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Analytical approach to network inference : Investigating degree distribution

dc.contributor.authorCecchini, Gloria
dc.contributor.authorSchelter, Bjoern
dc.date.accessioned2018-08-14T14:55:06Z
dc.date.available2018-08-14T14:55:06Z
dc.date.issued2018-08
dc.identifier130627743
dc.identifier70749850-adeb-47b9-94e2-169a4fe0d905
dc.identifier85051482237
dc.identifier.citationCecchini , G & Schelter , B 2018 , ' Analytical approach to network inference : Investigating degree distribution ' , Physical Review. E, Statistical, Nonlinear and Soft Matter Physics , vol. 98 , no. 2 , 022311 . https://doi.org/10.1103/PhysRevE.98.022311en
dc.identifier.issn1539-3755
dc.identifier.urihttp://hdl.handle.net/2164/10901
dc.descriptionThe authors thank Dr. Daniel Vogel for helpful comments and discussions. This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No 642563.en
dc.format.extent10
dc.format.extent2098059
dc.language.isoeng
dc.relation.ispartofPhysical Review. E, Statistical, Nonlinear and Soft Matter Physicsen
dc.subjectQA75 Electronic computers. Computer scienceen
dc.subjectEuropean Commissionen
dc.subject642563en
dc.subject.lccQA75en
dc.titleAnalytical approach to network inference : Investigating degree distributionen
dc.typeJournal articleen
dc.contributor.institutionUniversity of Aberdeen.Natural & Computing Sciencesen
dc.contributor.institutionUniversity of Aberdeen.Physicsen
dc.contributor.institutionUniversity of Aberdeen.Institute for Complex Systems and Mathematical Biology (ICSMB)en
dc.description.statusPeer revieweden
dc.identifier.doi10.1103/PhysRevE.98.022311


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