An Opinion Dynamics Model with Cross-Link Interaction

Abstract

At present, online social networks have become the main platform for people to express their opinions and interact with them, which has a great impact on the evolution of opinions. Therefore, the research on the evolution of opinions in online social networks has become a current hotspot. Opinion dynamics is an important tool to study the evolution law of opinions in the network. In the opinion dynamics model for online social networks, agents often can only interact with others who have links on the network. However, in reality, the interaction of agents’ opinions is not limited to agents with links on the network. Therefore, due to the lack of sufficient interaction, the traditional model cannot reflect the true final state of public opinion evolution. In view of the above situation, we propose a novel cross-link interaction mechanism which enables agents interact opinion with others without the limit of network inks and use machine learning methodology to get the cross-link interaction distance. After that, we introduce the mechanism to the bounded confidence opinion dynamics model. With this mechanism, the interaction of the agent will be more reasonable and more like social behavior. The simulation results show that the proposed model fits the real data better than traditional models and even under a very small bounded confidence value, agents opinions will still be around high-influence agents’ opinions.