ArticleScience advances2021
Inference and influence of network structure using snapshot social behavior without network data.
Article in Science advances, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
3 citing papers in PubMed.
- Enhancing Molecular Network-Based Cancer Driver Gene Prediction Using Machine Learning Approaches: Current Challenges and Opportunities.Journal of cellular and molecular medicine · 2025Review
- Evidence-driven spatiotemporal COVID-19 hospitalization prediction with Ising dynamics.Nature communications · 2023Article
- Influencing dynamics on social networks without knowledge of network microstructure.Journal of the Royal Society, Interface · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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.
Identifiers
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.