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Flow-Based Community Detection in Hypergraphs
Umeå University, Faculty of Science and Technology, Department of Physics.ORCID iD: 0000-0001-5859-4073
University of Namur, Namur, Belgium.
University of Oxford, Oxford, United Kingdom.
Umeå University, Faculty of Science and Technology, Department of Physics.ORCID iD: 0000-0002-1063-9102
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2022 (English)In: Higher-Order Systems / [ed] Federico Battiston; Giovanni Petri, Springer Science+Business Media B.V., 2022, , p. 21p. 141-161Chapter in book (Refereed)
Abstract [en]

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.

Place, publisher, year, edition, pages
Springer Science+Business Media B.V., 2022. , p. 21p. 141-161
Series
Understanding Complex Systems, ISSN 1860-0832, E-ISSN 1860-0840
National Category
Computer Sciences
Identifiers
URN: urn:nbn:se:umu:diva-194897DOI: 10.1007/978-3-030-91374-8_4Scopus ID: 2-s2.0-85129151109ISBN: 978-3-030-91374-8 (electronic)OAI: oai:DiVA.org:umu-194897DiVA, id: diva2:1666776
Available from: 2022-06-09 Created: 2022-06-09 Last updated: 2023-04-13Bibliographically approved
In thesis
1. Mapping higher-order dynamics and interactions in complex networks
Open this publication in new window or tab >>Mapping higher-order dynamics and interactions in complex networks
2023 (English)Doctoral thesis, comprehensive summary (Other academic)
Alternative title[sv]
Kartläggning av högre ordningens dynamik och interaktioner i komplexa nätverk
Abstract [en]

Complex systems research seeks to explain emergent properties in social, technological, and biological systems that result from interactions between their components. As data on the intricate relationships within these systems become increasingly available, there is a growing need for more sophisticated models to describe them accurately and offer deeper insights.

This thesis addresses challenges in incorporating higher-order interactions and dynamics into the analysis of complex systems that go beyond standard network approaches. It covers mapping changing network organizations, modeling higher-order dynamics on ordinary networks, integrating network structure and metadata, and modeling multibody interactions. The thesis offers new tools and models to enhance our understanding of how higher-order dynamics and interactions shape the organization and give rise to the function of complex systems by providing more accurate representations than traditional network models. These findings pave the way for new research in network science.

Abstract [sv]

Forskning om komplexa system strävar efter att förklara egenskaper som uppstår i sociala, teknologiska och biologiska system genom samspel mellan deras delar. När allt mer data om dessa relationer blir tillgänglig ökar behovet av mer avancerade modeller för att beskriva systemen korrekt och ge djupare insikter.

Den här avhandlingen tar upp utmaningar med att inkludera högre ordningens interaktioner och dynamik i analysen av komplexa system, genom att använda högre ordningens nätverksmodeller. Den behandlar kartläggning av förändrade nätverksstrukturer, modellering av högre ordningens dynamik i vanliga nätverk, kombinering av nätverksstruktur och metadata samt modellering av flerkroppsinteraktioner. De nya verktygen och modellerna ökar vår förståelse för hur högre ordningens dynamik och interaktioner påverkar organisationen och funktionen hos komplexa system. Detta görs genom att erbjuda mer precisa representationer än traditionella nätverksmodeller. Dessa resultat öppnar upp för framtida forskning inom nätverksvetenskap.

Place, publisher, year, edition, pages
Umeå: Umeå universitet, 2023. p. 65
Keywords
networks, community detection, information theory, the map equation
National Category
Other Physics Topics
Identifiers
urn:nbn:se:umu:diva-206628 (URN)978-91-7855-984-8 (ISBN)978-91-7855-985-5 (ISBN)
Public defence
2023-05-12, NAT.D.410, Naturvetarhuset, Umeå, 09:00 (English)
Opponent
Supervisors
Available from: 2023-04-21 Created: 2023-04-13 Last updated: 2023-04-14Bibliographically approved

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Eriksson, AntonRojas, AlexisRosvall, Martin

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