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• W2189943963 startingPage "150367" @default.
• W2189943963 abstract "Social network analysis provides a useful lens through which to view the structure of animal societies, and as a result its use is increasingly widespread. One challenge that many studies of animal social networks face is dealing with limited sample sizes, which introduces the potential for a high level of uncertainty in estimating the rates of association or interaction between individuals. We present a method based on Bayesian inference to incorporate uncertainty into network analyses. We test the reliability of this method at capturing both local and global properties of simulated networks, and compare it to a recently suggested method based on bootstrapping. Our results suggest that Bayesian inference can provide useful information about the underlying certainty in an observed network. When networks are well sampled, observed networks approach the real underlying social structure. However, when sampling is sparse, Bayesian inferred networks can provide realistic uncertainty estimates around edge weights. We also suggest a potential method for estimating the reliability of an observed network given the amount of sampling performed. This paper highlights how relatively simple procedures can be used to estimate uncertainty and reliability in studies using animal social network analysis." @default.
• W2189943963 created "2016-06-24" @default.
• W2189943963 creator A5054479228 @default.
• W2189943963 creator A5057607906 @default.
• W2189943963 date "2015-09-01" @default.
• W2189943963 modified "2023-10-13" @default.
• W2189943963 title "Estimating uncertainty and reliability of social network data using Bayesian inference" @default.
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• W2189943963 doi "https://doi.org/10.1098/rsos.150367" @default.
• W2189943963 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/4593693" @default.
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• W2189943963 hasPublicationYear "2015" @default.
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