Hypergraphs offer an explicit formalism to describe multibody interactions in complex systems. To connect dynamics and function in systems with these higher-order interactions, network scientists have generalised random-walk models to hypergraphs and studied the multibody effects on flow-based centrality measures. But mapping the large-scale structure of those flows requires effective community detection methods. We derive unipartite, bipartite, and multilayer network representations of hypergraph flows and explore how they and the underlying random-walk model change the number, size, depth, and overlap of identified multilevel communities. These results help researchers choose the appropriate modelling approach when mapping flows on hypergraphs.
@article{arxiv.2101.00656,
title = {Mapping flows on hypergraphs},
author = {Anton Eriksson and Daniel Edler and Alexis Rojas and Martin Rosvall},
journal= {arXiv preprint arXiv:2101.00656},
year = {2022}
}