Natural and artificial networked systems exhibit a high level of complexity, such as simultaneous relationships or interactions, temporal variations, interdependency.

We develop mathematical models to account for this high level of complexity of networked systems. We use higher-order tensors to encode available information and to represent these integrated systems, called multilayer networks.

How multiple types of simultaneous interactions among interconnected units lead to the emergence of complex behavior, such as collective intelligence or phase transitions?

We use multilayer modeling and analysis of complex data to understand structure and dynamics of systems of systems. Our theoretical and computational studies are (mostly) based on dynamical processes (e.g., random walks) to investigate:

Where do we apply multilayer network models?

We have shown that multilayer modeling often provides more insights than more traditional approaches, because they allow to naturally integrate available structural/relational/dynamical information. We successfully apply multilayer modeling and analysis to study: (selection)

With or for Industry

When we do not work to find an answer to fundamental questions, we strictly collaborate with industrial partners on projects or international challenges of high societal impact.

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