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Fast graph clustering with a new description model for community detection

文献类型：期刊论文

作者Bai, Liang1,2,3; Cheng, Xueqi1; Liang, Jiye2; Guo, Yike3
刊名INFORMATION SCIENCES
出版日期2017-05-01
卷号388页码:37-47
关键词Graph clustering Community detection Community description model Evaluation criterion Iterative algorithm
ISSN号0020-0255
DOI10.1016/j.ins.2017.01.026
英文摘要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.
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
语种英语
WOS记录号WOS:000394068100003
出版者ELSEVIER SCIENCE INC
源URL[http://119.78.100.204/handle/2XEOYT63/7488]  
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.Chinese Acad Sci, Inst Comp Technol, Beijing 100190, Peoples R China
2.Shanxi Univ, Sch Comp & Informat Technol, Taiyuan 030006, Shanxi, 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.

入库方式： OAI收割

来源：计算技术研究所

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