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Triadic influence as a proxy for compatibility in social relationships

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dc.affiliation.institutoUC3M. Instituto Universitario sobre Modelización y Simulación en Fluidodinámica, Nanociencia y Matemática Industrial Gregorio Millán Barbanyes
dc.contributor.authorRuiz García, Miguel
dc.contributor.authorOzaita Corral, Juan
dc.contributor.authorPereda García, María
dc.contributor.authorAlfonso, Antonio
dc.contributor.authorBrañas Garza, Pablo
dc.contributor.authorCuesta Ruiz, Jose Antonio
dc.contributor.authorSánchez Valdés, Ariel
dc.contributor.funderEuropean Commissionen
dc.contributor.funderMinisterio de Ciencia e Innovación (España)es
dc.contributor.funderUniversidad Carlos III de Madrides
dc.date.accessioned2023-12-19T12:35:17Z
dc.date.available2023-12-19T12:35:17Z
dc.date.issued2023-03-28
dc.description.abstractNetworks of social interactions are the substrate upon which civilizations are built. Often, we create new bonds with people that we like or feel that our relationships are damaged through the intervention of third parties. Despite their importance and the huge impact that these processes have in our lives, quantitative scientific understanding of them is still in its infancy, mainly due to the difficulty of collecting large datasets of social networks including individual attributes. In this work, we present a thorough study of real social networks of 13 schools, with more than 3,000 students and 60,000 declared positive and negative relationships, including tests for personal traits of all the students. We introduce a metric -the 'triadic influence'- that measures the influence of nearest neighbors in the relationships of their contacts. We use neural networks to predict the sign of the relationships in these social networks, extracting the probability that two students are friends or enemies depending on their personal attributes or the triadic influence. We alternatively use a high-dimensional embedding of the network structure to also predict the relationships. Remarkably, using the triadic influence (a simple one-dimensional metric) achieves the best accuracy, and adding the personal traits of the students does not improve the results, suggesting that the triadic influence acts as a proxy for the social compatibility of students. We postulate that the probabilities extracted from the neural networks - functions of the triadic influence and the personalities of the students - control the evolution of real social networks, opening an avenue for the quantitative study of these systems.en
dc.description.sponsorshipThis work has been partly supported by grant PGC2018-098186-B-I00 (BASIC) funded by MCIN/AEI/10.13039/501100011033 and by "ERDF A way of making Europe". M.R.-G. acknowledges support from the Spanish Ministry of Science and Innovation and NextGenerationEU through the Ramón y Cajal program (RYC2021-032055-I) and from the CONEX-Plus program funded by Universidad Carlos III de Madrid and the European Union's Horizon 2020 research and innovation program under the Marie Sklodowska-Curie grant agreement No. 801538. P.B.-G. acknowledges support from MCIN (PID2021-126892NB-I00), Agencia Andaluza de Cooperación Internacional para el Desarrollo (AACID-0I008/2020), Universidad de Granada (B.SEJ.280.UGR20), and Junta de Andalucía (PY18-FR-007).en
dc.format.extent8es
dc.identifier.bibliographicCitationRuiz-García, M., Ozaita, J., Pereda, M., Alfonso, A., Brañas–Garza, P., Cuesta, J. A., & Sánchez, Á. (2023). Triadic influence as a proxy for compatibility in social relationships. Proceedings of the National Academy of Sciences of the United States of America, 120(13).en
dc.identifier.doihttps://doi.org/10.1073/pnas.2215041120
dc.identifier.issn0027-8424
dc.identifier.publicationfirstpage1es
dc.identifier.publicationissue13, e2215041120es
dc.identifier.publicationlastpage8es
dc.identifier.publicationtitleProceedings of the National Academy of Sciences of the United States of Americaen
dc.identifier.publicationvolume120es
dc.identifier.urihttps://hdl.handle.net/10016/39115
dc.identifier.uxxiAR/0000033470
dc.language.isoenges
dc.publisherPNASen
dc.relation.projectIDinfo:eu-repo/grantAgreement/EC/COFUND-GA-2017-801538es
dc.relation.projectIDGobierno de España. PGC2018-098186-B-I00es
dc.rights© 2023 the Author(s).en
dc.rightsAtribución-NoComercial-SinDerivadas 3.0 España*
dc.rights.accessRightsopen accessen
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/es/*
dc.subject.ecienciaIngeniería Industriales
dc.subject.ecienciaIngeniería Mecánicaes
dc.subject.ecienciaMatemáticases
dc.subject.otherMachine learningen
dc.subject.otherRelationship predictionen
dc.subject.otherSocial networksen
dc.subject.otherTriadic influenceen
dc.titleTriadic influence as a proxy for compatibility in social relationshipsen
dc.typeresearch article*
dc.type.hasVersionVoR*
dspace.entity.typePublication
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