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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 如果检索请告诉我wos号，谢谢。 |
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baiyuefei
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风雪
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微型控制器: 金币+20, ★★★★★最佳答案, 谢谢 2014-09-10 11:42:58
oven1986: LS-EPI+1, 感谢应助！ 2014-09-10 12:54:56
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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® 备注和修正 其他信息 分主题子库:Index Medicus 记录所有者:NLM 状态:In-Data-Review 引文网络 0 被引频次 0 引用的参考文献 创建引文跟踪 (数据来自 Web of ScienceTM 核心合集) 查看 PubMed 相关论文 全部被引频次计数 0 / 所有数据库 0 / Web of Science 核心合集 0 / BIOSIS Citation Index 0 / 中国科学引文数据库 0 / Data Citation Index 0 / SciELO Citation Index 此记录来自: MEDLINE® 建议修正 如果希望提高此记录中数据的质量，请提供修正建议。 |
3楼2014-09-10 11:06:44
baiyuefei
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2楼2014-09-10 11:06:32
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