  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.
