title: "Follow the leader": a centrality guided clustering and its application to social network analysis.
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.
authors.affiliation: Department of Computer Science, Jiangnan University, Wuxi, Jiangsu 214122, China ; Department of Mathematics, West Virginia University, Morgantown, WV 26505, USA.
authors.forename: Qin
authors.initials: Q
authors.lastname: Wu
authors.forename: Xingqin
authors.initials: X
authors.lastname: Qi
authors.forename: Eddie
authors.initials: E
authors.lastname: Fuller
authors.forename: Cun-Quan
authors.initials: CQ
authors.lastname: Zhang
mesh_terms: D000465:Algorithms; D016000:Cluster Analysis; D003198:Computer Simulation; D005716:Game Theory; D006801:Humans; D007857:Leadership; D008962:Models, Theoretical; D011157:Population Dynamics; D012944:Social Support