Social Network Analysis with R

The course will provide a hands-on tour through the important concepts and methods of Social Network Analysis (SNA). The main goal is to put the participants on a well-lit road towards conducting a typical social-network-analytic project comfortably on their own using R. The focus is on the practical application of key ideas of SNA rather than discussing (social) theories standing behind them. Nonetheless, pointers to the relevant theoretical and applied literature will be provided.

To this end, the course will discuss importing network data from various formats, managing network data within R, basic SNA descriptives (including density, transitivity, homophily/segregation, and centrality), community detection, and creating effective network visualizations. The course will conclude with coverage of the basics of statistical modeling of networks with Exponential-family Random Graph Models (ERGM) and Stochastic Actor-Oriented Models (SAOM).

Course meetings will consist of two parts. The first part will consist of a presentation, demonstration, and discussions on various SNA concepts and methods. The second part will be focused on hands-on training in applying the presented concepts and tools using real data. While the instructors will provide datasets for these exercises, participants are encouraged to bring their own data.