File Download
 
Links for fulltext
(May Require Subscription)
 
Supplementary

Article: WebScore: An Effective Page Scoring Approach for Uncertain Web Social Networks
  • Basic View
  • Metadata View
  • XML View
TitleWebScore: An Effective Page Scoring Approach for Uncertain Web Social Networks
 
AuthorsQiao, S2
Li, T2
Chau, M1
Peng, J3
Zhu, Y2
Li, H2
 
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/
 
CitationInternational Journal Of Computational Intelligence Systems, 2011, v. 4 n. 5, p. 1012-1021 [How to Cite?]
DOI: http://dx.doi.org/10.1080/18756891.2011.9727849
 
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.
 
ISSN1875-6891
2013 Impact Factor: 0.451
2013 SCImago Journal Rankings: 0.504
 
DOIhttp://dx.doi.org/10.1080/18756891.2011.9727849
 
ReferencesReferences in Scopus
 
DC FieldValue
dc.contributor.authorQiao, S
 
dc.contributor.authorLi, T
 
dc.contributor.authorChau, M
 
dc.contributor.authorPeng, J
 
dc.contributor.authorZhu, Y
 
dc.contributor.authorLi, H
 
dc.date.accessioned2012-09-20T08:08:59Z
 
dc.date.available2012-09-20T08:08:59Z
 
dc.date.issued2011
 
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.
 
dc.description.naturelink_to_subscribed_fulltext
 
dc.identifier.citationInternational Journal Of Computational Intelligence Systems, 2011, v. 4 n. 5, p. 1012-1021 [How to Cite?]
DOI: http://dx.doi.org/10.1080/18756891.2011.9727849
 
dc.identifier.doihttp://dx.doi.org/10.1080/18756891.2011.9727849
 
dc.identifier.epage1021
 
dc.identifier.hkuros207065
 
dc.identifier.issn1875-6891
2013 Impact Factor: 0.451
2013 SCImago Journal Rankings: 0.504
 
dc.identifier.issue5
 
dc.identifier.scopuseid_2-s2.0-84857451347
 
dc.identifier.spage1012
 
dc.identifier.urihttp://hdl.handle.net/10722/164737
 
dc.identifier.volume4
 
dc.languageeng
 
dc.publisherAtlantis Press. The Journal's web site is located at http://www.atlantis-press.com/publications/ijcis/
 
dc.publisher.placeFrance
 
dc.relation.ispartofInternational Journal of Computational Intelligence Systems
 
dc.relation.referencesReferences in Scopus
 
dc.subjectcentrality measures
 
dc.subjectPageRank
 
dc.subjectuncertainty
 
dc.subjectweb social network
 
dc.titleWebScore: An Effective Page Scoring Approach for Uncertain Web Social Networks
 
dc.typeArticle
 
<?xml encoding="utf-8" version="1.0"?>
<item><contributor.author>Qiao, S</contributor.author>
<contributor.author>Li, T</contributor.author>
<contributor.author>Chau, M</contributor.author>
<contributor.author>Peng, J</contributor.author>
<contributor.author>Zhu, Y</contributor.author>
<contributor.author>Li, H</contributor.author>
<date.accessioned>2012-09-20T08:08:59Z</date.accessioned>
<date.available>2012-09-20T08:08:59Z</date.available>
<date.issued>2011</date.issued>
<identifier.citation>International Journal Of Computational Intelligence Systems, 2011, v. 4 n. 5, p. 1012-1021</identifier.citation>
<identifier.issn>1875-6891</identifier.issn>
<identifier.uri>http://hdl.handle.net/10722/164737</identifier.uri>
<description.abstract>To 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. &#169; 2011 Copyright Taylor and Francis Group, LLC.</description.abstract>
<language>eng</language>
<publisher>Atlantis Press. The Journal&apos;s web site is located at http://www.atlantis-press.com/publications/ijcis/</publisher>
<relation.ispartof>International Journal of Computational Intelligence Systems</relation.ispartof>
<subject>centrality measures</subject>
<subject>PageRank</subject>
<subject>uncertainty</subject>
<subject>web social network</subject>
<title>WebScore: An Effective Page Scoring Approach for Uncertain Web Social Networks</title>
<type>Article</type>
<description.nature>link_to_subscribed_fulltext</description.nature>
<identifier.doi>10.1080/18756891.2011.9727849</identifier.doi>
<identifier.scopus>eid_2-s2.0-84857451347</identifier.scopus>
<identifier.hkuros>207065</identifier.hkuros>
<relation.references>http://www.scopus.com/mlt/select.url?eid=2-s2.0-84857451347&amp;selection=ref&amp;src=s&amp;origin=recordpage</relation.references>
<identifier.volume>4</identifier.volume>
<identifier.issue>5</identifier.issue>
<identifier.spage>1012</identifier.spage>
<identifier.epage>1021</identifier.epage>
<publisher.place>France</publisher.place>
</item>
Author Affiliations
  1. The University of Hong Kong
  2. Southwest Jiaotong University
  3. Chengdu Public Security Bureau