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Article: WebScore: An Effective Page Scoring Approach for Uncertain Web Social Networks

TitleWebScore: An Effective Page Scoring Approach for Uncertain Web Social Networks
Authors
Keywordscentrality measures
PageRank
uncertainty
web social network
Issue Date2011
PublisherAtlantis Press. The Journal's web site is located at http://www.atlantis-press.com/publications/ijcis/
Citation
International Journal Of Computational Intelligence Systems, 2011, v. 4 n. 5, p. 1012-1021 How to Cite?
AbstractTo effectively score pages with uncertainty in web social networks, we first proposed a new concept called transition probability matrix and formally defined the uncertainty in web social networks. Second, we proposed a hybrid page scoring algorithm, called WebScore, based on the PageRank algorithm and three centrality measures including degree, betweenness, and closeness. Particularly, WebScore takes into a full consideration of the uncertainty of web social networks by computing the transition probability from one page to another. The basic idea of WebScore is to: (1) integrate uncertainty into PageRank in order to accurately rank pages, and (2) apply the centrality measures to calculate the importance of pages in web social networks. In order to verify the performance of WebScore, we developed a web social network analysis system which can partition web pages into distinct groups and score them in an effective fashion. Finally, we conducted extensive experiments on real data and the results show that WebScore is effective at scoring uncertain pages with less time deficiency than PageRank and centrality measures based page scoring algorithms. © 2011 Copyright Taylor and Francis Group, LLC.
Persistent Identifierhttp://hdl.handle.net/10722/164737
ISSN
2014 Impact Factor: 0.574
2014 SCImago Journal Rankings: 0.531
References

 

DC FieldValueLanguage
dc.contributor.authorQiao, Sen_HK
dc.contributor.authorLi, Ten_HK
dc.contributor.authorChau, Men_HK
dc.contributor.authorPeng, Jen_HK
dc.contributor.authorZhu, Yen_HK
dc.contributor.authorLi, Hen_HK
dc.date.accessioned2012-09-20T08:08:59Z-
dc.date.available2012-09-20T08:08:59Z-
dc.date.issued2011en_HK
dc.identifier.citationInternational Journal Of Computational Intelligence Systems, 2011, v. 4 n. 5, p. 1012-1021en_HK
dc.identifier.issn1875-6891en_HK
dc.identifier.urihttp://hdl.handle.net/10722/164737-
dc.description.abstractTo effectively score pages with uncertainty in web social networks, we first proposed a new concept called transition probability matrix and formally defined the uncertainty in web social networks. Second, we proposed a hybrid page scoring algorithm, called WebScore, based on the PageRank algorithm and three centrality measures including degree, betweenness, and closeness. Particularly, WebScore takes into a full consideration of the uncertainty of web social networks by computing the transition probability from one page to another. The basic idea of WebScore is to: (1) integrate uncertainty into PageRank in order to accurately rank pages, and (2) apply the centrality measures to calculate the importance of pages in web social networks. In order to verify the performance of WebScore, we developed a web social network analysis system which can partition web pages into distinct groups and score them in an effective fashion. Finally, we conducted extensive experiments on real data and the results show that WebScore is effective at scoring uncertain pages with less time deficiency than PageRank and centrality measures based page scoring algorithms. © 2011 Copyright Taylor and Francis Group, LLC.en_HK
dc.languageengen_US
dc.publisherAtlantis Press. The Journal's web site is located at http://www.atlantis-press.com/publications/ijcis/-
dc.relation.ispartofInternational Journal of Computational Intelligence Systemsen_HK
dc.subjectcentrality measuresen_HK
dc.subjectPageRanken_HK
dc.subjectuncertaintyen_HK
dc.subjectweb social networken_HK
dc.titleWebScore: An Effective Page Scoring Approach for Uncertain Web Social Networksen_HK
dc.typeArticleen_HK
dc.identifier.emailChau, M: mchau@hkucc.hku.hken_HK
dc.identifier.authorityChau, M=rp01051en_HK
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1080/18756891.2011.9727849en_HK
dc.identifier.scopuseid_2-s2.0-84857451347en_HK
dc.identifier.hkuros207065en_US
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-84857451347&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.volume4en_HK
dc.identifier.issue5en_HK
dc.identifier.spage1012en_HK
dc.identifier.epage1021en_HK
dc.publisher.placeFrance-
dc.identifier.scopusauthoridQiao, S=16205409900en_HK
dc.identifier.scopusauthoridLi, T=7406372548en_HK
dc.identifier.scopusauthoridChau, M=7006073763en_HK
dc.identifier.scopusauthoridPeng, J=7401958564en_HK
dc.identifier.scopusauthoridZhu, Y=8921604000en_HK
dc.identifier.scopusauthoridLi, H=36656039500en_HK

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