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The Scientific World Journal
Volume 2013, Article ID 368568, 9 pages
Research Article

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

1Department of Computer Science, Jiangnan University, Wuxi, Jiangsu 214122, China
2Department of Mathematics, West Virginia University, Morgantown, WV 26505, USA
3School of Mathematics and Statistics, Shandong University at Weihai, Weihai 264209, China

Received 8 August 2013; Accepted 10 September 2013

Academic Editors: T. C. Chan and Y. Wei

Copyright © 2013 Qin Wu et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


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.