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How choosing random-walk model and network representation matters for flow-based community detection in hypergraphs
Umeå University, Faculty of Science and Technology, Department of Physics.ORCID iD: 0000-0001-5859-4073
Umeå University, Faculty of Science and Technology, Department of Physics.ORCID iD: 0000-0001-5420-0591
Umeå University, Faculty of Science and Technology, Department of Physics.ORCID iD: 0000-0002-1063-9102
CoMuNe Lab, Fondazione Bruno Kessler, Povo (TN), Italy.
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2021 (English)In: Communications Physics, E-ISSN 2399-3650, Vol. 4, no 1, article id 133Article in journal (Refereed) Published
Abstract [en]

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. Mapping the large-scale structure of those flows requires effective community detection methods applied to cogent network representations. For different hypergraph data and research questions, which combination of random-walk model and network representation is best? We define 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.

Place, publisher, year, edition, pages
Nature Publishing Group, 2021. Vol. 4, no 1, article id 133
National Category
Computer Sciences
Identifiers
URN: urn:nbn:se:umu:diva-184903DOI: 10.1038/s42005-021-00634-zISI: 000663511200004Scopus ID: 2-s2.0-85107742703OAI: oai:DiVA.org:umu-184903DiVA, id: diva2:1570795
Available from: 2021-06-22 Created: 2021-06-22 Last updated: 2023-04-14Bibliographically 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, AntonEdler, DanielRojas, AlexisRosvall, Martin

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