期刊
BIOLOGICAL REVIEWS
卷 96, 期 6, 页码 2716-2734出版社
WILEY
DOI: 10.1111/brv.12775
关键词
agent-based model; animal sociality; configuration model; permutation; randomization; social network analysis
类别
资金
- National Science Foundation [DBI-1300426]
- University of Tennessee, Knoxville
- NSF [2015662]
- Division Of Integrative Organismal Systems
- Direct For Biological Sciences [2015662] Funding Source: National Science Foundation
Analyzing social networks is challenging and requires non-standard statistical methods. Generating effective reference models involves four approaches, including permutation, resampling, sampling from a distribution, and generative models. Researchers need to be aware of potential pitfalls to avoid.
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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