Similarity (geometry)

Opinion leadership

Artificial intelligence

Complex network

Random graph

Graph

Mathematics

Image (mathematics)

Political science

Social media

Public relations

Computer science

Network dynamics

World Wide Web

Cluster analysis

Theoretical computer science

Discrete mathematics

Clustering coefficient

Social network (sociolinguistics)

IEEE Transactions on Systems Man and Cybernetics Systems

Most existing opinion formation processes apply one opinion dynamics model. However, this article combines opinion formation and complex networks to innovatively develop two new opinion dynamics models to more realistically describe the opinion evolution process: 1) an opinion similarity mixed (OSM) model and 2) a structural similarity mixed (SSM) model, both of which include characteristics from the DeGroot model and the Hegselmann–Krause bounded confidence model. In addition, the strong and weak relations between individuals are considered. The network dynamically changes by two developed network updating algorithms based on opinion similarity and structural similarity. Simulations are then conducted using artificial and real-world networks, which are Erdös-Rényi random networks, random regular networks, scale-free networks, and the Twitter network. It is found that compared with static networks, the opinion evolution in dynamic networks produces fewer opinion clusters and smaller opinion variances. The dynamic network mechanism reduces the weak relations between agents and improves the global clustering coefficient in the ER random networks but not in the Twitter network, which means that the network topology has an impact on results. Therefore, it is concluded that agents' subjective behaviors significantly influence the outcome of opinion evolution and networks, which is consistent with real life.

Mixed Opinion Dynamics Based on DeGroot Model and Hegselmann–Krause Model in Social Networks