Community Detection Based on Modularity Density and Genetic Algorithm

Detecting and characterizing the community structure of complex network and social network is fundamental problem. Many of the proposed algorithm for detecting community based on modularity Q which fail to identify modules smaller than a scale community. In this paper, authors propose a new community detection algorithm based on genetic algorithm and modularity density (D value). We test our method on classical social networks whose community structure is already known and the results can be much easier compared with the method. Experiments show the capability of the method to successfully detect the community structure.

[1]  S. Fortunato,et al.  Resolution limit in community detection , 2006, Proceedings of the National Academy of Sciences.

[2]  Luonan Chen,et al.  Quantitative function for community detection. , 2008 .

[3]  Claudio Castellano,et al.  Defining and identifying communities in networks. , 2003, Proceedings of the National Academy of Sciences of the United States of America.

[4]  Norman P. Hummon,et al.  Connectivity in a citation network: The development of DNA theory☆ , 1989 .

[5]  Ying Wang,et al.  Quantitative Function for Community Detection , 2012, Physical review. E, Statistical, nonlinear, and soft matter physics.

[6]  M E J Newman,et al.  Community structure in social and biological networks , 2001, Proceedings of the National Academy of Sciences of the United States of America.

[7]  W. Zachary,et al.  An Information Flow Model for Conflict and Fission in Small Groups , 1977, Journal of Anthropological Research.