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Seminar Social Network Analysis

Contents

  • Specialty for this semester
  • Syllabus
  • Contents
  • References

This is the main course website for the seminar Social Network Analysis 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.

Specialty for this semester¶

This 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¶

Section Title
Getting Started Collecting Network Data
Getting Started Reading in Network Data
Bipartite Networks Visualize bipartite networks
Bipartite Networks Project bipartite networks
Metrics Obtain centrality metrics
Attributed Networks Edge attributes
Attributed Networks Vertex attributes
Attributed Networks Filter networks based on attributes
Groups Components in networks
Hands-on SNA 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.

  • « Lecture: Data Modeling and Knowledge Generation (Winter 2026/27)
  • Lecture "Bits, Bytes, and Beyond: Foundations of Computer Science" (Winter 2026/27) »

Published

15. Oct, 2026

Last Updated

Oct 15, 2026

Tags

  • lecturenotes 12
  • teaching 23
  • ubt 19

Links

  • elearning@ubt
  • cmlife@ubt
  • recordings
  • intro to R for newbies
  • posters of previous semesters

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