This is the main course website for the seminar Social Network Analysis in winter term 2023/24 at University of Bayreuth.
Recordings accompanying the seminar are available online. In case you are entirely new to programming (in R), please check out the corresponding introductory course (for UBT students).
Jump to the schedule to access explanations and materials!
- 23. November 2023: We revisit groups in network and talk about diffusion processes
- 16. November 2023: Next topic: Groups in networks
- 09. November 2023: Topic of the 9. November: Attributes in networks
- 02. November 2023: To be discussed on 2.11.: Measuring centrality in networks
- 26. October 2023: Now online: turning two-mode networks into one-mode networks
- 19. October 2023: Seminar starts and introductory topic is online! See the schedule for further information. Make sure to prepare next weeks session before class!
In the seminar, students are asked to work on self-selected research questions using methods of network analysis. The methods are new to the students in that they never worked with them before. That's why the seminar consists of some introductory part in which the methods are presented and their applicability using R is discussed. Students then formulate an appropriate research question, find or create a data set, and apply the newly acquired methods to the data set. After successful participation in this seminar, students are able to understand the most important theoretical and methodological principles of social network analysis and to apply these methods to their own research projects.
In the vast majority of cases, students have had no prior exposure to programming. This means that in the seminar they not only learn to apply the methods for network analysis in R, but often teach themselves basic skills in R as well.
An overview over selected results from previous semesters can be found here: https://mircoschoenfeld.de/seminar-social-network-analysis.html
|Collecting Network Data||
|Reading in Network Data||
|Visualize bipartite networks||
|Project bipartite networks||
|Obtain centrality metrics||
|Filter networks based on attributes||
|Components in networks||
|Find a video here|
|Find code material here|
|Find external material here|
- Marina Hennig, Ulrik Brandes, Jürgen Pfeffer, and Ines Mergel. Studying social networks: A guide to empirical research. Campus Verlag, 2012.