Fast graph clustering with a new description model for community detection

doi:10.1016/j.ins.2017.01.026

	Fast graph clustering with a new description model for community detection
	Bai, Liang 1,2,3; Cheng, Xueqi 2; Liang, Jiye 1; Guo, Yike 3
	2017-05-01
发表期刊	INFORMATION SCIENCES
ISSN	0020-0255
卷号	388 页码:37-47
摘要	Efficiently describing and discovering communities in a network is an important research concept for graph clustering. In the paper, we present a community description model that evaluates the local importance of a node in a community and its importance concentration in all communities to reflect its representability to the community. Based on the description model, we propose a new evaluation criterion and an iterative search algorithm for community detection (ISCD). The new algorithm can quickly discover communities in a large-scale network, due to the average linear-time complexity with the number of edges. Furthermore, we provide an initial method of input parameters including the number of communities and the initial partition before algorithm implementation, which can enhance the local-search quality of the iterative algorithm. The proposed algorithm with the initial method is called ISCD+. Finally, we compare the effectiveness and efficiency of the ISCD+ algorithm with six representative algorithms on several real network data sets. The experimental results illustrate that the proposed algorithm is suitable to address large-scale networks. (C) 2017 Elsevier Inc. All rights reserved.
关键词	Graph clustering Community detection Community description model Evaluation criterion Iterative algorithm
DOI	10.1016/j.ins.2017.01.026
收录类别	SCI
语种	英语
资助项目	National Natural Science Foundation of China[61305073] ; National Natural Science Foundation of China[61432011] ; National Natural Science Foundation of China[61472400] ; National Natural Science Foundation of China[61573229] ; National Natural Science Foundation of China[U1435212] ; National Key Basic Research and Development Program of China (973)[2013CB329404] ; National Key Basic Research and Development Program of China (973)[2014CB340400] ; Foundation of Doctoral Program Research of Ministry of Education of China[20131401120001] ; Technology Research Development Projects of Shanxi[2015021100] ; Scientific and Technological Innovation Programs of Higher Education Institutions in Shanxi[2014104] ; Scientific and Technological Innovation Programs of Higher Education Institutions in Shanxi[2015107]
WOS研究方向	Computer Science
WOS类目	Computer Science, Information Systems
WOS记录号	WOS:000394068100003
出版者	ELSEVIER SCIENCE INC
引用统计	被引频次：48[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://119.78.100.204/handle/2XEOYT63/7489
专题	中国科学院计算技术研究所期刊论文_英文
通讯作者	Bai, Liang
作者单位	1.Shanxi Univ, Sch Comp & Informat Technol, Taiyuan 030006, Shanxi, Peoples R China 2.Chinese Acad Sci, Inst Comp Technol, Beijing 100190, Peoples R China 3.Imperial Coll London, Dept Comp, London SW7, England
推荐引用方式 GB/T 7714	Bai, Liang,Cheng, Xueqi,Liang, Jiye,et al. Fast graph clustering with a new description model for community detection[J]. INFORMATION SCIENCES,2017,388:37-47.
APA	Bai, Liang,Cheng, Xueqi,Liang, Jiye,&Guo, Yike.(2017).Fast graph clustering with a new description model for community detection.INFORMATION SCIENCES,388,37-47.
MLA	Bai, Liang,et al."Fast graph clustering with a new description model for community detection".INFORMATION SCIENCES 388(2017):37-47.