The Chair of Machine Learning for Complex Networks adresses new data science and machine learning techniques for complex systems that can be modelled as graphs or networks. We further use network science techniques to study open questions in collaborative software engineering, online information systems and computational social science. Our approach is quantitative, data-driven and interdisciplinary, combining methods from computer science, network science, mathematics and physics.
Apart from statistical techniques to infer network models from uncertain data, a current focus of our chair is the use of higher-order graph models to better understand causal structures in time series data on complex systems. This novel direction of research in network science has major implications for our understanding of complex systems, both in terms of theoretical foundations as well as in terms of machine learning methods. A summary of our approach to tackle this issue has been published in Nature Physics.
Our Chair has an international and interdisciplinary focus. If you are interested to collaborate with us or if you are interested in a visit please have a look at current opportunities.