Population Cross Learning Algorithm Combining Greedy Search for Community Detection

In order to improve the precision of modularity optimization and community detection, this paper presented a complex network community detection algorithm based on cross learning among individuals of population combining greedy search. Individuals' codes indicated community partition. Individuals comparatively studied with each other to spread good genes and optimize modularity fast. Besides, aiming at improving the algorithm, the best communities, where some randomly selected nodes will move in, would be found by using greedy search maximizing the local modularity increment. The algorithm was tested on artificial networks and some typical real networks, compared with some typical algorithms. The results show that algorithm can get convergence quickly, achieve better modularity value, and finely detect and identify community structures.