Exponential-family Random Graph Modeling (ERGMs) with statnet

Statnet Development Team
U of Washington, UC Irvine, Penn State, UCLA

LIMITED to 30 Seats (This Workshop is already fully booked)

This workshop provides a introductory tutorial on exponential-family random graph models (ERGMs) for social networks, emphasizing a hands-on approach to fitting these models to empirical data. This session will provide a hands-on tutorial to ERGM modeling within the R/statnet platform. The ERGM framework allows for the parametrization, fitting, and simulation from models that incorporate the complex dependencies within relational data structures, and provides an extremely general and flexible means of representing them.

Topics covered within this session include: an overview of the ERGM framework; defining and fitting models to empirical data; interpretation of model coefficients; goodness-of-fit and model adequacy checking; simulation of networks using ERG models; degeneracy assessment; and modeling and simulation of complete networks from egocentrically sampled data. Attendees are expected to have had some prior exposure to R, but extensive experience is not assumed.

Prerequisites: Familiarity with basic descriptive network concepts and statistical methods for network analysis within the R/statnet platform is recommended. Attendees are expected to have had some prior exposure to R, but extensive experience is not assumed. The workshops “Introduction to Network Analysis with R and statnet” and “Basic Network Statistics with statnet” are recommended as preparation.

statnet is a collection of packages for the R statistical computing environment that supports the representation, manipulation, visualization, modeling, simulation, and analysis of network data. statnet packages are contributed by a team of volunteer developers, and are made freely available under the GNU Public License. statnet packages can be used with any computing platform that supports R (including Windows, Linux, and Mac), and they support statistical analysis of large networks, longitudinal network dynamics, and missing data.