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

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)...

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Bibliographic Details
Published in:IEEE transactions on systems, man, and cybernetics. Systems man, and cybernetics. Systems, 2023-01, Vol.53 (1), p.296-308
Main Authors: Wu, Zhibin, Zhou, Qinyue, Dong, Yucheng, Xu, Jiuping, Altalhi, Abdulrahman H., Herrera, Francisco
Format: Article
Language:English
Subjects:
Algorithms
Blogs
Clustering
Computational modeling
Cybernetics
DeGoot model
Evolution
Hegselmann–Krause (HK) bounded confidence model
Integrated circuits
Ions
Mathematical models
Network topologies
opinion dynamics
Self-similarity
Similarity
social network
Social networking (online)
Social networks
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Summary: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.
ISSN:2168-2216
2168-2232
DOI:10.1109/TSMC.2022.3178230