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Seminar Social Network Analysis (Winter 2022/23)

Contents

  • Syllabus
  • Contents
    • Getting Started
    • Bipartite Networks
    • Metrics
    • Groups
    • Attributed Networks
    • Hands-on SNA
  • References

This is the main course website for the seminar Social Network Analysis in winter term 2022/23 at University of Bayreuth.

Recordings accompanying the seminar are available online.

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/seminar-social-network-analysis.html

Contents¶

Getting Started¶

Title Video Source-Code
Collecting Network Data
Reading in Network Data

Bipartite Networks¶

Title Video Source-Code
Visualize bipartite networks
Project bipartite networks

Metrics¶

Title Video Source-Code
Obtain centrality metrics

Groups¶

Title Video Source-Code
Components in networks

Attributed Networks¶

Title Video Source-Code
Edge attributes
Vertex attributes

Filter networks based on attributes


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.

  • « Tutorial: Introduction to R for scholars of humanities and social sciences (Winter 2022/23)
  • Lecture: Data Modeling and Knowledge Generation (Winter 2022/23) »

Published

18. Oct, 2022

Last Updated

Dec 2, 2022

Tags

  • lecturenotes 7
  • teaching 17
  • ubt 14

Links

  • elearning@ubt
  • cmlife@ubt
  • recordings

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