Flow-Based Community Detection in Hypergraphs

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

Research output: Contribution in Book/Catalog/Report/Conference proceedingChapter (peer-reviewed)peer-review

5 Downloads (Pure)

Abstract

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.
Original languageEnglish
Title of host publicationHigher-Order Systems
PublisherSpringer
Pages141-161
Number of pages21
ISBN (Electronic)978-3-030-91374-8
ISBN (Print)978-3-030-91376-2
DOIs
Publication statusPublished - 27 Apr 2022

Publication series

NameUnderstanding Complex Systems
PublisherSpringer
ISSN (Print)1860-0832
ISSN (Electronic)1860-0840

Keywords

  • hypergraphs
  • Community detection
  • complex networks
  • Markov processes
  • Map Equation

Fingerprint

Dive into the research topics of 'Flow-Based Community Detection in Hypergraphs'. Together they form a unique fingerprint.

Cite this