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

Abstract

Real-world networks are typically characterised by a non-trivial organization at the mesoscale, such that groups of nodes are preferentially connected within distinguishable network regions known as communities. In this work the authors define unipartite, bipartite, and multilayer network representations of hypergraph flows to extract the community structure of social and biological systems with higher-order interactions.