| 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 |
| Means |
Proc Natl Acad Sci U S A |
| Scope |
Article |
| Nature |
Científica |
| JCR Quartile |
1 |
| SJR Quartile |
1 |
| 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 metricthe triadic influencethat 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 networksfunctions of the triadic influence and the personalities of the studentscontrol 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 r |
| Universidad Loyola members |
|
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