Skip to main content
bioRxiv
  • Home
  • About
  • Submit
  • ALERTS / RSS
Advanced Search
New Results

BISoN: A Bayesian Framework for Inference of Social Networks

View ORCID ProfileJordan D. A. Hart, View ORCID ProfileMichael N. Weiss, View ORCID ProfileDaniel W. Franks, View ORCID ProfileLauren J. N. Brent
doi: https://doi.org/10.1101/2021.12.20.473541
Jordan D. A. Hart
1Centre for Research in Animal Behaviour, University of Exeter, UK
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for Jordan D. A. Hart
  • For correspondence: jordan.da.hart@gmail.com
Michael N. Weiss
1Centre for Research in Animal Behaviour, University of Exeter, UK
2Center for Whale Research, Friday Harbour, WA, USA
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for Michael N. Weiss
Daniel W. Franks
3Departments of Biology and Computer Science, University of York, UK
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for Daniel W. Franks
Lauren J. N. Brent
1Centre for Research in Animal Behaviour, University of Exeter, UK
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for Lauren J. N. Brent
  • Abstract
  • Full Text
  • Info/History
  • Metrics
  • Data/Code
  • Preview PDF
Loading

Abstract

  1. Social networks are often constructed from point estimates of edge weights. In many contexts, edge weights are inferred from observational data, and the uncertainty around estimates can be affected by various factors. Though this has been acknowledged in previous work, methods that explicitly quantify uncertainty in edge weights have not yet been widely adopted, and remain undeveloped for many common types of data. Furthermore, existing methods are unable to cope with some of the complexities often found in observational data, and do not propagate uncertainty in edge weights to subsequent statistical analyses.

  2. We introduce a unified Bayesian framework for modelling social networks based on observational data. This framework, which we call BISoN, can accommodate many common types of observational social data, can capture confounds and model effects at the level of observations, and is fully compatible with popular methods used in social network analysis.

  3. We show how the framework can be applied to common types of data and how various types of downstream statistical analyses can be performed, including non-random association tests and regressions on network properties.

  4. Our framework opens up the opportunity to test new types of hypotheses, make full use of observational datasets, and increase the reliability of scientific inferences. We have made example R scripts available to enable adoption of the framework.

Competing Interest Statement

The authors have declared no competing interest.

Footnotes

  • https://github.com/JHart96/bison_examples

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
Back to top
PreviousNext
Posted June 01, 2022.
Download PDF
Data/Code
Email

Thank you for your interest in spreading the word about bioRxiv.

NOTE: Your email address is requested solely to identify you as the sender of this article.

Enter multiple addresses on separate lines or separate them with commas.
BISoN: A Bayesian Framework for Inference of Social Networks
(Your Name) has forwarded a page to you from bioRxiv
(Your Name) thought you would like to see this page from the bioRxiv website.
CAPTCHA
This question is for testing whether or not you are a human visitor and to prevent automated spam submissions.
Share
BISoN: A Bayesian Framework for Inference of Social Networks
Jordan D. A. Hart, Michael N. Weiss, Daniel W. Franks, Lauren J. N. Brent
bioRxiv 2021.12.20.473541; doi: https://doi.org/10.1101/2021.12.20.473541
Digg logo Reddit logo Twitter logo Facebook logo Google logo LinkedIn logo Mendeley logo
Citation Tools
BISoN: A Bayesian Framework for Inference of Social Networks
Jordan D. A. Hart, Michael N. Weiss, Daniel W. Franks, Lauren J. N. Brent
bioRxiv 2021.12.20.473541; doi: https://doi.org/10.1101/2021.12.20.473541

Citation Manager Formats

  • BibTeX
  • Bookends
  • EasyBib
  • EndNote (tagged)
  • EndNote 8 (xml)
  • Medlars
  • Mendeley
  • Papers
  • RefWorks Tagged
  • Ref Manager
  • RIS
  • Zotero
  • Tweet Widget
  • Facebook Like
  • Google Plus One
Subject Areas
All Articles
  • Animal Behavior and Cognition (3607)
  • Biochemistry (7581)
  • Bioengineering (5529)
  • Bioinformatics (20809)
  • Biophysics (10338)
  • Cancer Biology (7988)
  • Cell Biology (11647)
  • Clinical Trials (138)
  • Developmental Biology (6611)
  • Ecology (10217)
  • Epidemiology (2065)
  • Evolutionary Biology (13630)
  • Genetics (9550)
  • Genomics (12854)
  • Immunology (7925)
  • Microbiology (19555)
  • Molecular Biology (7668)
  • Neuroscience (42147)
  • Paleontology (308)
  • Pathology (1258)
  • Pharmacology and Toxicology (2203)
  • Physiology (3269)
  • Plant Biology (7051)
  • Scientific Communication and Education (1294)
  • Synthetic Biology (1952)
  • Systems Biology (5429)
  • Zoology (1119)