Distributed parallel deep learning with a hybrid backpropagation-particle swarm optimization for community detection in large complex networks

In this paper, a parallel deep learning-based community detection method in large complex networks (CNs) is proposed. First, a CN partitioning method is employed to divide the CN into multiple chunks to improve the efficiency in terms of space and time complexities. Next, the method is integrated with two optimization algorithms: (1) backpropagation (BP), which optimizes deep learning locally within each local chunk of the CN; (2) particle swarm optimization (PSO), which is used to improve the BP optimization involving all CN chunks. PSO utilizes a multi-objective function to improve the effectiveness of the proposed method. In addition, a distributed environment is set up to conduct parallel optimization of the proposed method so that multi-local optimizations could be performed simultaneously. A set of 16 real-world CNs in a range from small to large size are used to verify the effectiveness and efficiency of the method in a benchmark study. The proposed method is implemented in multi-machines with central processing unit (CPU) and graphics processing unit (GPU) devices. The results reveal the effective role of the proposed deep learning with hybrid BP–PSO optimization in detecting communities in large CNs, which requires minimum execution time on both CPU and GPU devices.