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.
Methods in Ecology and Evolution
|March 11, 2024
Summary
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.
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.
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