A guide to choosing and implementing reference models for social network analysis |
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Authors: | Elizabeth A Hobson Matthew J Silk Nina H Fefferman Daniel B Larremore Puck Rombach Saray Shai Noa Pinter-Wollman |
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Institution: | 1. Department of Biological Sciences, University of Cincinnati, 318 College Drive, Cincinnati, OH, 45221 U.S.A.;2. Centre for Ecology and Conservation, University of Exeter Penryn Campus, Treliever Road, Penryn, Cornwall, TR10 9FE U.K.;3. Departments of Ecology and Evolutionary Biology & Mathematics, University of Tennessee, 569 Dabney Hall, Knoxville, TN, 37996 U.S.A.;4. Department of Computer Science, University of Colorado Boulder, 1111 Engineering Drive, Boulder, CO, 80309 U.S.A.;5. Department of Mathematics & Statistics, University of Vermont, 82 University Place, Burlington, VT, 05405 U.S.A.;6. Department of Mathematics and Computer Science, Wesleyan University, Science Tower 655, 265 Church Street, Middletown, CT, 06459 U.S.A.;7. Department of Ecology and Evolutionary Biology, University of California, Los Angeles, 612 Charles E. Young Drive South, Los Angeles, CA, 90095 U.S.A. |
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Abstract: | Analysing social networks is challenging. Key features of relational data require the use of non-standard statistical methods such as developing system-specific null, or reference, models that randomize one or more components of the observed data. Here we review a variety of randomization procedures that generate reference models for social network analysis. Reference models provide an expectation for hypothesis testing when analysing network data. We outline the key stages in producing an effective reference model and detail four approaches for generating reference distributions: permutation, resampling, sampling from a distribution, and generative models. We highlight when each type of approach would be appropriate and note potential pitfalls for researchers to avoid. Throughout, we illustrate our points with examples from a simulated social system. Our aim is to provide social network researchers with a deeper understanding of analytical approaches to enhance their confidence when tailoring reference models to specific research questions. |
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Keywords: | agent-based model animal sociality configuration model permutation randomization social network analysis |
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