Chair for Machine Learning for Complex Networks

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.

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