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Efficient community detection of network flows for varying Markov times and bipartite networks
Umeå University, Faculty of Science and Technology, Department of Physics. Department of IT and Computer Engineering, Iran University of Science and Technology, Teheran, Iran. (Integrated Science Lab (Icelab))
Umeå University, Faculty of Science and Technology, Department of Physics. (Integrated Science Lab (Icelab))
Umeå University, Faculty of Science and Technology, Department of Physics. (Integrated Science Lab (Icelab))ORCID iD: 0000-0002-7181-9940
2016 (English)In: Physical Review E, ISSN 2470-0045, Vol. 93, no 3, 032309Article in journal (Refereed) PublishedText
Abstract [en]

Community detection of network flows conventionally assumes one-step dynamics on the links. For sparse networks and interest in large-scale structures, longer timescales may be more appropriate. Oppositely, for large networks and interest in small-scale structures, shorter timescales may be better. However, current methods for analyzing networks at different timescales require expensive and often infeasible network reconstructions. To overcome this problem, we introduce a method that takes advantage of the inner workings of the map equation and evades the reconstruction step. This makes it possible to efficiently analyze large networks at different Markov times with no extra overhead cost. The method also evades the costly unipartite projection for identifying flow modules in bipartite networks.

Place, publisher, year, edition, pages
2016. Vol. 93, no 3, 032309
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URN: urn:nbn:se:umu:diva-120647DOI: 10.1103/PhysRevE.93.032309ISI: 000371745800002OAI: oai:DiVA.org:umu-120647DiVA: diva2:950818
Available from: 2016-08-02 Created: 2016-05-18 Last updated: 2016-08-02Bibliographically approved

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Kheirkhahzadeh, MasoumehLancichinetti, AndreaRosvall, Martin
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