Flow-Based Community Detection in Hypergraphs

Anton Eriksson, Timoteo Carletti, Renaud Lambiotte, Alexis Rojas, Martin Rosvall

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Résumé

To connect structure, dynamics and function in systems with multibody interactions, network scientists model random walks on hypergraphs and identify communities that confine the walks for a long time. The two flow-based community-detection methods Markov stability and the map equation identify such communities based on different principles and search algorithms. But how similar are the resulting communities? We explain both methods’ machinery applied to hypergraphs and compare them on synthetic and real-world hypergraphs using various hyperedge-size biased random walks and time scales. We find that the map equation is more sensitive to time-scale changes and that Markov stability is more sensitive to hyperedge-size biases.
langue originaleAnglais
titreHigher-Order Systems
EditeurSpringer
Pages141-161
Nombre de pages21
ISBN (Electronique)978-3-030-91374-8
ISBN (imprimé)978-3-030-91376-2
Les DOIs
Etat de la publicationPublié - 27 avr. 2022

Série de publications

NomUnderstanding Complex Systems
EditeurSpringer
ISSN (imprimé)1860-0832
ISSN (Electronique)1860-0840

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