RT Journal Article
SR Electronic
T1 BISoN: A Bayesian Framework for Inference of Social Networks
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 2021.12.20.473541
DO 10.1101/2021.12.20.473541
A1 Jordan D. A. Hart
A1 Michael N. Weiss
A1 Daniel W. Franks
A1 Lauren J. N. Brent
YR 2022
UL http://biorxiv.org/content/early/2022/06/01/2021.12.20.473541.abstract
AB 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.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.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.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 StatementThe authors have declared no competing interest.