Research on Social Network Inference Method Based on ConNIe Algorithm

In recent years, Internet technology and online social networks have developed rapidly, enabling people to express their opinions, ideas, emotional exchanges and economic exchanges randomly. Inferences about social networks are made possible by observational data exchanged by people on the Internet. In this paper, through the analysis of ConNIe algorithm, the effects of sparse parameter, propagation time distribution model and its parameters on the inferred results of this algorithm are studied. Then, based on the research, this paper use perceptron algorithm to classify the propagation time distribution model and use particle swarm optimization algorithm to optimize the sparse parameter and the parameters of propagation time distribution model. Finally, a social network inference framework based on ConNIe algorithm is proposed to make up for ConNIe. Some of the shortcomings of the algorithm have gotten over. The research in this paper helps people to understand the social network itself, and it has a wide range of practical value in the fields of social public opinion control and marketing.