INSTRUCTOR: Alberto Caimo, University College Dublin, Ireland

CRAN: https://CRAN.R-project.org/package=Bergm

WEBSITE: http://acaimo.github.io/Bergm

SUMMARY:

Bayesian analysis is a promising approach to social network analysis because it yields a rich fully probabilistic picture of uncertainty which is essential when dealing with relational data. Using a Bayesian framework for exponential random graph models (ERGMs) leads directly to the inclusion of prior information about the network effects and provides access to the uncertainties by evaluating the posterior distribution of the parameters. The growing interest in Bayesian ERGMs can be attributed to the development of very efficient computational tools developed over the last decade.

This hands-on workshop will provide participants with the opportunity to acquire essential knowledge of the main characteristics of Bayesian ERGMs using the Bergm package for R.

TOPICS:

– Brief overview of ERGMs;

– Intro to Bayesian analysis;

– Prior specification;

– Model fitting and model selection;

– Interpretation of model and parameter posterior estimates;

– Model assessment via goodness-of-fit procedures.

The workshop will have a strong focus on the practical implementation features of the software that will be described by the analysis of real network data.

Interactive material will support the acquisition of concepts and understanding of the tutorial through code, scripts, and documentation.

PREREQUISITES:

Basic knowledge of social network analysis and R. Participants are recommended to bring a laptop with R/RStudio, and Bergm installed.

REFERENCES:

Caimo, A., Bouranis, L., Krause, R., and Friel, N. (2022) “Statistical Network Analysis with Bergm.” Journal of Statistical Software, 104(1), 1–23.