Networks matter! This holds for technical infrastructures like communication or transportation networks, for
information systems and social media in the World Wide Web, but also for various social, economic and biological
systems. What can we learn from data that capture the interaction topology of such complex systems? What is
the role of individual nodes and how can we discover significant patterns in the structure of networks? How do
these structures influence dynamical process like diffusion or the spreading of epidemics? Which are the most influential
actors in a social network? And how can we analyse time series data on systems with dynamic network topologies?
Deepening the understanding of the theoretical concepts covered in the lecture Statistical Network Analysis, in
this lab we will develop our own library of network analysis techniques based on the python package pathpy, which is
developed at our Chair. The course material consists of the script of the course Statistical Network Analysis, jupyter
notebooks as well as network data sets that will be used for statistical analyses. The successful completion of the
course requires to complete a final group project.