BERGM WEBSITE: http://acaimo.github.io/Bergm
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 (e.g.,  and ).
This hands-on 3-hour workshop will provide participants with the opportunity to acquire essential knowledge of the main characteristics of Bayesian ERGMs using the latest version of the Bergm package for R .
The workshop will have a strong focus on the practical implementation of the package functions through the analysis of real network data.
Interactive material will support the acquisition of concepts and understanding of the tutorial through code, scripts, and documentation.
- Introduction to the Bayesian ERGMs;
- Prior specification;
- Model selection and estimation;
- Interpretation of model and parameter posterior estimates;
- Missing data imputation;
- Model assessment via goodness-of-fit procedures.
PREREQUISITES: Basic knowledge of exponential random graph models and R.
 Caimo, A. and Friel, N. (2011). Bayesian Inference for Exponential Random Graph Models, Social Networks, 33(1), 41-55.
 Bouranis, L., Friel, N., and Maire, F. (2017). Efficient Bayesian inference for exponential random graph models by correcting the pseudo-posterior distribution. Social Networks, 50, 98-108.
 Caimo, A. and Friel, N. (2014). Bergm: Bayesian Exponential Random Graphs in R, Journal of Statistical Software, 61(2), 1-25. jstatsoft.org/v61/i02