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Temporal patterns behind the strength of persistent ties

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dc.affiliation.dptoUC3M. Departamento de Matemáticases
dc.affiliation.grupoinvUC3M. Grupo de Investigación: Interdisciplinar de Sistemas Complejos (GISC)es
dc.contributor.authorNavarro Hernández, Henry Daniel
dc.contributor.authorMiritello, Giovanna
dc.contributor.authorCanales, Arturo
dc.contributor.authorMoro, Esteban
dc.contributor.funderMinisterio de Economía y Competitividad (España)es
dc.date.accessioned2019-07-16T10:46:49Z
dc.date.available2019-07-16T10:46:49Z
dc.date.issued2017-12-16
dc.description.abstractSocial networks are made out of strong and weak ties having very different structural and dynamical properties. But what features of human interaction build a strong tie? Here we approach this question from a practical way by finding what are the properties of social interactions that make ties more persistent and thus stronger to maintain social interactions in the future. Using a large longitudinal mobile phone database we build a predictive model of tie persistence based on intensity, intimacy, structural and temporal patterns of social interaction. While our results confirm that structural (embeddedness) and intensity (number of calls) features are correlated with tie persistence, temporal features of communication events are better and more efficient predictors for tie persistence. Specifically, although communication within ties is always bursty we find that ties that are more bursty than the average are more likely to decay, signaling that tie strength is not only reflected in the intensity or topology of the network, but also on how individuals distribute time or attention across their relationships. We also found that stable relationships have and require a constant rhythm and if communication is halted for more than 8 times the previous communication frequency, most likely the tie will decay. Our results not only are important to understand the strength of social relationships but also to unveil the entanglement between the different temporal scales in networks, from microscopic tie burstiness and rhythm to macroscopic network evolution.en
dc.description.sponsorshipEM acknowledges funding from Ministerio de Economía y Competividad (Spain) through projects FIS2013-47532-C3-3-P and FIS2016-78904-C3-3-P.en
dc.format.extent19
dc.identifier.bibliographicCitationNavarro, H., Miritello, G., Canales, A. y Moro, E. (2017).Temporal patterns behind the strength of persistent ties. EPJ Data Science, 6.en
dc.identifier.doihttps://doi.org/10.1140/epjds/s13688-017-0127-3
dc.identifier.issn2193-1127
dc.identifier.publicationtitleEPJ Data Scienceen
dc.identifier.publicationvolume31
dc.identifier.urihttps://hdl.handle.net/10016/28614
dc.identifier.uxxiAR/0000022365
dc.language.isoengen
dc.publisherSpringer Openen
dc.relation.projectIDGobierno de España. FIS2013-47532-C3-3-Pes
dc.relation.projectIDGobierno de España. FIS2016-78904-C3-3-Pes
dc.rights© 2018 The Author(s).en
dc.rightsAtribución 3.0 España*
dc.rights.accessRightsopen accessen
dc.rights.urihttp://creativecommons.org/licenses/by/3.0/es/*
dc.subject.ecienciaMatemáticases
dc.subject.otherSocial networksen
dc.subject.otherTie strengthen
dc.subject.otherTemporal patternsen
dc.titleTemporal patterns behind the strength of persistent tiesen
dc.typeresearch article*
dc.type.hasVersionVoR*
dspace.entity.typePublication
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