Inference of a universal social scale and segregation measures using social connectivity kernels
| العنوان: | Inference of a universal social scale and segregation measures using social connectivity kernels |
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| المؤلفون: | Nick S. Jones, Till Hoffmann |
| المساهمون: | Engineering & Physical Science Research Council (EPSRC) |
| المصدر: | J R Soc Interface |
| بيانات النشر: | The Royal Society, 2020. |
| سنة النشر: | 2020 |
| مصطلحات موضوعية: | FOS: Computer and information sciences, Computer science, Inference, Space (commercial competition), Social Environment, Biochemistry, Social Networking, Social space, 0504 sociology, 050602 political science & public administration, Blau space, ego networks, media_common, Social distance, 05 social sciences, Computer Science - Social and Information Networks, segregation, 0506 political science, bepress|Social and Behavioral Sciences|Sociology, bepress|Social and Behavioral Sciences|Sociology|Quantitative, Qualitative, Comparative, and Historical Methodologies, Conditional independence, Metric (mathematics), SocArXiv|Social and Behavioral Sciences|Sociology|Social Psychology and Interaction, Biotechnology, social networks, Physics - Physics and Society, 050402 sociology, General Science & Technology, media_common.quotation_subject, Biomedical Engineering, Biophysics, FOS: Physical sciences, Bioengineering, Physics and Society (physics.soc-ph), Methodology (stat.ME), SocArXiv|Social and Behavioral Sciences|Sociology, Biomaterials, Humans, Statistics - Methodology, bepress|Social and Behavioral Sciences|Sociology|Social Psychology and Interaction, Social and Information Networks (cs.SI), inference, Social Segregation, Bayes Theorem, Data science, Friendship, SocArXiv|Social and Behavioral Sciences|Sociology|Mathematical Sociology, bepress|Social and Behavioral Sciences, SocArXiv|Social and Behavioral Sciences, Life Sciences–Mathematics interface |
| الوصف: | How people connect with one another is a fundamental question in the social sciences, and the resulting social networks can have a profound impact on our daily lives. Blau offered a powerful explanation: people connect with one another based on their positions in a social space. Yet a principled measure of social distance, allowing comparison within and between societies, remains elusive. We use the connectivity kernel of conditionally-independent edge models to develop a family of segregation statistics with desirable properties: they offer an intuitive and universal characteristic scale on social space (facilitating comparison across datasets and societies), are applicable to multivariate and mixed node attributes, and capture segregation at the level of individuals, pairs of individuals, and society as a whole. We show that the segregation statistics can induce a metric on Blau space (a space spanned by the attributes of the members of society) and provide maps of two societies. Under a Bayesian paradigm, we infer the parameters of the connectivity kernel from eleven ego-network datasets collected in four surveys in the United Kingdom and United States. The importance of different dimensions of Blau space is similar across time and location, suggesting a macroscopically stable social fabric. Physical separation and age differences have the most significant impact on segregation within friendship networks with implications for intergenerational mixing and isolation in later stages of life. Comment: Article: 23 pages, 3 figures. Supplementary material: 8 pages, 1 figure |
| تدمد: | 1742-5662 1742-5689 |
| الوصول الحر: | https://explore.openaire.eu/search/publication?articleId=doi_dedup___::95f22d3512c670395d1d9228b66aa3bdTest https://doi.org/10.1098/rsif.2020.0638Test |
| حقوق: | OPEN |
| رقم الانضمام: | edsair.doi.dedup.....95f22d3512c670395d1d9228b66aa3bd |
| قاعدة البيانات: | OpenAIRE |
| تدمد: | 17425662 17425689 |
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