[1]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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Recognition and analysis of structural hole spanner in multi-granularitybased on community structure

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 11 Number of periods: 2016 3 Page number: 343-351 Column: 学术论文—知识工程 Public date: 2016-06-25
Title:
Recognition and analysis of structural hole spanner in multi-granularitybased on community structure
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
Keywords:
tructural hole; community structure; multi-granularity; hierarchical structure; community division; hierarchical networks; network structure; social network analysis
CLC:
TP393
DOI:
10.11992/tis.201603048
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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