Mapping flows on hypergraphs

Abstract

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.

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Cited by in corpus (17)

• Dynamics on higher-order networks: A review
• Higher-order interactions shape collective dynamics differently in hypergraphs and simplicial complexes
• Higher-order motif analysis in hypergraphs
• Inference of hyperedges and overlapping communities in hypergraphs
• Community Detection in Large Hypergraphs
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• Multiplex measures for higher-order networks
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