Quantifying randomness in real networks

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dc.contributor Aalto-yliopisto fi
dc.contributor Aalto University en
dc.contributor.author Orsini, C.
dc.contributor.author Dankulov, M.M.
dc.contributor.author Colomer - de-Simon, P.
dc.contributor.author Jamakovic, A.
dc.contributor.author Mahadevan, P.
dc.contributor.author Vahdat, A.
dc.contributor.author Bassler, K.E.
dc.contributor.author Toroczkai, Z.
dc.contributor.author Boguna, M.
dc.contributor.author Caldarelli, G.
dc.contributor.author Fortunato, Santo
dc.contributor.author Krioukov, D.
dc.date.accessioned 2017-05-11T06:54:55Z
dc.date.available 2017-05-11T06:54:55Z
dc.date.issued 2015
dc.identifier.citation Orsini , C , Dankulov , M M , Colomer - de-Simon , P , Jamakovic , A , Mahadevan , P , Vahdat , A , Bassler , K E , Toroczkai , Z , Boguna , M , Caldarelli , G , Fortunato , S & Krioukov , D 2015 , ' Quantifying randomness in real networks ' NATURE COMMUNICATIONS , vol 6 , 8627 , pp. 1-10 . DOI: 10.1038/ncomms9627 en
dc.identifier.issn 2041-1723
dc.identifier.other PURE UUID: 40a51c89-40bc-4636-a0fb-4ec9c90f443d
dc.identifier.other PURE ITEMURL: https://research.aalto.fi/en/publications/quantifying-randomness-in-real-networks(40a51c89-40bc-4636-a0fb-4ec9c90f443d).html
dc.identifier.other PURE FILEURL: https://research.aalto.fi/files/12948456/ncomms9627.pdf
dc.identifier.uri https://aaltodoc.aalto.fi/handle/123456789/25485
dc.description VK: Fortunato, S.
dc.description.abstract Represented as graphs, real networks are intricate combinations of order and disorder. Fixing some of the structural properties of network models to their values observed in real networks, many other properties appear as statistical consequences of these fixed observables, plus randomness in other respects. Here we employ the dk-series, a complete set of basic characteristics of the network structure, to study the statistical dependencies between different network properties. We consider six real networks—the Internet, US airport network, human protein interactions, technosocial web of trust, English word network, and an fMRI map of the human brain—and find that many important local and global structural properties of these networks are closely reproduced by dk-random graphs whose degree distributions, degree correlations and clustering are as in the corresponding real network. We discuss important conceptual, methodological, and practical implications of this evaluation of network randomness, and release software to generate dk-random graphs. en
dc.format.extent 1-10
dc.format.mimetype application/pdf
dc.language.iso en en
dc.relation.ispartofseries NATURE COMMUNICATIONS en
dc.relation.ispartofseries Volume 6 en
dc.rights openAccess en
dc.title Quantifying randomness in real networks en
dc.type A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä fi
dc.description.version Peer reviewed en
dc.contributor.department BECS
dc.contributor.department Department of Computer Science en
dc.identifier.urn URN:NBN:fi:aalto-201705113869
dc.identifier.doi 10.1038/ncomms9627
dc.type.version publishedVersion


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