The World Wide Web is based on formally defined languages and protocols, but only human activities in the Web such as the creation of Web pages, the use of applications, such as blog platforms, social networking services (e.g., Facebook and Twitter) and Wikipedia, generate added value. This added value is based on the interrelationship of content that is created and used by millions of individuals and organizations, as well as the underlying technology. 

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

We will deal with the following topics:

• 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, and
• Analysis of temporal networks.

Within the class project, students have to select a topic from the field of complex networks and discuss existing research based on a selection of two to three research articles. In each class, students have to apply their insights on exemplary data sets. We will primarily use the programming language and software environment for statistical computing R with their different network libraries. This year it is planned to incorporate the Python software package NetworkX as well.

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

Newman, Mark: Networks: An Introduction. Oxford University Press, 2010.
Dorogovtsev, Sergey: Lectures on Complex Networks, Oxford University Press, 2010.
Easley, David, Kleinberg, Jon: Network, crowds, and markets. Cambridge, 2010.