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 influen- tial actors in a social network? And how can we analyse time series data on systems with dynamic network topologies?

This course will equip participants with statistical analysis techniques that are needed to answer such questions based on network data across different disciplines. The course will show how networked systems can be modelled and how patterns in their topology can be characterised quantitatively. Students will understand how the topology of networks shapes dynamical processes, how statistical characteristics influence the robustness of systems, and how complex macroscopic features emerge from simple processes.

The course combines a series of lectures – which introduce theoretical concepts in statistical network analysis – with weekly exercises that show how we can apply them in practical network analysis tasks. The course material consists of annotated slides for lectures, lecture videos, and jupyter notebooks. Students can apply and deepen their knowledge through weekly exercise sheets. The successful completion of the course requires to pass a final written exam.