Genetic algorithm optimizing modularity density for community detection

Identification and detection of the community structure is fundamental and important problem for the analysis of complex network.To detect community structure precisely,a new community detection algorithm was designed based on genetic algorithm and modularity density D.The algorithm does not need any prior knowledge about the number of communities,requires no arbitrary convergence or abruption criteria,and can generally find the global optimal solution.Genetic algorithm for detecting communities in complex networks,based on optimizing network modularity density was presented here.The algorithm was illustrated and compared with GN algorithm by using classic real world networks.Optimizing modularity density D not only can resolve detail modules but also can correctly identify the number of communities.Experimental results show the method can reveal community structure more precisely than traditional approaches.According to the definition of community in a strong sense,the nodes in experimental result which have more connections within the community than with the rest of the graph are more than the other partitions.