Opinion evolution in adaptive complex networks

Abstract

With the rapid development of information technology, information dissemination becomes more convenient, which makes public opinion events in the society happen frequently and become the focus. The report is written to study the process of opinion diversity and analyse the dynamic evolution relationship between the network structure and opinions to reveal the inherent law, which has very important practical significance. Based on the research of domestic and foreign scholars on complex networks theory and opinion dynamics, this paper uses computer simulation, theoretical modeling and analysis as methodology to reasonably abstract the interaction mode and behavior state between individuals in the network, and establishes a simulation model of opinion propagation on the complex network. The Deffaunt model is selected to conduct simulation according to the relationship between network topology structure and opinion communication. This report studies the influence of different tolerance threshold, opinion composition and rewiring probability on the evolution direction, speed and time respectively. The ”fundamental belief ” was introduced into the algorithm to make the simulated data more consistent with the reality. The experimental results show that the number of clusters when dynamic equilibrium is only related to the maximum tolerance. Finally, the report summarizes the above research and design, and evaluates the network structure change and evolution trend of network public opinion, and puts forward relevant suggestions for future research work.