Social Network Analysis Workshop at HSE-St. Petersburg

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Last month I had the wonderful opportunity to help instruct an intensive eight-day workshop on the subject of social network analysis. Affiliated with the Sociology of Education and Science Laboratory at the Higher School of Economics—Saint Petersburg, the workshop sought to recreate the atmosphere of ICPSR summer courses. This workshop was the first of its kind in Russia to offer social networks training as a summer methods course.

Each day the course began with lectures provided by Jeroen Bruggeman (Amsterdam University), followed by applied labs in R taught by Professor Bruggeman and myself (HSE–Moscow), a discussion of the course readings led by Nina O’Brien (University of Southern California), and concluded by student research presentations. The workshop was made possible by Valeria Ivaniushina (HSE—St. Petersburg) and Daniel Alexandrov’s (HSE—St. Petersburg) Sociology of Education and Science Laboratory.

Fortunately for non-attendants, the Laboratory has posted the majority of the workshop’s materials online. I’ll leave it to Bad Hessian’s readership to independently peruse through the readings as well as Professor Bruggeman’s lectures and lab slides.

My own contribution was to run the first half of the R labs. The labs began with an introduction to R’s basic functions and concluded with CUG and QAP tests. I designed the labs with the assumption that most students were unfamiliar with both R and the sna package. The outline of the labs is as follows:

  1. Familiarity with R.  Covers R history, basic usage, and data types.
  2. Networks & R.  Continues the previous lab and covers data importation and conversion from edge lists to matrices.
  3. Plotting Networks in R.  Covers the usage of gplot(), incorporating attributes into visualizations, converting data from two-mode to one-mode networks, setting thresholds for valued networks, and saving coordinates.
  4. Graph-Level Indexes.  Reviews global network properties like density, mutuality, dyad censuses, triad censuses, transitivity, network components, and diameter.
  5. Random Network Comparisons. Introduction to creating random networks and baseline effects, CUG and QAP hypothesis tests, centrality, and centralization.