Valued Network Modeling with statnet

Pavel Krivitsky & Carter Butts
Penn State University

LIMITED to 30 Seats

This workshop session provides a tutorial using statnet software ‘ particularly ergm and latentnet ‘ to model social networks whose ties have weights (e.g., counts of interactions) or are ranks (i.e., each actor ranks the others according to some criterion), using latent space models and exponential-family random graph models (ERGMs) generalized to valued ties, and emphasizing a hands-on approach to fitting these models to empirical data.

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, while latent space models postulate an unobserved social space in which actors are embedded, facilitating principled visualization and group detection. Topics covered within this session include: importing, modifying, and exporting edge values on network objects; an overview of the valued ERGM framework and the notion of reference distribution; an overview of latent space models for social networks; defining and fitting models to empirical data, including ERGM terms meaningful for counts and ranks; interpretation of model coefficients; simulation of networks using these models; and ERGM degeneracy assessment.

Prerequisites: Attendees are expected to have had some prior exposure to R, but extensive experience is not assumed. Familiarity with binary ERG modeling with the R/statnet platform (e.g., from the “Exponential-family Random Graph (ERG) Modeling with statnet” workshop session) is assumed.

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