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Bayesian modeling and uncertainty quantification for descriptive social networks.

Thomas Nemmers1, Anjana Narayan2, Sudipto Banerjee3

  • 1Lockton Companies, LLC, 4275 Executive Square, Suite 600, La Jolla, CA 92037, USA.

Statistics and Its Interface
|January 22, 2019
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Summary
This summary is machine-generated.

This study introduces a straightforward Bayesian method for analyzing small social networks, quantifying uncertainty in descriptive network analysis. It offers a computationally feasible approach for social scientists using R packages.

Keywords:
Bayesian modelingDirected graphsKrackhardt’s axiomsPageRank algorithmsSocial network analysisUncertainty quantification

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Area of Science:

  • Social Network Analysis
  • Bayesian Statistics
  • Computational Graph Theory

Background:

  • Statistical network analysis methods are complex and data-intensive.
  • Descriptive methods like Krackhardt's axioms and PageRank offer insights but lack uncertainty quantification.
  • There is a research gap in statistical approaches for small social networks.

Purpose of the Study:

  • To present a simple, implementable Bayesian approach for modeling and quantifying uncertainty in small descriptive social networks.
  • To bridge Bayesian statistical inference and social network analysis for practicing social scientists.
  • To provide a computationally feasible method using existing R packages.

Main Methods:

  • A Bayesian statistical inference approach.
  • Utilizing R packages such as 'sna' and 'rjags'.
  • Applying fully Bayesian model-based inference to a small network dataset.

Main Results:

  • Successfully modeled and quantified uncertainty in a small descriptive social network.
  • Demonstrated the feasibility of the Bayesian approach for social scientists.
  • Provided a statistical analysis of transnational identities among 18 websites.

Conclusions:

  • The proposed Bayesian method is a straightforward and computationally feasible tool for uncertainty quantification in small social network analysis.
  • This approach enhances descriptive social network analysis by incorporating statistical rigor.
  • Facilitates the application of advanced statistical techniques in social science research.