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The unreasonable effectiveness of tree-based theory for networks with clustering

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Show simple item record Melnik, Sergey Hackett, Adam W. Porter, Mason A Mucha, Peter J Gleeson, James P. 2015-05-12T15:25:30Z 2015-05-12T15:25:30Z 2011
dc.description peer-reviewed en_US
dc.description.abstract We demonstrate that a tree-based theory for various dynamical processes operating on static, undirected networks yields extremely accurate results for several networks with high levels of clustering. We find that such a theory works well as long as the mean intervertex distance l is sufficiently small-that is, as long as it is close to the value of l in a random network with negligible clustering and the same degree-degree correlations. We support this hypothesis numerically using both real-world networks from various domains and several classes of synthetic clustered networks. We present analytical calculations that further support our claim that tree-based theories can be accurate for clustered networks, provided that the networks are "sufficiently small" worlds. en_US
dc.language.iso eng en_US
dc.publisher American Physical Society en_US
dc.relation.ispartofseries Physical Review E;83, 036112
dc.subject complex networks en_US
dc.subject heterogeneous networks en_US
dc.subject random graphs en_US
dc.subject populations en_US
dc.subject epidemics en_US
dc.subject dynamics en_US
dc.subject disease en_US
dc.subject spread en_US
dc.title The unreasonable effectiveness of tree-based theory for networks with clustering en_US
dc.type info:eu-repo/semantics/article en_US
dc.type.supercollection all_ul_research en_US
dc.type.supercollection ul_published_reviewed en_US 2015-05-12T15:01:21Z
dc.description.version PUBLISHED
dc.identifier.doi 10.1103/PhysRevE.83.036112
dc.contributor.sponsor SFI en_US
dc.relation.projectid 06/IN.1/I366 en_US
dc.relation.projectid 06/MI/005 en_US
dc.rights.accessrights info:eu-repo/semantics/openAccess en_US
dc.internal.rssid 1132568
dc.internal.copyrightchecked Yes
dc.description.status peer-reviewed

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