Wu Qin, Qi Xingqin, Fuller Eddie, Zhang Cun-Quan
Department of Computer Science, Jiangnan University, Wuxi, Jiangsu 214122, China ; Department of Mathematics, West Virginia University, Morgantown, WV 26505, USA.
ScientificWorldJournal. 2013 Oct 24;2013:368568. doi: 10.1155/2013/368568. eCollection 2013.
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.
在图论和网络分析中，顶点的中心性衡量了一个顶点在图中的相对重要性。中心性在网络分析中起着关键作用，并且已经使用不同的方法进行了广泛的研究。受顶点中心性思想的启发，本文提出了一种新颖的中心性引导聚类（CGC）方法。与传统聚类方法通常随机选择聚类的初始中心不同，CGC聚类算法从一个“领导者”——具有最高中心性得分的顶点——开始，当满足某些标准时，一个新的“成员”被添加到与“领导者”相同的聚类中。CGC算法还支持重叠成员关系。文中给出了在三个基准社交网络数据集上的实验，结果表明所提出的CGC算法在社交网络聚类中表现良好。
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