Journal
SCIENTIFIC WORLD JOURNAL
Volume -, Issue -, Pages -Publisher
HINDAWI LTD
DOI: 10.1155/2013/368568
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Funding
- National Natural Science Foundation of China [61202312]
- NSA [H98230-12-1-0233]
- NSF [DMS-1264800]
- Fundamental Research Funds for the Central Universities, China [JUSRP11231]
- Shandong Province Natural Science Foundation of China [ZR2010AQ018]
- Division Of Mathematical Sciences
- Direct For Mathematical & Physical Scien [1264800] Funding Source: National Science Foundation
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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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