A guide to choosing and implementing reference models for social network analysis

TL;DR

This paper reviews various methods for creating reference models in social network analysis, guiding researchers in selecting appropriate randomization techniques to improve hypothesis testing accuracy.

Contribution

It provides a comprehensive overview of different randomization procedures and key steps for developing effective reference models in social network research.

Findings

Four main approaches: permutation, resampling, distribution sampling, generative models.

Guidelines for choosing suitable methods based on research context.

Illustrative examples from simulated social systems.

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.