Research on Modular Method of Complex Network Structure Based on Probability Distribution

The complex network node and a large number of data constantly changing complex network structure of the modular method, previously proposed mostly lack of universality and linear, cannot effectively improve network performance index engine. In this paper, a method of complex network structure based on probability distribution is proposed. The modular structure of complex network has an exclusive process, which is divided into two steps: module variable allocation and structured index. In the module variable assignment, the proposed method constructs a probability distribution model of complex network module, and obtains the model learning sample by Gibbs sampling. The sample is monitored to obtain the real solution of the conditional probability function. The number of real solutions is represented by the number of real solutions. In structured index, the proposed method uses the LDA topic model to determine whether or not the modular complex network is established. The experimental results show that the modular complex network constructed by the method has strong linear.