Inference and influence of network structure using snapshot social behavior without network data

In a more and more polarized world, the role of underling social structure driving social influence in opinions and behaviours is still unexplored. In this study we developed a method, the kernel-Blau-Ising model (KBI), to uncover how people are influenced/connected based on their socio-demographic coordinates (e.g., income, age, education, postcode), and tested the model in the EU referendum and two London Mayoral elections.

Outline of the KBI methodology (high res image here). Input data consist of aggregated behavioral data for different geographical areas and sociodemographic variables (age, income, education, etc.) associated to those areas (from census data). (A) Heatmap of (hypothetical) behavioral data in Greater London, in this case electoral outcomes, where red represents 100% votes to Labour and blue represents 100% votes to Conservatives. (B) Probability distribution of behavioral outcomes in (A). (C) Blau space representation of the behavioral outcomes spanned by sociodemographic characteristics (e.g., age and income). (D) Blau space representation of KBI approach using input data in and learning parameters: the External Fields, which account for the general trends, e.g., older people are more likely to vote Conservatives than younger people, and the network that connects the population according to their distances in the Blau space and their homophilic preferences. Once the model parameters are learnt, we can further estimate how changes and interventions affect behavioral outcomes. Examples of potential network-sensitive intervention strategies: how changes to income distribution (E) and homophilic preferences (F) can reduce behavioral polarization.

Despite using no social network data, we discover established signatures of homophily, the tendency to befriend those similar to oneself—the stronger homophily is, the more social segregation. We found consistent geographical segregation for the three elections, while education was a strong segregation factor for the EU Referendum, it wasn’t for Mayoral Elections, however, age and income were. The model can be used to explore how reducing inequalities or encouraging mixing among groups can reduce social polarization. You can read about our work "Inference of a universal social scale and segregation measures using social connectivity kernels" free in the journal Science Advances here. Antonia and Nick