A Global Opinion-influencing Consensus Model based on the DeGroot

Social networks have become an important platform for transmitting and sharing information due to the rapid development of online social networks. Opinion dynamics have been examined through the consensus-reaching process (CRP) in social networks, which aims to model the evolution of opinions in social networks so that agents can reach a consensus. Many researchers have solved the CRP problem by improving the DeGroot model, which models divide agents into two categories, leaders and followers, and change the network structure or the values of the agents' opinions to bring the network to a consensus. However, all of them require the construction of leaders who can influence the entire network. Consensus cannot be achieved without the leaders. This paper proposes a global opinion-influencing consensus model (GOCM) to solve the CRP problem. A network agent refers not only to the opinion of its neighbors but also to the average opinion of the entire network. The numerical analysis indicates that our proposed GOCM can significantly accelerate the achievement of an expected consensus.