An Improved Artificial Bee Colony Algorithm for Community Detection in Bipartite Networks

In the past few decades, although people have conducted in-depth research on community detection in one-mode networks, bipartite networks has not been extensively researched. this paper, we propose an improved artificial bee colony algorithm named IABC-BN, which is used to detect communities two-mode graphs (i.e. graphs) with two kinds of vertices cluster community). Firstly, paper proposed a novel population initialization process (ABC) method for graph identification. This can improve diversity initial ABC and speed up its convergence rate. Secondly, employed search step algorithm, new combinatorial equation proposed. guided by global optimal solution better neighbour current solution. By using combination increased parameter perturbation frequency, exploitation ability further enhanced. Thirdly, onlooker bees step, another also improves level opposition based studying promote algorithm. Lastly, scout stage, probability threshold $\beta $ introduced enhance exploration To our knowledge, IABC-BN presented first identification cluster. For verifying accuracy results method, large number experiments are carried out making use synthetic real graphs. The test outcomes show that excellent discovery graph.