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Mapping biased higher-order walks reveals overlapping communities
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: 0000-0001-7881-2496
Umeå University, Faculty of Science and Technology, Department of Physics.ORCID iD: 0000-0002-7181-9940
(English)Manuscript (preprint) (Other academic)
Abstract [en]

Researchers use networks to model relational data from complex systems, and tools from network science to map them and understand their function. Flow communities capture the organization of various real-world systems such as social networks, protein-protein interactions, and species distributions, and are often overlapping. However, mapping overlapping flow-based communities requires higher-order data, which is not always available. To address this issue, we take inspiration from the representation-learning algorithm node2vec, and apply higher-order biased random walks on first-order networks to obtain higher-order data. But instead of explicitly simulating the walks, we model them with sparse memory networks and control the complexity of the higher-order model with an information-theoretic approach through a tunable information-loss parameter. Using the map equation framework, we partition the resulting higher-order networks into overlapping modules. We find that our method recovers planted overlapping partitions in synthetic benchmarks and identifies overlapping communities in real-world networks.

Keywords [en]
networks, community detection, information theory, the map equation
National Category
Other Physics Topics
Identifiers
URN: urn:nbn:se:umu:diva-206627DOI: 10.48550/arXiv.2304.05775OAI: oai:DiVA.org:umu-206627DiVA, id: diva2:1750385
Available from: 2023-04-13 Created: 2023-04-13 Last updated: 2023-04-13
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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Holmgren, AntonBlöcker, ChristopherRosvall, Martin

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