Hierarchical structure and the prediction of missing links in networks
dc.contributor.author | Clauset, Aaron | en_US |
dc.contributor.author | Moore, Cristopher | en_US |
dc.contributor.author | Newman, M. E. J. | en_US |
dc.date.accessioned | 2009-06-01T17:27:20Z | |
dc.date.available | 2009-06-01T17:27:20Z | |
dc.date.issued | 2008-05-01 | en_US |
dc.identifier.citation | Clauset, 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.issn | 0028-0836 | en_US |
dc.identifier.uri | https://hdl.handle.net/2027.42/62623 | |
dc.identifier.uri | http://www.ncbi.nlm.nih.gov/sites/entrez?cmd=retrieve&db=pubmed&list_uids=18451861&dopt=citation | en_US |
dc.description.abstract | Networks 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.extent | 265653 bytes | |
dc.format.extent | 2489 bytes | |
dc.format.mimetype | application/pdf | |
dc.format.mimetype | text/plain | |
dc.publisher | Nature Publishing Group | en_US |
dc.source | Nature | en_US |
dc.title | Hierarchical structure and the prediction of missing links in networks | en_US |
dc.type | Article | en_US |
dc.subject.hlbtoplevel | Science | en_US |
dc.description.peerreviewed | Peer Reviewed | en_US |
dc.contributor.affiliationum | [Newman, M. E. J.] Univ Michigan, Dept Phys, Ann Arbor, MI 48109 USA | en_US |
dc.contributor.affiliationum | [Newman, M. E. J.] Univ Michigan, Ctr Study Complex Syst, Ann Arbor, MI 48109 USA | en_US |
dc.contributor.affiliationother | [Clauset, Aaron | en_US |
dc.contributor.affiliationother | Moore, Cristopher] Univ New Mexico, Dept Comp Sci, Albuquerque, NM 87131 USA | en_US |
dc.contributor.affiliationother | [Moore, Cristopher] Univ New Mexico, Dept Phys & Astron, Albuquerque, NM 87131 USA | en_US |
dc.contributor.affiliationother | [Clauset, Aaron | en_US |
dc.contributor.affiliationother | Moore, Cristopher | en_US |
dc.contributor.affiliationother | Newman, M. E. J.] Santa Fe Inst, Santa Fe, NM 87501 USA | en_US |
dc.identifier.pmid | 18451861 | en_US |
dc.description.bitstreamurl | http://deepblue.lib.umich.edu/bitstream/2027.42/62623/1/nature06830.pdf | |
dc.identifier.doi | http://dx.doi.org/10.1038/nature06830 | en_US |
dc.identifier.source | Nature | en_US |
dc.contributor.authoremail | aaronc@santafe.edu | en_US |
dc.owningcollname | Interdisciplinary and Peer-Reviewed |
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