How choosing random-walk model and network representation matters for flow-based community detection in hypergraphs release_hyjou6il2jg7rhv44fxsp4miue

by Anton Eriksson, Daniel Edler, Alexis Rojas, Manlio de Domenico, Martin Rosvall

Published in Communications Physics by Springer Science and Business Media LLC.

2021  

Abstract

<jats:title>Abstract</jats:title>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. 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.
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