Mapping flows on sparse networks with missing links

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dc.citation.issue1
dc.citation.rankM21
dc.citation.spage012302
dc.citation.volume102
dc.contributor.authorSmiljanić, Jelena
dc.contributor.authorEdler, Daniel
dc.contributor.authorRosvall, Martin
dc.date.accessioned2024-06-20T07:37:36Z
dc.date.available2024-06-20T07:37:36Z
dc.date.issued2020-07-06
dc.description.abstractUnreliable network data can cause community-detection methods to overfit and highlight spurious structures with misleading information about the organization and function of complex systems. Here we show how to detect significant flow-based communities in sparse networks with missing links using the map equation. Since the map equation builds on Shannon entropy estimation, it assumes complete data such that analyzing undersampled networks can lead to overfitting. To overcome this problem, we incorporate a Bayesian approach with assumptions about network uncertainties into the map equation framework. Results in both synthetic and real-world networks show that the Bayesian estimate of the map equation provides a principled approach to revealing significant structures in undersampled networks.
dc.identifier.doi10.1103/physreve.102.012302
dc.identifier.issn2470-0045
dc.identifier.issn2470-0053
dc.identifier.scopus2-s2.0-85089465455
dc.identifier.urihttps://pub.ipb.ac.rs/handle/123456789/125
dc.identifier.wos000550381200011
dc.language.isoen
dc.publisherAmerican Physical Society (APS)
dc.relation.ispartofPhysical Review E
dc.relation.ispartofabbrPhys. Rev. E
dc.rightsopenAccess
dc.titleMapping flows on sparse networks with missing links
dc.typeArticle
dc.type.versionpublishedVersion
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