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A guide to choosing and implementing reference models for social network analysis
Author(s) -
Hobson Elizabeth A.,
Silk Matthew J.,
Fefferman Nina H.,
Larremore Daniel B.,
Rombach Puck,
Shai Saray,
PinterWollman Noa
Publication year - 2021
Publication title -
biological reviews
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 4.993
H-Index - 165
eISSN - 1469-185X
pISSN - 1464-7931
DOI - 10.1111/brv.12775
Subject(s) - resampling , computer science , key (lock) , variety (cybernetics) , social network analysis , data mining , data science , social network (sociolinguistics) , machine learning , permutation (music) , null distribution , reference data , reference model , artificial intelligence , statistical hypothesis testing , statistics , test statistic , mathematics , physics , computer security , world wide web , acoustics , social media , software engineering
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.