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BISoN: A Bayesian Framework for Inference of Social Networks.

Jordan D A Hart1, Michael N Weiss2, Daniel W Franks3

  • 1University of Exeter - Department of Psychology, Washington Singer Building Perry Road Exeter, Exeter, Devon EX4 4QJ, United Kingdom of Great Britain and Northern Ireland.

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Summary
This summary is machine-generated.

This study introduces BISoN, a Bayesian framework for analyzing animal social networks from observational data. It quantifies uncertainty in social connections, improving the reliability of network analyses and scientific inferences.

Keywords:
Bayesian inferenceanimal social network analysisdyadic regressionnetwork metricsnodal regression

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Area of Science:

  • Ecology
  • Behavioral Ecology
  • Network Science

Background:

  • Animal social networks are typically built using estimated edge weights, often from observational data.
  • Existing methods struggle to quantify uncertainty in these estimates and handle complex observational data.
  • This uncertainty is not propagated to subsequent statistical analyses, limiting reliability.

Purpose of the Study:

  • To introduce a unified Bayesian framework, BISoN, for robust social network modeling from observational data.
  • To accommodate diverse observational data types and model confounds at the observation level.
  • To enable downstream statistical analyses and improve the reliability of inferences in social network analysis.

Main Methods:

  • Developed a unified Bayesian framework (BISoN) for social network modeling.
  • Designed the framework to accommodate various observational social data types.
  • Ensured compatibility with established social network analysis methods.

Main Results:

  • BISoN can model complex observational social data, including confounds.
  • The framework successfully propagates uncertainty in edge weights to downstream analyses.
  • Demonstrated application to non-random association tests and regressions on network properties.

Conclusions:

  • The BISoN framework enhances the analysis of animal social networks using observational data.
  • It allows for more comprehensive hypothesis testing and reliable scientific inferences.
  • An R package and scripts are available to facilitate adoption and application.