Research on the Interaction between Information and Behavior Based on Small Group Effect on Multilayer Social Networks

Abstract

In the era of big data, massive amounts of information play an important role in individual behavior and decision-making. In order to investigate the interaction mechanism between information and individual behavior, we consider the influence of the "small group" network structure in social networks, and construct an information-behavior coupled dynamics propagation model (UAL-NBN) based on small group effect. Then we carry out theoretical analysis and derive the dynamic evolution equations for the model used the Micro Markov Chain Approach (MMCA). And we verify the correctness of the theoretical analysis by performing Monte Carlo simulations (MC). The results show that the small group effect does promote the spread of information and behavior in the population, which is reflected in reducing the epidemic threshold and increasing the outbreak size. In addition, we also conclude that the more small group structures exist in social networks, the more significant the promotion effect of the small group effect is. Finally, we describe the specific application of the model in scenarios such as epidemic control, rumor governance, social behavior advocacy, and consumer marketing, and provide theoretical reference and suggestions for the government and other relevant departments to formulate policies which promote the spread of behavior in society through information dissemination.