Estimating uncertainty and reliability of social network data using Bayesian inference

Farine, Dan; Strandburg‐Peshkin, Ariana

doi:10.1098/rsos.150367

Cited by 68 publications

(85 citation statements)

References 35 publications

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“…Even with 50% of edges, cases or nodes missing, the k-test often achieved higher power than degree-based tests with complete data. Missing edges had less impact on power than other types of missing data, which is reassuring given that interaction data are often the most under-sampled type of data in practice [32,55]. For the k-test, missing cases resulted in a greater reduction in power than missing nodes.…”

Section: Discussionmentioning

confidence: 83%

Evaluating empirical contact networks as potential transmission pathways for infectious diseases

VanderWaal

Enns

Picasso

et al. 2016

J. R. Soc. Interface.

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Networks are often used to incorporate heterogeneity in contact patterns in mathematical models of pathogen spread. However, few tools exist to evaluate whether potential transmission pathways in a population are adequately represented by an observed contact network. Here, we describe a novel permutation-based approach, the network k-test, to determine whether the pattern of cases within the observed contact network are likely to have resulted from transmission processes in the network, indicating that the network represents potential transmission pathways between nodes. Using simulated data of pathogen spread, we compare the power of this approach to other commonly used analytical methods. We test the robustness of this technique across common sampling constraints, including undetected cases, unobserved individuals and missing interaction data. We also demonstrate the application of this technique in two case studies of livestock and wildlife networks. We show that the power of the k-test to correctly identify the epidemiologic relevance of contact networks is substantially greater than other methods, even when 50% of contact or case data are missing. We further demonstrate that the impact of missing data on network analysis depends on the structure of the network and the type of missing data.

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Section: Discussionmentioning

confidence: 83%

Evaluating empirical contact networks as potential transmission pathways for infectious diseases

VanderWaal

Enns

Picasso

et al. 2016

J. R. Soc. Interface.

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“…Indeed, there is increasing awareness of the need to estimate uncertainty of social data (e.g. Farine & Strandburg‐Peshkin, ; Lusseau et al., ), including in the study of dominance hierarchies (Adams, ). Yet, uncertainty is seldom measured when analysing dominance hierarchies (but see, e.g.…”

Section: Discussionmentioning

confidence: 99%

“…There is increasing awareness of the need to estimate uncertainty of social data (e.g. Farine & Strandburg‐Peshkin, ; Lusseau, Whitehead, & Gero, ). More than a decade ago Adams () proposed a Bayesian approach to estimate hierarchy uncertainty, however, behavioural and evolutionary ecologists have not yet broadly adopted Bayesian procedures, possibly due to their apparent complexity.…”

Section: Introductionmentioning

confidence: 99%

A practical guide for inferring reliable dominance hierarchies and estimating their uncertainty

Sánchez‐Tójar

Schroeder

Farine

2017

Journal of Animal Ecology

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142

182

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Many animal social structures are organized hierarchically, with some individuals monopolizing resources. Dominance hierarchies have received great attention from behavioural and evolutionary ecologists. There are many methods for inferring hierarchies from social interactions. Yet, there are no clear guidelines about how many observed dominance interactions (i.e. sampling effort) are necessary for inferring reliable dominance hierarchies, nor are there any established tools for quantifying their uncertainty. We simulate interactions (winners and losers) in scenarios of varying steepness (the probability that a dominant defeats a subordinate based on their difference in rank). Using these data, we (1) quantify how the number of interactions recorded and the steepness of the hierarchy affect the performance of five methods for inferring hierarchies, (2) propose an amendment that improves the performance of a popular method, and (3) suggest two easy procedures to measure uncertainty and steepness in the inferred hierarchy. We find that the ratio of interactions to individuals required to infer reliable hierarchies is surprisingly low, but depends on the steepness of the hierarchy and the method used. We show that David's score and our novel randomized Elo-rating are the best methods when hierarchies are not extremely steep, where the original Elo-rating, the I&SI and the recently described ADAGIO perform less well. In addition, we show that two simple methods can be used to estimate uncertainty at the individual and group level, and that the randomized Elo-rating repeatability provides researchers with a standardized measure valid for comparing the steepness of different hierarchies. We provide several worked examples to guide researchers interested in studying dominance hierarchies. Methods for inferring dominance hierarchies are relatively robust. We recommend that a ratio of observed interactions to individuals of at least 10 (for steep hierarchies), and ideally 20 serves as a good benchmark. Our simple procedures for estimating uncertainty in the observed data will facilitate evaluating whether sufficient data have been collected, while plotting the shape of the hierarchy will provide new insights into the social structure of the study organism.

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“…For example, the choice of how to quantify the relationship between individuals (e.g. the association index) can influence the distribution of edge weights (Cairns & Schwager ; Farine & Strandburg‐Peshkin ). However, because the null model will construct the null networks in exactly the same way (including using the same association index), the choice of index should not interact with the choice of null model.…”

Section: Introductionmentioning

confidence: 99%

A guide to null models for animal social network analysis

2017

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Summary Null models are an important component of the social network analysis toolbox. However, their use in hypothesis testing is still not widespread. Furthermore, several different approaches for constructing null models exist, each with their relative strengths and weaknesses, and often testing different hypotheses.In this study, I highlight why null models are important for robust hypothesis testing in studies of animal social networks. Using simulated data containing a known observation bias, I test how different statistical tests and null models perform if such a bias was unknown.I show that permutations of the raw observational (or ‘pre‐network’) data consistently account for underlying structure in the generated social network, and thus can reduce both type I and type II error rates. However, permutations of pre‐network data remain relatively uncommon in animal social network analysis because they are challenging to implement for certain data types, particularly those from focal follows and GPS tracking.I explain simple routines that can easily be implemented across different types of data, and supply R code that applies each type of null model to the same simulated dataset. The R code can easily be modified to test hypotheses with empirical data. Widespread use of pre‐network data permutation methods will benefit researchers by facilitating robust hypothesis testing.

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Estimating uncertainty and reliability of social network data using Bayesian inference

Cited by 68 publications

References 35 publications

Evaluating empirical contact networks as potential transmission pathways for infectious diseases

Evaluating empirical contact networks as potential transmission pathways for infectious diseases

A practical guide for inferring reliable dominance hierarchies and estimating their uncertainty

A guide to null models for animal social network analysis

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