# Statistical Inference in Political Networks Research

## TL;DR

This paper reviews key statistical methods for analyzing political networks, covering their forms, applications, and computational aspects, and offers guidelines for choosing appropriate techniques.

## Contribution

It provides a comprehensive overview of inferential network analysis methods specifically tailored for political science research, highlighting their applications and computational considerations.

## Key findings

- Summarizes prominent methods like ERGMs, latent space models, and stochastic actor models.
- Identifies major applications of these methods in political science.
- Provides guidelines for method selection based on research context.

## Abstract

Researchers interested in statistically modeling network data have a well-established and quickly growing set of approaches from which to choose. Several of these methods have been regularly applied in research on political networks, while others have yet to permeate the field. Here, we review the most prominent methods of inferential network analysis---both for cross-sectionally and longitudinally observed networks including (temporal) exponential random graph models, latent space models, the quadratic assignment procedure, and stochastic actor oriented models. For each method, we summarize its analytic form, identify prominent published applications in political science and discuss computational considerations. We conclude with a set of guidelines for selecting a method for a given application.