Abstract
Social network analysis is a suite of approaches for exploring relational data. Two approaches commonly used to analyze animal social network data are permutationbased tests of significance and exponential random graph models. However, the performance of these approaches when analyzing different types of network data has not been simultaneously evaluated. Here we test both approaches to determine their performance when analyzing a range of biologically realistic simulated animal social networks. We examined the false positive and false negative error rate of an effect of a twolevel explanatory variable (e.g., sex) on the number and combined strength of an individual's network connections. We measured error rates for two types of simulated data collection methods in a range of network structures, and with/without a confounding effect and missing observations. Both methods performed consistently well in networks of dyadic interactions, and worse on networks constructed using observations of individuals in groups. Exponential random graph models had a marginally lower rate of false positives than permutations in most cases. Phenotypic assortativity had a large influence on the false positive rate, and a smaller effect on the false negative rate for both methods in all network types. Aspects of within and betweengroup network structure influenced error rates, but not to the same extent. In "grouping eventbased" networks, increased sampling effort marginally decreased rates of false negatives, but increased rates of false positives for both analysis methods. These results provide guidelines for biologists analyzing and interpreting their own network data using these methods.
Original language  English 

Pages (fromto)  12661276 
Number of pages  11 
Journal  Behavioral Ecology 
Volume  31 
Issue number  5 
DOIs  
Publication status  Published  12 Sep 2020 
Keywords
 Exponential random graph model
 Permutation
 Randomization
 Social network analysis
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The performance of permutations and exponential random graph models when analysing animal networks (R code and data)
Silk, M. (Creator), Evans, J. (Creator) & Fisher, D. (Creator), DRYAD, 2020
DOI: 10.5061/dryad.9w0vt4bcn, http://datadryad.org/stash/dataset/doi:10.5061/dryad.9w0vt4bcn
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