Inference and influence of network structure using snapshot social behavior without network data
| Autor: | Antonia Godoy-Lorite, Nick S. Jones |
|---|---|
| Přispěvatelé: | Engineering & Physical Science Research Council (EPSRC) |
| Rok vydání: | 2021 |
| Předmět: |
Exploit
Computer science media_common.quotation_subject Population Inference 01 natural sciences Social preferences Voting 0103 physical sciences 050602 political science & public administration Econometrics media_common.cataloged_instance European union 010306 general physics education Research Articles media_common Network model Applied Physics Structure (mathematical logic) education.field_of_study Network Science Multidisciplinary 05 social sciences SciAdv r-articles 0506 political science Research Article |
| Zdroj: | Science Advances |
| Popis: | Inference method uncovers homophilic network structures and behavior from only a single snapshot of population behavioral data. Population behavior, like voting and vaccination, depends on the structure of social networks. This structure can differ depending on behavior type and is typically hidden. However, we do often have behavioral data, albeit only snapshots taken at one time point. We present a method jointly inferring a model for both network structure and human behavior using only snapshot population-level behavioral data. This exploits the simplicity of a few parameter model, geometric sociodemographic network model, and a spin-based model of behavior. We illustrate, for the European Union referendum and two London mayoral elections, how the model offers both prediction and the interpretation of the homophilic inclinations of the population. Beyond extracting behavior-specific network structure from behavioral datasets, our approach yields a framework linking inequalities and social preferences to behavioral outcomes. We illustrate potential network-sensitive policies: How changes to income inequality, social temperature, and homophilic preferences might have reduced polarization in a recent election. |
| Databáze: | OpenAIRE |
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