ArticleBehavioral ecology and sociobiology2022
Common permutation methods in animal social network analysis do not control for non-independence.
Article in Behavioral ecology and sociobiology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers.
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28 citing papers in PubMed, 63 citations in OpenAlex.
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- A causal framework for the drivers of animal social network structure.PLoS computational biology · 2025Article
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- Individual and group level health factors influence social networks of dairy calves.Scientific reports · 2025Article
- Animal social networks are robust to changing association definitions.Behavioral ecology and sociobiology · 2025Article
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- The adaptive value of density-dependent habitat specialization and social network centrality.Nature communications · 2024Article
- Social and genetic connectivity despite ecological variation in a killer whale network.Proceedings. Biological sciences · 2024Article
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- Spatiotemporal-social association predicts immunological similarity in rewilded mice.Science advances · 2023Article
- Effect of behavioural sampling methods on local and global social network metrics: a case-study of three macaque species.Royal Society open science · 2023Article
- BISoN: A Bayesian Framework for Inference of Social Networks.Methods in ecology and evolution · 2023Article
- The heterogeneous herd: Drivers of close-contact variation in African buffalo and implications for pathogen invasion.Ecology and evolution · 2023Article
- Agent-based null models for examining experimental social interaction networks.Scientific reports · 2023Article
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4 authors at 2 institutions in 1 country.
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Abstract
The non-independence of social network data is a cause for concern among behavioural ecologists conducting social network analysis. This has led to the adoption of several permutation-based methods for testing common hypotheses. One of the most common types of analysis is nodal regression, where the relationships between node-level network metrics and nodal covariates are analysed using a permutation technique known as node-label permutations. We show that, contrary to accepted wisdom, node-label permutations do not automatically account for the non-independences assumed to exist in network data, because regression-based permutation tests still assume exchangeability of residuals. The same assumption also applies to the quadratic assignment procedure (QAP), a permutation-based method often used for conducting dyadic regression. We highlight that node-label permutations produce the same Supplementary Information: The online version contains supplementary material available at 10.1007/s00265-022-03254-x.
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