This is the main course website for the seminar Social Network Analysis in winter term 2024/25 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).
News¶
Jump to the schedule to access explanations and materials!
- 13. January 2025: Your poster draft needs to be ready – we will have a preliminary poster presentation in this session!
- 18. November 2024: Be ready to talk about your research projects in this session!
- 11. November 2024: Groups will be the topic of this session!
- 04. November 2024: Please prepare the topics of attributes in networks for this session!
- 28. October 2024: In this session, we will be talking about centrality metrics
- 21. October 2024: For this session, prepare the bits on reading in network data and “Visualizing bipartite networks”!
- 14. October 2024: Seminar starts and introductory topic is online! See the schedule for further information. Make sure to prepare next weeks session before class!
Specialty for 2024/25¶
This winter term, the seminar is conducted in cooperation with Prof. Dr. Mario Larch who is a professor of Empirical Economics. Hence, we will discuss the examples along the lines of economical interpretation.
To prepare the sessions, please use this dataset for your analysis!
Syllabus¶
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/results-and-posters-of-the-seminar-social-network-analysis.html
Contents¶
Getting Started¶
| Title | Video | Source-Code | |
|---|---|---|---|
| Collecting Network Data |
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| Reading in Network Data |
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Bipartite Networks¶
| Title | Video | Source-Code | |
|---|---|---|---|
| Visualize bipartite networks |
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| Project bipartite networks |
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Metrics¶
| Title | Video | Source-Code | |
|---|---|---|---|
| Obtain centrality metrics |
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Attributed Networks¶
| Title | Video | Source-Code | |
|---|---|---|---|
| Edge attributes |
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| Vertex attributes |
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| Filter networks based on attributes |
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Groups¶
| Title | Video | Source-Code | |
|---|---|---|---|
| Components in networks |
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Hands-on SNA¶
| Title | Source-Code | Material | |
|---|---|---|---|
| Analyze a real-world social network dataset |
|
Legend:
| Find a video here | |
| Find code material here | |
| Find external material here |
References¶
- Marina Hennig, Ulrik Brandes, Jürgen Pfeffer, and Ines Mergel. Studying social networks: A guide to empirical research. Campus Verlag, 2012.