As mentioned in a previous post, Alex Hanna and I had the opportunity to teach last week at the Higher School of Economic’s International Social Network Analysis Summer School in St. Petersburg. While last year’s workshop emphasized smaller social networks, this year’s workshop focused on online networks. For my part, I provided an introductory lecture to social network analysis along with four labs on the subject of R and social network analysis.
The introduction to social network analysis began with an historical overview, followed by outlining which concepts constitute a social network. The remaining portions review subjects relating to subgraphs, walks, centrality, cohesive subgroups, along with major research subjects in the field. Setting aside the substantive interest in networks, the first lab covered basic R usage, objects, and syntax. Admittedly, this material was relatively dryer, though necessary to make the most of the network analysis software in R. We followed this introduction to R with an introduction to R’s social network analysis software. This second lab introduces the class to the different network packages within R, reading data, basic measurements brought up in the introductory lecture, and visualization. The third R SNA lab was on the subject of graph-level indices, random graphs, and Conditional Uniform Graph tests. Both the second and third labs were conducted primarily using the igraph package. The fourth and final lab of the course was on the subject of exponential random graph modeling. For this lab, we walked through tests for homophily and edgewise-shared partner effects using data on both our Twitter hashtag (#SNASPb2013) as well as US political blogs.
The slides include scripts that download and read the data used within all lab examples.
I’ve hosted PDFs of all the slides on Google Drive.