Overview
This course is taught by Filip Agneessens and runs for four and a half days (20 hours), starting May 20 (Monday) and ending May 24 (Fridaymorning). It is a more technical and in-depth workshop than the Introductory workshop (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 have previously 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 Monday May 20 till Thursday May 24 at 9:30-11:45 ET, 12:30-14:15 ET, and 14:30-15:00pm. On Friday morning May 24, we meet from 9:30 to 11:45am ET. At the end of each day participants will receive homework, which they can perform in small groups of 2-3 with the support of TAs. These focus both on running analyses and interpreting results.
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):
Monday. May 20
§ 9:30-11:45am: Basic R and importing network data in R (Chapter 5)
§ 12:30-14:15pm: Network visualization with R (Chapter 7)
§ 14:30-15:00pm: Exercises on visualization (TA)
Tuesday, May 21
§ 9:30-11:45am: Centrality measures - part 1 (Chapter 9)
§ 12:30-14:15pm: Centrality measures - part 2 (Chapter 9)
§ 14:30-15:00pm: Exercises on centrality (TA)
Wednesday, May 22
§ 9:30-11:45am: Local node-level measures (Chapter 8)
§ 12:30-14:15pm: Group-level measures (Chapter 10)
§ 14:30-15:00pm: Exercises on node- and group-level measures (TA)
Thursday, May 23
§ 9:30-11:45am: Subgroups and community detection (Chapter 11) and two-mode network analysis (Chapter 13)
§ 12:30-14:15pm: Equivalence and basic principles of blockmodeling (Chapter 12)
§ 14:30-15:00pm: Exercises (TA)
Friday, May 24
§ 9:30-11:45am: Summarizing, survey data collection, levels of analysis (Chapter 14; Agneessens, 2020) and Q&A session
Software
We will be using a number of packages in R to perform specific analysis. Prior 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
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.
Agneessens, F., & Labianca, G. J. (2022). Collecting survey-based social network information in work organizations. Social Networks, 68, 31-47. https://doi.org/10.1016/j.socnet.2021.04.003
Instructor contact information
Filip Agneessens: <Filip.Agneessens@unitn.it>