A way of finding networks communities with vector partitioning
Lidong Fu
Abstract
Lidong Fu
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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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