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	FVBM:A Filter-Verification-Based Method for Finding Top-k Closeness Centrality on Dynamic Social Networks
	Yiyong Lin ; Jinbo Zhang ; Yuanxiang Ying ; Shenda Hong ; Hongyan Li
	2016
关键词	Closeness centrality Filter-Verification Dynamic social network
英文摘要	Closeness centrality is often used to identify the top-k most prominent nodes in a network.Real networks,however,are rapidly evolving all the time,which results in the previous methods hard to adapt.A more scalable method that can immediately react to the dynamic network is demanding.In this paper,we endeavour to propose a filter and verification framework to handle such new trends in the large-scale network.We adopt several pruning methods to generate a much smaller candidate set so that bring down the number of necessary time-consuming calculations.Then we do verification on the subset;which is a much time efficient manner.To further speed up the filter procedure,we incremental update the influenced part of the data structure.Extensive experiments using real networks demonstrate its high scalability and efficiency.; 389-392
语种	英语
出处	International Asia-Pacific Web Conference(第18届国际亚太互联网大会)论文集苏州大学
内容类型	其他
源URL	[http://ir.pku.edu.cn/handle/20.500.11897/462473]
专题	信息科学技术学院
推荐引用方式 GB/T 7714	Yiyong Lin,Jinbo Zhang,Yuanxiang Ying,et al. FVBM:A Filter-Verification-Based Method for Finding Top-k Closeness Centrality on Dynamic Social Networks. 2016-01-01.

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