efficiency. By contrast, when the networks are denser, centralized influencers outperform. These results provide a framework for selecting efficient strategies according to different contexts in which social networks emerge.
publishDate 2020
dc.date.none.fl_str_mv 2020
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/2445/158847
url https://hdl.handle.net/2445/158847
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv Reproducció del document publicat a: https://doi.org/10.1038/s41598-020-60239-4
Scientific Reports, 2020, vol. 10, num. 1, p. 3666
https://doi.org/10.1038/s41598-020-60239-4
dc.rights.none.fl_str_mv cc-by (c) Montes, Felipe et al., 2020
http://creativecommons.org/licenses/by/3.0/es
info:eu-repo/semantics/openAccess
rights_invalid_str_mv cc-by (c) Montes, Felipe et al., 2020
http://creativecommons.org/licenses/by/3.0/es
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Nature Publishing Group
publisher.none.fl_str_mv Nature Publishing Group
dc.source.none.fl_str_mv Articles publicats en revistes (Física de la Matèria Condensada)
reponame:Dipòsit Digital de la UB
instname:Universidad de Barcelona
instname_str Universidad de Barcelona
reponame_str Dipòsit Digital de la UB
collection Dipòsit Digital de la UB
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15,301603

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