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Social power and opinion formation in complex networks

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  • Jalili, Mahdi

Abstract

In this paper we investigate the effects of social power on the evolution of opinions in model networks as well as in a number of real social networks. A continuous opinion formation model is considered and the analysis is performed through numerical simulation. Social power is given to a proportion of agents selected either randomly or based on their degrees. As artificial network structures, we consider scale-free networks constructed through preferential attachment and Watts–Strogatz networks. Numerical simulations show that scale-free networks with degree-based social power on the hub nodes have an optimal case where the largest number of the nodes reaches a consensus. However, given power to a random selection of nodes could not improve consensus properties. Introducing social power in Watts–Strogatz networks could not significantly change the consensus profile.

Suggested Citation

  • Jalili, Mahdi, 2013. "Social power and opinion formation in complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(4), pages 959-966.
  • Handle: RePEc:eee:phsmap:v:392:y:2013:i:4:p:959-966
    DOI: 10.1016/j.physa.2012.10.013
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    3. Shane T. Mueller & Yin-Yin Sarah Tan, 2018. "Cognitive perspectives on opinion dynamics: the role of knowledge in consensus formation, opinion divergence, and group polarization," Journal of Computational Social Science, Springer, vol. 1(1), pages 15-48, January.
    4. Evangelos Ioannidis & Nikos Varsakelis & Ioannis Antoniou, 2020. "Promoters versus Adversaries of Change: Agent-Based Modeling of Organizational Conflict in Co-Evolving Networks," Mathematics, MDPI, vol. 8(12), pages 1-25, December.
    5. Cheng, Xiu & Long, Ruyin & Chen, Hong & Yang, Jiahui, 2019. "Does social interaction have an impact on residents’ sustainable lifestyle decisions? A multi-agent stimulation based on regret and game theory," Applied Energy, Elsevier, vol. 251(C), pages 1-1.
    6. Castro, Luis E. & Shaikh, Nazrul I., 2018. "A particle-learning-based approach to estimate the influence matrix of online social networks," Computational Statistics & Data Analysis, Elsevier, vol. 126(C), pages 1-18.
    7. Yingcheng Xu & Li Wang & Bo Xu & Wei Jiang & Chaoqun Deng & Fang Ji & Xiaobo Xu, 0. "An information integration and transmission model of multi-source data for product quality and safety," Information Systems Frontiers, Springer, vol. 0, pages 1-22.
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    10. Xi Chen & Xiao Zhang & Yong Xie & Wei Li, 2017. "Opinion Dynamics of Social-Similarity-Based Hegselmann–Krause Model," Complexity, Hindawi, vol. 2017, pages 1-12, December.
    11. Han, Wenchen & Feng, Yuee & Qian, Xiaolan & Yang, Qihui & Huang, Changwei, 2020. "Clusters and the entropy in opinion dynamics on complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 559(C).
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