Refining Graph Partitioning for Community Detection

Qian Tieyun; Yang Yang; Wang Shuo

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Refining Graph Partitioning for Community Detection

Qian Tieyun 1 * #,Yang Yang 2,Wang Shuo 2

1.State Key Laboratory of Software Engineering, Wuhan University

2.International School of Software, Wuhan University

*Correspondence author

#Submitted by

Subject:

Funding: Specialized Research Fund for the Doctoral Program of Higher Education, China （No.20090141120050）

Opened online: 9 October 2010

Accepted by: none

Citation: Qian Tieyun,Yang Yang,Wang Shuo.Refining Graph Partitioning for Community Detection[OL]. [ 9 October 2010] http://en.paper.edu.cn/en_releasepaper/content/4387034

Graph partitioning is traditionally designed for dividing the vertices into clusters such that a predefined quality function, for example, normalized cut or ratio cut, is approximately optimized. Recently, demand for social network analysis arouses the new research interest on graph clustering with no constraint on the size of partitions. Social network exhibits some key properties such as power-law and small-world. This paper presents a new definition for measuring the small world weight between two vertices, then a refinement strategy is designed for improving results obtained by traditional graph partitioning algorithm. Experimental evaluation demonstrates that the proposed algorithm can effectively enhance the objective functions.

Keywords:Computer science and theory; graph partitioning; community detection

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