Discovering Overlapping Communities by Clustering Local Link Structures

查看全文 作　　者：TAO [1]Haicheng;WANG [1]Youquan;WU Zhi'[2]ang;BU [2]Zhan;CAO [1,2]Jie

机构地区：[1]College of Computer Sci.and Eng.,Nanjing University of Science and Technology;[2]School of Information Engineering,Nanjing University of Finance and Economics

出　　处：《Chinese Journal of Electronics》索引2017年第26卷第2期,共5页

基　　金：supported by the National Natural Science Foundation of China(No.71571093,No.71372188,No.61502222);National Center for International Joint Research on E-Business Information Processing(No.2013B01035);National Key Technologies R&D Program of China(No.2013BAH16F03) 摘　　要：Recent advances point out that the existing community detection methods commonly face two challenges:incorrect base-structures and incorrect membership of weak-ties.To overcome both problems,a Local link structure(LLS) clustering based method for overlapping community detection is proposed.We extend the similarity of a pair of links to a group of links named LLS,and thus transform mining LLSs as a pattern mining problem.We prove that LLS with an appropriate threshold can filter weak-ties in the form of bridge and local bridge with its span being larger than 3.A compositive framework is presented for overlapping community detection based on LLS mining and clustering.Comparative experiments on both synthetical and real-world networks demonstrate that our method has advantage over six existing methods on discovering higher quality communities. 关键词：社区链路结构链接结构模式挖掘学习策略阈值滤波基于策略比较实验

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Discovering Overlapping Communities by Clustering Local Link Structures

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