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dc.contributor.authorPfaltz, John L.
dc.date.accessioned2020-05-05T06:06:39Z
dc.date.available2020-05-05T06:06:39Z
dc.date.issued2019cs
dc.identifier.citationMathematics for Applications. 2019 vol. 8, č. 1, s. 79-96. ISSN 1805-3629cs
dc.identifier.issn1805-3629
dc.identifier.urihttp://hdl.handle.net/11012/186964
dc.description.abstractIn this paper, we study relational networks. They may be as large as socialnetworks or as small as neural networks. We employ the concepts of closure andclosure operators to describe their structures, and introduce the idea of functionaltransformation to model their dynamic qualities.One transformation,ω, reduces a complex network to a much simpler form, yetpreserves important properties such as path connectivity and centrality measures.The other transformation,ε, expands a network by using grammar-like productions.Both are continuous (with respect to closure) and we show thatεis effectivelyω−1in thatω·ε·ω=ω.It is thought thatωmay model human memory consolidation and thatεmaymodel memory reconstruction.en
dc.formattextcs
dc.format.extent79-96cs
dc.format.mimetypeapplication/pdfen
dc.language.isoencs
dc.publisherVysoké učení technické v Brně, Fakulta strojního inženýrství, Ústav matematikycs
dc.relation.ispartofMathematics for Applicationsen
dc.relation.urihttp://ma.fme.vutbr.cz/archiv/8_1/ma_8_1_pfaltz_final.pdfcs
dc.rights© Vysoké učení technické v Brně, Fakulta strojního inženýrství, Ústav matematikycs
dc.titleTwo network transformationsen
eprints.affiliatedInstitution.departmentÚstav matematikycs
eprints.affiliatedInstitution.facultyFakulta strojního inženýrstvícs
dc.coverage.issue1cs
dc.coverage.volume8cs
dc.identifier.doi10.13164/ma.2019.06en
dc.rights.accessopenAccessen
dc.type.driverarticleen
dc.type.statusPeer-revieweden
dc.type.versionpublishedVersionen


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