This course is taught by Filip Agneessens and Francisco Trincado-Munoz and runs with twice daily sessions across two weeks, with meeting on June 9 (Thursday), June 10 (Friday), June 13 (Monday), June 14 (Tuesday) and June 16 (Thursday) for a total of 22.5 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. The textbook used for this course is "Borgatti, Everett, Johnson and Agneessens (2022) Analyzing Social Networks Using R. Sage".
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.
Meeting times for the course will be 10:00am-12:15 (EDT) and 12:45-3:00pm (EDT) on instructional days. 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.
Below, you will find a tentative schedule (chapter numbers refer to Borgatti, Everett, Johnson and Agneessens, 2022):
Thursday. June 9
§ AM: Basic R and importing network data in R (Chapter 5)
§ PM: Network visualization with R (Chapter 7)
Friday, June 10
§ AM: Local node-level measures (Chapter 8)
§ PM: Centrality measures - part 1 (Chapter 9)
Monday, June 13
§ AM: Centrality measures - part 2 (Chapter 9)
§ PM: Group-level measures (Chapter 10)
Tuesday, June 14
§ AM: Subgroups and community detection (Chapter 11)
§ PM: Equivalence and basic principles of blockmodelling (Chapter 12)
Thursday, June 16
§ AM: Two-mode network data (Chapter 13)
§ PM: Introduction to statistical analysis (Chapters 14 and 15)
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).
Borgatti, S. P., Everett, M. G., Johnson, J. C., & Agneessens, F. (2022). Analyzing Social Networks Using R. Sage.
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.
Instructor contact information
Filip Agneessens: <Filip.Agneessens@unitn.it>
Francisco Trincado <email@example.com>