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dc.contributor.authorPeixoto, Tiago P.
dc.date.accessioned2023-06-16T09:16:35Z
dc.date.available2023-06-16T09:16:35Z
dc.date.issued2021
dc.identifier.issn2160-3308
dc.identifier.doi10.1103/PhysRevX.11.021003
dc.identifier.urihttp://hdl.handle.net/20.500.14018/13778
dc.description.abstractCommunity detection methods attempt to divide a network into groups of nodes that share similar properties, thus revealing its large-scale structure. A major challenge when employing such methods is that they are often degenerate, typically yielding a complex landscape of competing answers. As an attempt to extract understanding from a population of alternative solutions, many methods exist to establish a consensus among them in the form of a single partition “point estimate” that summarizes the whole distribution. Here, we show that it is, in general, not possible to obtain a consistent answer from such point estimates when the underlying distribution is too heterogeneous. As an alternative, we provide a comprehensive set of methods designed to characterize and summarize complex populations of partitions in a manner that captures not only the existing consensus but also the dissensus between elements of the population. Our approach is able to model mixed populations of partitions, where multiple consensuses can coexist, representing different competing hypotheses for the network structure. We also show how our methods can be used to compare pairs of partitions, how they can be generalized to hierarchical divisions, and how they can be used to perform statistical model selection between competing hypotheses.
dc.language.isoeng
dc.publisherAmerican Physical Society
dc.rightsCC BY 4.0
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectComplex systems
dc.subjectInterdisciplinary physics
dc.subjectStatistical physics
dc.titleRevealing Consensus and Dissensus between Network Partitions
dc.typeJournal article
dc.source.journaltitlePhysical Review X
dc.source.volume11
dc.source.issue2
dc.source.spage1
dc.source.epage30
dc.description.versionPublished version
refterms.dateFOA2023-06-16T09:16:35Z
dc.contributor.unitDepartment of Network and Data Science
dc.source.journalabbrevPhys. Rev. X
dc.identifier.urlhttps://link.aps.org/doi/10.1103/PhysRevX.11.021003


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