[1]赵姝,赵晖,陈洁,等.基于社团结构的多粒度结构洞占据者发现及分析[J].智能系统学报编辑部,2016,11(3):343-351.[doi:10.11992/tis.201603048]
　ZHAO Shu,ZHAO Hui,CHEN Jie,et al.Recognition and analysis of structural hole spanner in multi-granularitybased on community structure[J].CAAI Transactions on Intelligent Systems,2016,11(3):343-351.[doi:10.11992/tis.201603048]
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基于社团结构的多粒度结构洞占据者发现及分析

《智能系统学报》编辑部[ISSN 1673-4785/CN 23-1538/TP] 卷: 11 期数: 2016年第3期 页码: 343-351 栏目: 学术论文—知识工程 出版日期: 2016-06-25
Title:
Recognition and analysis of structural hole spanner in multi-granularitybased on community structure
作者:
赵姝1,2, 赵晖1,2, 陈洁1,2, 陈喜1,2, 张燕平1,2
1. 安徽大学 计算机科学与技术学院, 安徽 合肥 230601;
2. 安徽大学 信息保障技术协同创新中心, 安徽 合肥 230601
Author(s):
ZHAO Shu1,2, ZHAO Hui1,2, CHEN Jie1,2, CHEN Xi1,2, ZHANG Yanping1,2
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
关键词:
结构洞; 社团结构; 多粒度; 层次结构; 社团划分; 分层网络; 网络结构; 社会网络分析
Keywords:
tructural hole; community structure; multi-granularity; hierarchical structure; community division; hierarchical networks; network structure; social network analysis
分类号:
TP393
DOI:
10.11992/tis.201603048
摘要:
目前研究者已提出一些基于社团结构的结构洞发现方法,然而不同粒度下社团划分结果使网络呈现层次化结构,影响社团结构中节点跨越结构洞的程度。本文基于网络社团划分思想提出一种分层网络的结构洞发现方法MG_MaxD。首先,使用分层递阶社团划分算法(本文使用EAGLE算法),得到不同粒度的社团结构;然后,使用结构洞发现算法MG_MaxD得到不同粒度下的结构洞占据者;最后,使用结构洞跨越程度指标分析不同粒度下的社团结构对节点跨越结构洞程度的影响。在公用和真实数据集上的实验结果表明节点跨越结构洞的程度即结构洞节点的优势将随着粒度的变细而增大。
Abstract:
Recently, more and more attentions have been paid to research of structural holes, and some methods have been proposed to identify the structural holes based on the community structure. However, the network indicates a hierarchical structure after dividing into communities in different granularity, and influences the nodes’ extent to span structural holes in community structure. A structural hole spanners mining algorithm, named MG_MaxD, is proposed which is in a hierarchical network based on the idea of network community division. First,different granular communities are partitioned by using hierarchical community dividing algorithm (such as EAGLE in this paper). Then, structural hole spanners mining algorithm MG_MaxD is used to identifying the structural hole spanners in each granularity. Finally, using the measurement of the extent of node spanning structural holes to analysis the effect of community structure under different granularity that influence the node’s extent to span structural holes. Experimental results on public data and real data indicate that the extent of nodes to span structural holes namely the node’s advantages will increase with the granularity get thinner.
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备注/Memo

收稿日期:2016-3-20;改回日期:。
基金项目:国家高技术研究发展计划项目(2015AA124102);国家自然科学基金项目(61402006、61175046);安徽省高等学校省级自然科学研究项目(KJ2013A016);安徽省自然科学基金项目(1508085MF113);教育部留学回国人员科研启动基金项目.
作者简介:赵姝,女,1979年生,教授,主要研究方向为机器学习、社交网络、智能计算。主持国家自然科学基金、省部级项目等多项。已授权专利1项,获软件著作权3项,发表学术论文20余篇。赵晖,男,1992年生,硕士研究生,主要研究方向为社交网络。陈洁,女,1982年生,博士研究生,主要研究方向为智能计算。
通讯作者:赵姝.E-mail:gongxs7@163.com.

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