A self-organizing community detection algorithm for complex networks

Abstract

Complex network is a kind of system structure, which widely exists in human society and nature. It can be used to capture and describe the evolution law, evolution mechanism, and dynamic behaviors. We study the model of entity growth in complex networks, achieve the single node growth model, block growth model and degree of communication difficulty based growth model, then carry out the theoretical analysis and experimental simulation, it is concluded that the entity growth model holds the characteristics of high robustness, high clustering coefficient and low average path. According to the growth model, this paper analyzes the basic idea and implementation process of the self-organizing community discovery algorithm based on information entropy, experimental results show that it is structurally reasonable and has important significance in practical application.