2010Journal of Xi'an University of Science and TechnologyRequires access

A way of finding networks communities with vector partitioning

Lidong Fu

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Abstract

To detect community structure in complex network effectively,modularity density function(D value) is optimized.By optimizing process,how the D function optimizing can be reformulated as a vector partitioning approach is shown and a new vector partitioning algorithm is proposed.The approach is illustrated by using a real world network.Experimental results indicate that the new algorithms are efficient for finding community structures in complex networks.

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What this paper is about

To detect community structure in complex network effectively,modularity density function(D value) is optimized.By optimizing process,how the D function optimizing can be reformulated as a vector partitioning approach is shown and a new vector partitioning algorithm is proposed.The approach is illustrated by using a real world network.Experimental results indicate that the new algorithms are efficient for finding community structures in complex networks.

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Available abstract

To detect community structure in complex network effectively,modularity density function(D value) is optimized.By optimizing process,how the D function optimizing can be reformulated as a vector partitioning approach is shown and a new vector partitioning algorithm is proposed.The approach is illustrated by using a real world network.Experimental results indicate that the new algorithms are efficient for finding community structures in complex networks.

Key concepts: Modularity (biology), Community structure, Computer science, Complex network, Function (biology), Process (computing), Clique percolation method, Data mining

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