Machine Learning for Complex Networks

Statistical Network Analysis

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 global 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 networks with dynamic topologies?

In this course, students get an introduction to statistical modeling and analysis techniques that can be used to study networked systems across disciplines. The course shows how we can represent networks mathematically and we can characterize patterns in their topology quantitatively. Students will understand how networks shape dynamical processes and how complex link topologies emerge from simple network formation processes. The accompanying practice lectures implement key network analysis and machine learning techniques ans show how network science challenges can be solved using python.