Francesco Tudisco

Associate Professor (Reader) in Machine Learning

School of Mathematics, The University of Edinburgh
The Maxwell Institute for Mathematical Sciences
School of Mathematics, Gran Sasso Science Institute JCMB, King’s Buildings, Edinburgh EH93FD UK
email: f dot tudisco at

Learning the effective order of a hypergraph dynamical system

Leonie Neuhäuser, Michael Scholkemper, Francesco Tudisco, Michael T. Schaub,
preprint, (2023)


Dynamical systems on hypergraphs can display a rich set of behaviours not observable for systems with pairwise interactions. Given a distributed dynamical system with a putative hypergraph structure, an interesting question is thus how much of this hypergraph structure is actually necessary to faithfully replicate the observed dynamical behaviour. To answer this question, we propose a method to determine the minimum order of a hypergraph necessary to approximate the corresponding dynamics accurately. Specifically, we develop an analytical framework that allows us to determine this order when the type of dynamics is known. We utilize these ideas in conjunction with a hypergraph neural network to directly learn the dynamics itself and the resulting order of the hypergraph from both synthetic and real data sets consisting of observed system trajectories.

Please cite this paper as:

  title={Learning the effective order of a hypergraph dynamical system},
  author={Neuhäuser, Leonie and  Scholkemper, Michael and  Tudisco, Francesco and Schaub, Michael T.},

Links: arxiv

Keywords: deep learning neural networks deep learning hypergraphs higher-order networks networks