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Conference Paper: The probabilistic maximum coverage problem in social networks

TitleThe probabilistic maximum coverage problem in social networks
Authors
KeywordsCollaboration network
Computational loads
Diffusion model
Maximum coverage
Social networks
Issue Date2011
PublisherIEEE. The Journal's web site is located at http://www.ieeexplore.ieee.org/xpl/conhome.jsp?punumber=1000308
Citation
Globecom - IEEE Global Telecommunications Conference, 2011 How to Cite?
AbstractIn this paper we consider the problem of maximizing information propagation in social networks. To solve it, we introduce a probabilistic maximum coverage problem, and further purpose a cluster-based heuristic and a neighborhood-removal heuristic for two basic diffusion models, namely, the Linear Threshold Model and the Independent Cascade Model, respectively. Our proposed strategies are compared with the pure greedy algorithm and centrality-based schemes via experiments on large collaboration networks. We find that our proposed algorithms perform better than centrality-based schemes and achieve approximately the same performance as the greedy algorithm. Moreover, the computational load is significantly reduced compared with the greedy heuristic. © 2011 IEEE.
Persistent Identifierhttp://hdl.handle.net/10722/158776
References

 

DC FieldValueLanguage
dc.contributor.authorFan, Xen_US
dc.contributor.authorLi, VOKen_US
dc.date.accessioned2012-08-08T09:01:16Z-
dc.date.available2012-08-08T09:01:16Z-
dc.date.issued2011en_US
dc.identifier.citationGlobecom - IEEE Global Telecommunications Conference, 2011en_US
dc.identifier.urihttp://hdl.handle.net/10722/158776-
dc.description.abstractIn this paper we consider the problem of maximizing information propagation in social networks. To solve it, we introduce a probabilistic maximum coverage problem, and further purpose a cluster-based heuristic and a neighborhood-removal heuristic for two basic diffusion models, namely, the Linear Threshold Model and the Independent Cascade Model, respectively. Our proposed strategies are compared with the pure greedy algorithm and centrality-based schemes via experiments on large collaboration networks. We find that our proposed algorithms perform better than centrality-based schemes and achieve approximately the same performance as the greedy algorithm. Moreover, the computational load is significantly reduced compared with the greedy heuristic. © 2011 IEEE.en_US
dc.languageengen_US
dc.publisherIEEE. The Journal's web site is located at http://www.ieeexplore.ieee.org/xpl/conhome.jsp?punumber=1000308-
dc.relation.ispartofGLOBECOM - IEEE Global Telecommunications Conferenceen_US
dc.rights©2011 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.-
dc.rightsCreative Commons: Attribution 3.0 Hong Kong License-
dc.subjectCollaboration network-
dc.subjectComputational loads-
dc.subjectDiffusion model-
dc.subjectMaximum coverage-
dc.subjectSocial networks-
dc.titleThe probabilistic maximum coverage problem in social networksen_US
dc.typeConference_Paperen_US
dc.identifier.emailLi, VOK:vli@eee.hku.hken_US
dc.identifier.authorityLi, VOK=rp00150en_US
dc.description.naturepublished_or_final_versionen_US
dc.identifier.doi10.1109/GLOCOM.2011.6133985en_US
dc.identifier.scopuseid_2-s2.0-84857214285en_US
dc.identifier.hkuros210672-
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-84857214285&selection=ref&src=s&origin=recordpageen_US
dc.description.otherProceedings of the IEEE Global Telecommunications Conference (GLOBECOM 2011), Houston, TX, USA, 5-9 December 2011-
dc.identifier.scopusauthoridFan, X=40561113100en_US
dc.identifier.scopusauthoridLi, VOK=7202621685en_US

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