Although the field of complex networks has been actively researched for several decades, higher-order networks describing group interactions have just recently gained special attention.

At the expense of the richer description of the interacting components, more complex mathematical tools taken from the field of topological data analysis need to be applied for their study. Despite numerous studies in this field, the structural dynamics and evolution of higher-order networks are still not well understood as of today.

The goal of the proposed research project is to build topological models to detect and predict the structural dynamics of real-world higher-order networks. Among others, these models could shed light on the evolution of neural networks such as the human brain, the dynamics of scientific collaborations, or the prediction of group relationships in social networks.