A guide to choosing and implementing reference models for social network analysis
Abstract
Analyzing 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 analyzing 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.
Cite
@article{arxiv.2012.04720,
title = {A guide to choosing and implementing reference models for social network analysis},
author = {Elizabeth A. Hobson and Matthew J. Silk and Nina H. Fefferman and Daniel B. Larremore and Puck Rombach and Saray Shai and Noa Pinter-Wollman},
journal= {arXiv preprint arXiv:2012.04720},
year = {2021}
}