Networks matter! This holds for technical infrastructures like the Internet, 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 the topology of such complex networked 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? Which are the most influential actors in a social network? And how can we analyse time series data on dynamic networks?
In this course, students get an introduction to statistical modelling and analysis techniques that are needed to quantitatively study complex networked systems. The course will show how such systems can be represented mathematically and how patterns in their topology can be characterised quantitatively. Students will understand how networks shape dynamical processes and how complex topologies emerge from simple growth processes.
The course combines theory lecture, which introduce theoretical foundations of statistical network analysis, with practice lectures that show how we can practically apply network analysis in python. The accompanying exercises consist of computer simulations and real-world data analysis tasks that can be solved using the python package pathpy.
- Dozent*in: Ingo Scholtes