|本期目录/Table of Contents|

[1]李 凡,赵 姝*,陈 洁,等.基于加权中介中心性的结构洞占据者方法获取[J].南京大学学报(自然科学),2017,53(4):756.[doi:10.13232/j.cnki.jnju.2017.04.019]
 Li Fan,Zhao Shu*,Chen Jie,et al.Mining structural hole spanners based on weighted betweenness centrality[J].Journal of Nanjing University(Natural Sciences),2017,53(4):756.[doi:10.13232/j.cnki.jnju.2017.04.019]





Mining structural hole spanners based on weighted betweenness centrality
李 凡12赵 姝12*陈 洁12张燕平12
Li Fan12Zhao Shu12*Chen Jie12Zhang Yanping12
1.School of Computer Science and Technology,Anhui University,Hefei,230601,China; 2.Center of Information Support and Assurance Technology,Anhui University,Hefei,230601,China
social networkstructural holebetweeness centralityweighted networkconstraint
With the development of various kinds of networks and computer science,the idea of what kind of position in the network could obtain the profits has been concerned by many people.The structural hole spanners may have access to abundant information and significant benefits.More and more attention has been paid on mining structural hole spanners.At present,researches on mining structural hole spanners are mainly for unweighted network.Actually,in a real network,the edge weight has a great influence on network performance.This paper proposes a novel weighted betweenness centrality algorithm to discover the structural hole spanners,named W_BCHIS,based on the existing algorithm HIS,for the weighted network.The algorithm W_BCHIS introduces the edge weight into the betweeness centrality function.This method first uses the discovery community algorithm to detect the weighted network community.Then the edge weight is introduced into the betweeness centrality function to initialize the importance of nodes.Two functions to define the importance of nodes and the possibility of occupying the structural holes are calculated iteratly until they are converged.Finally,experimental results on the public datasets and real datasets show that the proposed algorithm W_BCHIS is better than the-state-of-art algorithms.


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基金项目:国家自然科学基金(61402006,61602003),安徽省自然科学基金(1508085MF113,1708085QF156,1708085MF163),安徽省高等学校省级自然科学基金重点项目(KJ2016A016),教育部留学回国人员科研启动基金(第49批) 收稿日期:2017-06-06 *通讯联系人,E-mail:zhaoshuzs2002@hotmail.com
更新日期/Last Update: 2017-08-02