Bayesian computational algorithms for social network analysis

TL;DR

This paper reviews recent Bayesian computational algorithms for social network analysis, focusing on exponential random graph models and latent space models using R software.

Contribution

It provides an overview of the latest Bayesian methods and algorithms applied to social network models, highlighting their implementation in R.

Findings

Enhanced Bayesian algorithms for ERGMs and LSMs

Improved computational efficiency in social network analysis

Practical R implementations for advanced models

Abstract

In this chapter we review some of the most recent computational advances in the rapidly expanding field of statistical social network analysis using the R open-source software. In particular we will focus on Bayesian estimation for two important families of models: exponential random graph models (ERGMs) and latent space models (LSMs).