Document Type
Article
Publication Date
2013
College/Unit
Eberly College of Arts and Sciences
Department/Program/Center
Mathematics
Abstract
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.
Digital Commons Citation
Wu, Qin; Qi, Xingqin; Fuller, Eddie; and Zhang, Cun-Quan, "“Follow the Leader”: A Centrality Guided Clustering and Its Application to Social Network Analysis" (2013). Faculty & Staff Scholarship. 2665.
https://researchrepository.wvu.edu/faculty_publications/2665
Source Citation
Wu, Q., Qi, X., Fuller, E., & Zhang, C.-Q. (2013). “Follow the Leader”: A Centrality Guided Clustering and Its Application to Social Network Analysis. The Scientific World Journal, 2013, 1–9. https://doi.org/10.1155/2013/368568
Comments
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.