An Improved Genetic Algorithm Based on Local Modularity for Community Detection in Complex Network

Community detection has been an issue in complex network research. In the paper, according to the definition of weak community, we firstly propose a local modularity and then design a new mutation operator with better efficiency based on local modularity. The mutation operator selects the neighbor node that can best embody the definition of weak community structures as mutated result, which makes the mutated candidate solution closer to the optimal solution. Furthermore, to accelerate the emergence of the optimal solution, the roulette selection is integrated into a uniform crossover operator. On the basis of the above works, an improved Genetic Algorithm based on the local modularity (IGALM) is presented for Community detection. The proposed algorithm is tested and compared to the other algorithms on both computer-generated network and real-world networks. The comparative experimental results reflect that the new algorithm is feasible and effective in small and large scale complex networks.