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Hierarchical structure and the prediction of missing links in networks

dc.contributor.authorClauset, Aaronen_US
dc.contributor.authorMoore, Cristopheren_US
dc.contributor.authorNewman, M. E. J.en_US
dc.date.accessioned2009-06-01T17:27:20Z
dc.date.available2009-06-01T17:27:20Z
dc.date.issued2008-05-01en_US
dc.identifier.citationClauset, Aaron; Moore, Cristopher; Newman, M. E. J.. (2008) "Hierarchical structure and the prediction of missing links in networks." Nature 453(7191): 98-101. <http://hdl.handle.net/2027.42/62623>en_US
dc.identifier.issn0028-0836en_US
dc.identifier.urihttps://hdl.handle.net/2027.42/62623
dc.identifier.urihttp://www.ncbi.nlm.nih.gov/sites/entrez?cmd=retrieve&db=pubmed&list_uids=18451861&dopt=citationen_US
dc.description.abstractNetworks have in recent years emerged as an invaluable tool for describing and quantifying complex systems in many branches of science(1-3). Recent studies suggest that networks often exhibit hierarchical organization, in which vertices divide into groups that further subdivide into groups of groups, and so forth over multiple scales. In many cases the groups are found to correspond to known functional units, such as ecological niches in food webs, modules in biochemical networks ( protein interaction networks, metabolic networks or genetic regulatory networks) or communities in social networks(4-7). Here we present a general technique for inferring hierarchical structure from network data and show that the existence of hierarchy can simultaneously explain and quantitatively reproduce many commonly observed topological properties of networks, such as right- skewed degree distributions, high clustering coefficients and short path lengths. We further show that knowledge of hierarchical structure can be used to predict missing connections in partly known networks with high accuracy, and for more general network structures than competing techniques(8). Taken together, our results suggest that hierarchy is a central organizing principle of complex networks, capable of offering insight into many network phenomena.en_US
dc.format.extent265653 bytes
dc.format.extent2489 bytes
dc.format.mimetypeapplication/pdf
dc.format.mimetypetext/plain
dc.publisherNature Publishing Groupen_US
dc.sourceNatureen_US
dc.titleHierarchical structure and the prediction of missing links in networksen_US
dc.typeArticleen_US
dc.subject.hlbtoplevelScienceen_US
dc.description.peerreviewedPeer Revieweden_US
dc.contributor.affiliationum[Newman, M. E. J.] Univ Michigan, Dept Phys, Ann Arbor, MI 48109 USAen_US
dc.contributor.affiliationum[Newman, M. E. J.] Univ Michigan, Ctr Study Complex Syst, Ann Arbor, MI 48109 USAen_US
dc.contributor.affiliationother[Clauset, Aaronen_US
dc.contributor.affiliationotherMoore, Cristopher] Univ New Mexico, Dept Comp Sci, Albuquerque, NM 87131 USAen_US
dc.contributor.affiliationother[Moore, Cristopher] Univ New Mexico, Dept Phys & Astron, Albuquerque, NM 87131 USAen_US
dc.contributor.affiliationother[Clauset, Aaronen_US
dc.contributor.affiliationotherMoore, Cristopheren_US
dc.contributor.affiliationotherNewman, M. E. J.] Santa Fe Inst, Santa Fe, NM 87501 USAen_US
dc.identifier.pmid18451861en_US
dc.description.bitstreamurlhttp://deepblue.lib.umich.edu/bitstream/2027.42/62623/1/nature06830.pdf
dc.identifier.doihttp://dx.doi.org/10.1038/nature06830en_US
dc.identifier.sourceNatureen_US
dc.contributor.authoremailaaronc@santafe.eduen_US
dc.owningcollnameInterdisciplinary and Peer-Reviewed


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