DeepAI AI Chat
Log In Sign Up

Inference of a universal social scale and segregation measures using social connectivity kernels

by   Till Hoffmann, et al.

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.


Semantic homophily in online communication: evidence from Twitter

People are observed to assortatively connect on a set of traits. This ph...

Estimating the number of SARS-CoV-2 infections and the impact of social distancing in the United States

Understanding the number of individuals who have been infected with the ...

Interpreting Social Respect: A Normative Lens for ML Models

Machine learning is often viewed as an inherently value-neutral process:...

Learning multi-faceted representations of individuals from heterogeneous evidence using neural networks

Inferring latent attributes of people online is an important social comp...

Generate Country-Scale Networks of Interaction from Scattered Statistics

It is common to define the structure of interactions among a population ...

Apps, Places and People: strategies, limitations and trade-offs in the physical and digital worlds

Cognition has been found to constrain several aspects of human behaviour...