@inbook{1724c8d0f0174040a56e8d3f5a2bdb5e,
title = "Flow-Based Community Detection in Hypergraphs",
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{\textquoteright} 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.",
keywords = "hypergraphs, Community detection, complex networks, Markov processes, Map Equation",
author = "Anton Eriksson and Timoteo Carletti and Renaud Lambiotte and Alexis Rojas and Martin Rosvall",
note = "Funding Information: A.E was supported by the Swedish Foundation for Strategic Research, Grant No. SB16-0089. A.R. and M.R. were supported by the Swedish Research Council, Grant No. 2016-00796. R.L. was supported by the EPSRC Grants No. EP/V013068/1 and EP/V03474X/1. Publisher Copyright: {\textcopyright} 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.",
year = "2022",
month = apr,
day = "27",
doi = "https://doi.org/10.1007/978-3-030-91374-8_4",
language = "English",
isbn = "978-3-030-91376-2",
series = "Understanding Complex Systems",
publisher = "Springer",
pages = "141--161",
booktitle = "Higher-Order Systems",
}