University of Limerick Institutional Repository

Accuracy of mean-field theory for dynamics on real-world networks

DSpace Repository

Show simple item record

dc.contributor.author Gleeson, James P.
dc.contributor.author Melnik, Sergey
dc.contributor.author Ward, Jonathan A
dc.contributor.author Porter, Mason A
dc.contributor.author Murcha, Peter J
dc.date.accessioned 2015-05-06T11:16:46Z
dc.date.available 2015-05-06T11:16:46Z
dc.date.issued 2012
dc.identifier.uri http://hdl.handle.net/10344/4453
dc.description peer-reviewed en_US
dc.description.abstract Mean-field analysis is an important tool for understanding dynamics on complex networks. However, surprisingly little attention has been paid to the question of whether mean-field predictions are accurate, and this is particularly true for real-world networks with clustering and modular structure. In this paper, we compare mean-field predictions to numerical simulation results for dynamical processes running on 21 real-world networks and demonstrate that the accuracy of such theory depends not only on the mean degree of the networks but also on the mean first-neighbor degree. We show that mean-field theory can give (unexpectedly) accurate results for certain dynamics on disassortative real-world networks even when the mean degree is as low as 4. en_US
dc.language.iso eng en_US
dc.publisher American Physical Society en_US
dc.relation.ispartofseries Physical Review E;85, 026106
dc.relation.uri http://dx.doi.org/10.1103/PhysRevE.85.026106
dc.subject complex networks en_US
dc.subject heterogeneous networks en_US
dc.subject epidemics en_US
dc.subject spread en_US
dc.subject models en_US
dc.title Accuracy of mean-field theory for dynamics on real-world networks 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
dc.date.updated 2015-05-05T18:44:37Z
dc.description.version PUBLISHED
dc.identifier.doi 10.1103/PhysRevE.85.026106
dc.contributor.sponsor SFI en_US
dc.contributor.sponsor INSPIRE en_US
dc.relation.projectid 06/IN.1/I366 en_US
dc.relation.projectid 06/MI/005 en_US
dc.relation.projectid 09/SRC/EI780 en_US
dc.relation.projectid 220020177 en_US
dc.relation.projectid EP/I016058/1 en_US
dc.relation.projectid DMS-0645369 en_US
dc.rights.accessrights info:eu-repo/semantics/openAccess en_US
dc.internal.rssid 1387570
dc.internal.copyrightchecked Yes
dc.description.status peer-reviewed


Files in this item

This item appears in the following Collection(s)

Show simple item record

Search ULIR


Browse

My Account

Statistics