Moving Beyond Descriptives: Basic Network Statistics with statnet

Lorien Jasny & Ryan Acton
University of California, Davis
ljasny@ucdavis.edu

LIMITED to 30 Seats

This workshop session will serve as an introduction to the use of basic statistical methods for network analysis within the R/statnet platform. The session will focus on permutation tests for marginal relationships between node or graph-level indices and covariates in both one and two mode networks; network correlation and regression; and exploratory multivariate analysis of multi-network data sets. Participants will gain an understanding of the mechanics of these tests, the hypotheses they test, and how to use the functions in the SNA package. Attendees are expected to have had some prior exposure to R, but extensive experience is not assumed. Completion of the “Introduction to Network Analysis with R and statnet” workshop session is suggested (but not required) as preparation for this session. Familiarity with the basic concepts of descriptive network analysis (e.g., centrality scores, network visualization) is strongly recommended.

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 network models, network dynamics, and missing data.