Intermediate SNA (R)

Overview

This course is taught by Filip Agneessens and runs with twice daily sessions for one week (June 21-25) for a total of 20 contact hours. It is a more technical and in-depth workshop than the Introductory workshop, but covers many of the same concepts (see the Schedule below). It focuses on the concepts and methods of SNA, particularly as they apply to specific research objectives. In this course, everything is related back to the research questions -- how the network analysis relates to consequences of interest (see also, Agneessens, 2020). In addition, the mathematics and algorithms behind the measures and techniques is explained. Prior familiarity with some basic concepts in social network analysis is assumed.

Different social network packages in R will be used. However, no prior knowledge of R is required. Please ensure that you downloaded a recent version of R and RStudio and also ensure that you are able to download R packages such as "igraph". Visit our software page long in advance of the workshop for details.

The course meets daily from 10:00-12:00 (EDT) and 12:30-2:30 (EDT) starting Monday, June 21 through Friday, June 25. At the end of each day, participants will receive homework, which includes running analyses and interpreting results, and which they can perform in small groups of 2-3. These results will then be discussed in the next meeting.

Please note this workshop will not be recorded.

Schedule

Monday. June 21

§ AM: Basic R and importing network data in R

§ PM: Network visualization with R

Tuesday, June 22

§ AM: Measures of position: Local measures

§ PM: Measures of position: Centrality

Wednesday, June 23

§ AM: Different measures of cohesion

§ PM: Reciprocity, transitivity, triadic closure and homophily

Thursday, June 24

§ AM: Subgroups and community detection

§ PM: Analysis of multiple groups

Friday, June 25

§ AM: Two-mode network data

§ PM: Recap

Software

  • We will be using a number of packages in R to perform specific analysis. Familiarity with R is not required.

  • For your own convenience, it might be helpful if you have two monitors (or two pcs) available, so you are simultaneously able to see the programs and “attend” class).

Readings

  • Agneessens, F. (2020). Dyadic, nodal and group-level approaches to study the antecedents and consequences of networks: Which social network models to use and when. In The Oxford Handbook of Social Networks. Oxford University Press.

  • Agneessens, F., Borgatti, S. P., & Everett, M. G. (2017). Geodesic based centrality: Unifying the local and the global. Social Networks, 49, 12-26.

  • Opsahl, T., Agneessens, F., & Skvoretz, J. (2010). Node centrality in weighted networks: Generalizing degree and shortest paths. Social Networks, 32(3), 245-251.

TA contact information