# Inference and Influence of Large-Scale Social Networks Using Snapshot Population Behaviour without Network Data

**Authors:** Antonia Godoy-Lorite, Nick S. Jones  
**arXiv:** [2003.07146](https://arxiv.org/abs/2003.07146)  
**Date:** 2020-03-16  
**Summary page:** https://www.summarizepaper.com/en/paper/2003.07146v2/

## Key points

- Study focuses on influence of social networks on population behaviors like voting and vaccination
- Social networks play crucial role in shaping behaviors but are often hidden and difficult to analyze
- Large-scale behavioral data can provide valuable insights, even if they are snapshots at specific timepoints
- Researchers propose method combining inference of network structure with networked model of human behavior using snapshot data
- Leveraging parameterized geometric socio-demographic network model and spin-based model of behavior
- Approach applied to EU Referendum and two London Mayoral elections, offering predictions and interpretations of homophilic inclinations
- Approach enables crude calculus linking inequalities and social preferences to behavioral outcomes
- Examples provided for potential network-sensitive policies that could reduce polarization in elections by addressing factors like income inequality, social temperature, and homophilic preferences
- Study presents innovative method for understanding influence of social networks on population behaviors using limited snapshot data
- Offers insights into how network structures shape behaviors and implications for designing effective policies considering social dynamics to reduce polarization in elections.

## Summary

The study focuses on the influence of social networks on population behaviors such as voting and vaccination. While social networks play a crucial role in shaping these behaviors, they are often hidden and difficult to analyze. However, large-scale behavioral data can provide valuable insights, even if they are only snapshots taken at a specific timepoint. To address this challenge, the researchers propose a method that combines the inference of large-scale network structure with a networked model of human behavior using only snapshot population behavioral data. They leverage a simple parameterized geometric socio-demographic network model and a spin-based model of behavior to achieve this. The authors demonstrate the effectiveness of their approach by applying it to the EU Referendum and two London Mayoral elections. The model not only offers predictions but also provides interpretations of our homophilic inclinations, which refer to our tendency to associate with people who share similar characteristics or beliefs. Beyond extracting behavior-specific network structures from large-scale datasets, this approach also enables a crude calculus that links inequalities and social preferences to behavioral outcomes. The researchers provide examples of potential network-sensitive policies that could have reduced polarization in a recent election by addressing factors such as income inequality, social temperature, and homophilic preferences. Overall, this study presents an innovative method for understanding the influence of social networks on population behaviors using limited snapshot data. It offers valuable insights into how network structures shape our behaviors and provides implications for designing effective policies that consider social dynamics in order to reduce polarization in elections.