“Follow the Leader”: A Centrality Guided Clustering and Its Application to Social Network Analysis

Within graph theory and network analysis, centrality of a vertex measures the relative importance of a vertex within a graph. The centrality plays key role in network analysis and has been widely studied using different methods. Inspired by the idea of vertex centrality, a novel centrality guided cl...

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Main Authors: Qin Wu, Xingqin Qi, Eddie Fuller, Cun-Quan Zhang
Format: Article
Language:English
Published: Wiley 2013-01-01
Series:The Scientific World Journal
Online Access:http://dx.doi.org/10.1155/2013/368568
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author Qin Wu
Xingqin Qi
Eddie Fuller
Cun-Quan Zhang
author_facet Qin Wu
Xingqin Qi
Eddie Fuller
Cun-Quan Zhang
author_sort Qin Wu
collection DOAJ
description Within graph theory and network analysis, centrality of a vertex measures the relative importance of a vertex within a graph. The centrality plays key role in network analysis and has been widely studied using different methods. Inspired by the idea of vertex centrality, a novel centrality guided clustering (CGC) is proposed in this paper. Different from traditional clustering methods which usually choose the initial center of a cluster randomly, the CGC clustering algorithm starts from a “LEADER”—a vertex with the highest centrality score—and a new “member” is added into the same cluster as the “LEADER” when some criterion is satisfied. The CGC algorithm also supports overlapping membership. Experiments on three benchmark social network data sets are presented and the results indicate that the proposed CGC algorithm works well in social network clustering.
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institution Kabale University
issn 1537-744X
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publishDate 2013-01-01
publisher Wiley
record_format Article
series The Scientific World Journal
spelling doaj-art-0710458524d642faaca2bbe285be0df82025-02-03T01:11:00ZengWileyThe Scientific World Journal1537-744X2013-01-01201310.1155/2013/368568368568“Follow the Leader”: A Centrality Guided Clustering and Its Application to Social Network AnalysisQin Wu0Xingqin Qi1Eddie Fuller2Cun-Quan Zhang3Department of Computer Science, Jiangnan University, Wuxi, Jiangsu 214122, ChinaDepartment of Mathematics, West Virginia University, Morgantown, WV 26505, USADepartment of Mathematics, West Virginia University, Morgantown, WV 26505, USADepartment of Mathematics, West Virginia University, Morgantown, WV 26505, USAWithin graph theory and network analysis, centrality of a vertex measures the relative importance of a vertex within a graph. The centrality plays key role in network analysis and has been widely studied using different methods. Inspired by the idea of vertex centrality, a novel centrality guided clustering (CGC) is proposed in this paper. Different from traditional clustering methods which usually choose the initial center of a cluster randomly, the CGC clustering algorithm starts from a “LEADER”—a vertex with the highest centrality score—and a new “member” is added into the same cluster as the “LEADER” when some criterion is satisfied. The CGC algorithm also supports overlapping membership. Experiments on three benchmark social network data sets are presented and the results indicate that the proposed CGC algorithm works well in social network clustering.http://dx.doi.org/10.1155/2013/368568
spellingShingle Qin Wu
Xingqin Qi
Eddie Fuller
Cun-Quan Zhang
“Follow the Leader”: A Centrality Guided Clustering and Its Application to Social Network Analysis
The Scientific World Journal
title “Follow the Leader”: A Centrality Guided Clustering and Its Application to Social Network Analysis
title_full “Follow the Leader”: A Centrality Guided Clustering and Its Application to Social Network Analysis
title_fullStr “Follow the Leader”: A Centrality Guided Clustering and Its Application to Social Network Analysis
title_full_unstemmed “Follow the Leader”: A Centrality Guided Clustering and Its Application to Social Network Analysis
title_short “Follow the Leader”: A Centrality Guided Clustering and Its Application to Social Network Analysis
title_sort follow the leader a centrality guided clustering and its application to social network analysis
url http://dx.doi.org/10.1155/2013/368568
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AT xingqinqi followtheleaderacentralityguidedclusteringanditsapplicationtosocialnetworkanalysis
AT eddiefuller followtheleaderacentralityguidedclusteringanditsapplicationtosocialnetworkanalysis
AT cunquanzhang followtheleaderacentralityguidedclusteringanditsapplicationtosocialnetworkanalysis