@article{eriksson_edler_rojas_de domenico_rosvall_2021,
title={How choosing random-walk model and network representation matters for flow-based community detection in hypergraphs},
DOI={10.1038/s42005-021-00634-z},
abstractNote={AbstractHypergraphs 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. Mapping the large-scale structure of those flows requires effective community detection methods applied to cogent network representations. For different hypergraph data and research questions, which combination of random-walk model and network representation is best? We define 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.},
publisher={Springer Science and Business Media LLC},
author={Eriksson, Anton and Edler, Daniel and Rojas, Alexis and de Domenico, Manlio and Rosvall, Martin},
year={2021},
month={Jun}
}