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Computational Network Analysis

InstructorClaudia Müller-Birn
Credit Points5
Number of Places15
RoomSeminarraum 051 (Takustr. 9)
StartFeb 22, 2016 | 10:00 AM
endMar 11, 2016 | 02:00 PM


Student Profile

Studierende am Ende ihres Bachelorstudium (> 4 Semester) und Studierende im Masterstudiengang Informatik oder aus verwandten Disziplinen (Mathematik, Bioinformatik) 


The World Wide Web bases on formally defined languages and protocols. But only human activities on the Web generate added value. Such activities are, for example, the creation of Web pages, or the use of applications, such as blog platforms, social networking services (e.g., Facebook and Twitter) and Wikipedia. The value added by these human activities is generated by the interrelationship of content that is created and used by millions of individuals and organizations, as well as the underlying technology. The people that form this added value can be described as communities that evolve over time. These communities can be modeled as networks and their evolution depending on the technology employed can be analyzed.

In this course, you will learn central concepts and approaches of network analysis by discussing existing research findings in this area. Students will learn to analyze data collected from the Web by defining social or information networks. The practical application of your insights takes place within a separate class project in the exercise lesson.

We will deal with the following topics (not exhaustive list):

  • Basic network measures (e.g. centrality),
  • Network models (random, scale-free),
  • Network structures (e.g., bow-tie structure of the Web),
  • Community detection, modularity and overlapping communities,
  • Dissemination of information in networks, 
  • Analysis of temporal networks, and
  • Visualization of networks.

Within the class project, students can select from different topics that are provided by the lecturer. This year, the data sets are amongst others from Wikidata (structured data community), OpenStreetMap (geographic data community), and Genius (collaborative annotation community). In each class, students have to apply their insights on these or exemplary data sets. 

Furthermore, we will primarily use the programming language and software environment for statistical computing GNU R with their different network libraries. One part in each class will be used to teach the basics of data analysis and visualization with GNU.


  • Easley, David, Kleinberg, Jon: Network, crowds, and markets. Cambridge, 2010.
  • Newman, Mark: Networks: An Introduction. Oxford University Press, 2010.