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题目 Detecting Community Structures in Networks by Label Propagation with Prediction of Percolation Transition
作者 Aiping Zhang, Guang Ren, Yejin Lin, Baozhu Jia, Hui Cao, Jundong Zhang, and Shubin Zhang
期刊 Scientific World Journal
卷期 Volume 2014 (2014), Article ID 148686, 14 pages
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baiyuefei

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jssxh: 金币+5, LS-EPI+1, 谢谢参与，请继续关注本版块！ 2014-09-04 18:07:10

Detecting community structures in networks by label propagation with prediction of percolation transition.

作者:Zhang, Aiping; Ren, Guang; Lin, Yejin; Jia, Baozhu; Cao, Hui; Zhang, Jundong; Zhang, Shubin

TheScientificWorldJournal

卷:2014

页:148686

DOI:10.1155/2014/148686

出版年:2014 (Epub 2014 Jul 07)

摘要

Though label propagation algorithm (LPA) is one of the fastest algorithms for community detection in complex networks, the problem of trivial solutions frequently occurring in the algorithm affects its performance. We propose a label propagation algorithm with prediction of percolation transition (LPAp). After analyzing the reason for multiple solutions of LPA, by transforming the process of community detection into network construction process, a trivial solution in label propagation is considered as a giant component in the percolation transition. We add a prediction process of percolation transition in label propagation to delay the occurrence of trivial solutions, which makes small communities easier to be found. We also give an incomplete update condition which considers both neighbor purity and the contribution of small degree vertices to community detection to reduce the computation time of LPAp. Numerical tests are conducted. Experimental results on synthetic networks and real-world networks show that the LPAp is more accurate, more sensitive to small community, and has the ability to identify a single community structure. Moreover, LPAp with the incomplete update process can use less computation time than LPA, nearly without modularity loss.

作者信息

地址:College of Marine Engineering, Dalian Maritime University, Dalian 116026, China.

文献信息

文献类型:Journal Article

语种:English

PubMed ID:25110725

NLM 唯一 ID:101131163

创建日期: 11 Aug 2014

电子出版:07 Jul 2014

国家/地区: United States

ISSN:1537-744X

期刊信息

Impact Factor (影响因子): Journal Citation Reports®

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其他信息

分主题子库:Index Medicus

记录所有者:NLM

状态:In-Data-Review

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