Title Triadic influence as a proxy for compatibility in social relationships
Authors Ruiz-García M. , Ozaita J. , Pereda M. , ALFONSO COSTILLO, ANTONIO, BRAÑAS GARZA, PABLO ERNESTO, Cuesta J.A. , Sánchez A.
External publication No
Scope Article
Nature Científica
JCR Quartile 1
SJR Quartile 1
SJR Impact 3.737
Web https://www.scopus.com/inward/record.uri?eid=2-s2.0-85150836104&doi=10.1073%2fpnas.2215041120&partnerID=40&md5=c6900954b5b1428089e1ddadb1435ff5
Publication date 01/03/2023
ISI 001001376000001
Scopus Id 2-s2.0-85150836104
DOI 10.1073/pnas.2215041120
Abstract Networks 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. Copyright © 2023 the Author(s).
Keywords article; controlled study; embedding; friend; human; human experiment; machine learning; major clinical study; personality; prediction; probability; social interaction; social network; friend; human relation; school; social interaction; social network; Friends; Humans; Interpersonal Relations; Schools; Social Interaction; Social Networking
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