Community Detection Algorithms Combining Improved Differential Evolution and Modularity Density

Abstract

Community detection is the foundation and core of research in the fields of personalized recommendation, group feature collection and social network analysis. However, existing community detection algorithms generally have some problems for dealing with increasingly complex social networks, such as low accuracy, slow convergence rate and limited modularity resolution. The idea of differential evolution and modularity density is introduced into community detection, and a community detection algorithm combining improved differential evolution and modularity density is proposed. Firstly, the algorithm adjusts the mutation strategy and parameters of differential evolution, and then takes the modularity density as the fitness function to overcome the limitation of the modularity resolution, and then corrects the operation according to the community structure to improve the individual quality in the population and accelerate the global convergence. Finally, the proposed method is compared with other popular community detection algorithms on computer generated networks and 5 representative real world networks. The experimental results show that the proposed algorithm has higher accuracy and better convergence performance.

Keywords

• community detection
• social network
• differential evolution
• modularity density
• mutation strategy