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dc.contributor.authorChen, Yi-Chengen_US
dc.contributor.authorZhu, Wen-Yuanen_US
dc.contributor.authorPeng, Wen-Chihen_US
dc.contributor.authorLee, Wang-Chienen_US
dc.contributor.authorLee, Suh-Yinen_US
dc.date.accessioned2014-12-08T15:36:09Z-
dc.date.available2014-12-08T15:36:09Z-
dc.date.issued2014-04-01en_US
dc.identifier.issn2157-6904en_US
dc.identifier.urihttp://dx.doi.org/10.1145/2532549en_US
dc.identifier.urihttp://hdl.handle.net/11536/24493-
dc.description.abstractGiven a social graph, the problem of influence maximization is to determine a set of nodes that maximizes the spread of influences. While some recent research has studied the problem of influence maximization, these works are generally too time consuming for practical use in a large-scale social network. In this article, we develop a new framework, community-based influence maximization (CIM), to tackle the influence maximization problem with an emphasis on the time efficiency issue. Our proposed framework, CIM, comprises three phases: (i) community detection, (ii) candidate generation, and (iii) seed selection. Specifically, phase (i) discovers the community structure of the network; phase (ii) uses the information of communities to narrow down the possible seed candidates; and phase (iii) finalizes the seed nodes from the candidate set. By exploiting the properties of the community structures, we are able to avoid overlapped information and thus efficiently select the number of seeds to maximize information spreads. The experimental results on both synthetic and real datasets show that the proposed CIM algorithm significantly outperforms the state-of-the-art algorithms in terms of efficiency and scalability, with almost no compromise of effectiveness.en_US
dc.language.isoen_USen_US
dc.subjectAlgorithmsen_US
dc.subjectTheoryen_US
dc.subjectMeasurementen_US
dc.subjectCommunity detectionen_US
dc.subjectdiffusion modelsen_US
dc.subjectinfluence maximizationen_US
dc.subjectsocial network analysisen_US
dc.titleCIM: Community-Based Influence Maximization in Social Networksen_US
dc.typeArticleen_US
dc.identifier.doi10.1145/2532549en_US
dc.identifier.journalACM TRANSACTIONS ON INTELLIGENT SYSTEMS AND TECHNOLOGYen_US
dc.citation.volume5en_US
dc.citation.issue2en_US
dc.citation.epageen_US
dc.contributor.department資訊工程學系zh_TW
dc.contributor.departmentDepartment of Computer Scienceen_US
dc.identifier.wosnumberWOS:000335576200004-
dc.citation.woscount1-
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