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dc.contributor.authorYang, Haixuan
dc.date.accessioned2014-01-22T16:58:14Z
dc.date.available2014-01-22T16:58:14Z
dc.date.issued2006
dc.identifier.citationYang, Haixuan and King, Irwin and Lyu, Michael R (2006) Predictive random graph ranking on the web Neural Networks, 2006. IJCNN'06. International Joint Conference onen_US
dc.identifier.isbn0-7803-9490-9
dc.identifier.urihttp://hdl.handle.net/10379/3996
dc.description.abstractThe incomplete information about the Web structure causes inaccurate results of various ranking algorithms. In this paper, we propose a solution to this problem by formulating a new framework called, Predictive Random Graph Ranking, in which we generate a random graph based on the known information about the Web structure. The random graph can be considered as the predicted Web structure, on which ranking algorithm are expected to be improved in accuracy. For this purpose, we extend some current ranking algorithms from a static graph to a random graph. Experimental results show that the Predictive Random Graph Ranking framework can improve the accuracy of the ranking algorithms such as PageRank, Common Neighbor, and Jaccard's Coefficient.en_US
dc.formatapplication/pdfen_US
dc.language.isoenen_US
dc.relation.ispartofNeural Networks, 2006. IJCNN'06. International Joint Conference onen
dc.rightsAttribution-NonCommercial-NoDerivs 3.0 Ireland
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/3.0/ie/
dc.subjectWeb sitesen_US
dc.subjectGraph theoryen_US
dc.subjectJaccard coefficienten_US
dc.subjectPageRanken_US
dc.subjectWeb structureen_US
dc.subjectCommon neighbouren_US
dc.subjectPredictive random graph rankingen_US
dc.subjectComputer scienceen_US
dc.subjectCrawlersen_US
dc.subjectWeb pagesen_US
dc.titlePredictive random graph ranking on the weben_US
dc.typeConference Paperen_US
dc.date.updated2013-09-25T12:44:09Z
dc.local.publishedsourcehttp://dx.doi.org/10.1109/IJCNN.2006.246901en_US
dc.description.peer-reviewedpeer-reviewed
dc.contributor.funder|~|
dc.internal.rssid3760800
dc.local.contactHaixuan Yang, School Of Mathematics,Statistics, & Applied Mathematics, Adb-G013, Nui Galway. 2320 Email: haixuan.yang@nuigalway.ie
dc.local.copyrightcheckedNo
dc.local.versionPUBLISHED
nui.item.downloads1498


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Attribution-NonCommercial-NoDerivs 3.0 Ireland
Except where otherwise noted, this item's license is described as Attribution-NonCommercial-NoDerivs 3.0 Ireland