A Social Network Analysis Approach to Explore the Dualistic Interaction between the Digital Economy and Industrial Economics

Abstract

In recent years, the digital economy has continuously innovated the traditional industrial economy and brought about the continuous optimization of economic structure. In recent years, the digital economy has continuously innovated the traditional industrial economy and brought about continuous optimization of economic structure. By combining the AP clustering algorithm, this paper creates a representative viewpoint extraction model that extracts the relevant features of relevant variables and generates a more representative economic viewpoint. The social network structure between the digital economy and industrial economics is analyzed using the social relationship graph. Using the information gain calculation formula, the correlation calculation is carried out on the characteristics of variables with stronger relationships and larger information gain. Meanwhile, the correlation coefficient between variables with larger information gain is calculated by Spearman’s coefficient. The influencing factors of industrial economic development under the digital economy are analyzed through three dimensions, and the theoretical model of group effect analysis is constructed to analyze the binary interaction between the two. Through the method of empirical analysis, the interaction between the digital economy and the industrial economy is tested in terms of smoothness and cointegration. HP filtering, as well as impulse response analysis, are carried out to analyze the interaction between the two in A. After 9 groups of experiments, the cointegration number of the original hypothesis is 0 or ≥1, the original hypothesis is rejected at a significance level of 1%, and the eigenvalues range from 67.4566 to 69.7445, and there is a correlation between the two. The impulse response value of the industrial economy improves from 0.0002 to 0.001. The development of the digital economy will lead to the development of the industrial economy.

Keywords

• ap clustering
• social network analysis
• spearman’s coefficient
• group effect analysis
• binary interactions
• 93c62