IDEAS home Printed from https://ideas.repec.org/a/eee/phsmap/v559y2020ics0378437120305380.html
   My bibliography  Save this article

Clusters and the entropy in opinion dynamics on complex networks

Author

Listed:
  • Han, Wenchen
  • Feng, Yuee
  • Qian, Xiaolan
  • Yang, Qihui
  • Huang, Changwei

Abstract

In this work, we investigate a heterogeneous population in the modified Hegselmann–Krause opinion model on complex networks. We introduce the Shannon information entropy about all relative opinion clusters to characterize the cluster profile in the final configuration. Independent of network structures, there exists the optimal stubbornness of one subpopulation for the largest number of clusters and the highest entropy. Besides, there is the optimal bounded confidence (or subpopulation ratio) of one subpopulation for the smallest number of clusters and the lowest entropy. However, network structures affect cluster profiles indeed. A large average degree favors consensus for making different networks more similar with complete graphs. The network size has limited impact on cluster profiles of heterogeneous populations on scale-free networks but has significant effects upon those on small-world networks.

Suggested Citation

  • 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).
    • Handle: RePEc:eee:phsmap:v:559:y:2020:i:c:s0378437120305380
    DOI: 10.1016/j.physa.2020.125033
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0378437120305380
    Download Restriction: Full text for ScienceDirect subscribers only. Journal offers the option of making the article available online on Science direct for a fee of $3,000
    File URL: https://libkey.io/10.1016/j.physa.2020.125033?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Gandica, Yérali & del Castillo-Mussot, Marcelo & Vázquez, Gerardo J. & Rojas, Sergio, 2010. "Continuous opinion model in small-world directed networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 389(24), pages 5864-5870.
    2. Xi Chen & Shen Zhao & Wei Li, 2019. "Opinion Dynamics Model Based on Cognitive Styles: Field-Dependence and Field-Independence," Complexity, Hindawi, vol. 2019, pages 1-12, February.
    3. 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.
    4. Wang, Shaoli & Rong, Libin & Wu, Jianhong, 2016. "Bistability and multistability in opinion dynamics models," Applied Mathematics and Computation, Elsevier, vol. 289(C), pages 388-395.
    5. Rainer Hegselmann & Ulrich Krause, 2002. "Opinion Dynamics and Bounded Confidence Models, Analysis and Simulation," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 5(3), pages 1-2.
    6. AskariSichani, Omid & Jalili, Mahdi, 2015. "Influence maximization of informed agents in social networks," Applied Mathematics and Computation, Elsevier, vol. 254(C), pages 229-239.
    7. Pineda, M. & Buendía, G.M., 2015. "Mass media and heterogeneous bounds of confidence in continuous opinion dynamics," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 420(C), pages 73-84.
    8. Fu, Guiyuan & Zhang, Weidong & Li, Zhijun, 2015. "Opinion dynamics of modified Hegselmann–Krause model in a group-based population with heterogeneous bounded confidence," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 419(C), pages 558-565.
    9. Han, Wenchen & Huang, Changwei & Yang, Junzhong, 2019. "Opinion clusters in a modified Hegselmann–Krause model with heterogeneous bounded confidences and stubbornness," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 531(C).
    10. Jan Lorenz, 2007. "Continuous Opinion Dynamics Under Bounded Confidence: A Survey," International Journal of Modern Physics C (IJMPC), World Scientific Publishing Co. Pte. Ltd., vol. 18(12), pages 1819-1838.
    11. Katarzyna Sznajd-Weron & Józef Sznajd, 2000. "Opinion Evolution In Closed Community," International Journal of Modern Physics C (IJMPC), World Scientific Publishing Co. Pte. Ltd., vol. 11(06), pages 1157-1165.
    12. Guillaume Deffuant & David Neau & Frederic Amblard & Gérard Weisbuch, 2000. "Mixing beliefs among interacting agents," Advances in Complex Systems (ACS), World Scientific Publishing Co. Pte. Ltd., vol. 3(01n04), pages 87-98.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Han, Wenchen & Gao, Shun & Huang, Changwei & Yang, Junzhong, 2022. "Non-consensus states in circular opinion model with repulsive interaction," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 585(C).
    2. Gomes, P.F. & Fernandes, H.A. & Costa, A.A., 2022. "Topological transition in a coupled dynamics in random networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 597(C).
    3. Chen, Yu & Ling, Guang & Song, Xiangxiang & Tu, Wenhui, 2023. "Characterizing the statistical complexity of nonlinear time series via ordinal pattern transition networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 618(C).
    4. Huang, Changwei & Hou, Yongzhao & Han, Wenchen, 2023. "Coevolution of consensus and cooperation in evolutionary Hegselmann–Krause dilemma with the cooperation cost," Chaos, Solitons & Fractals, Elsevier, vol. 168(C).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Huang, Changwei & Bian, Huanyu & Han, Wenchen, 2024. "Breaking the symmetry neutralizes the extremization under the repulsion and higher order interactions," Chaos, Solitons & Fractals, Elsevier, vol. 180(C).
    2. Han, Wenchen & Gao, Shun & Huang, Changwei & Yang, Junzhong, 2022. "Non-consensus states in circular opinion model with repulsive interaction," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 585(C).
    3. Huang, Changwei & Dai, Qionglin & Han, Wenchen & Feng, Yuee & Cheng, Hongyan & Li, Haihong, 2018. "Effects of heterogeneous convergence rate on consensus in opinion dynamics," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 499(C), pages 428-435.
    4. Huang, Changwei & Hou, Yongzhao & Han, Wenchen, 2023. "Coevolution of consensus and cooperation in evolutionary Hegselmann–Krause dilemma with the cooperation cost," Chaos, Solitons & Fractals, Elsevier, vol. 168(C).
    5. Li, Mingwu & Dankowicz, Harry, 2019. "Impact of temporal network structures on the speed of consensus formation in opinion dynamics," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 523(C), pages 1355-1370.
    6. Wang, Chaoqian, 2021. "Opinion dynamics with bilateral propaganda and unilateral information blockade," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 566(C).
    7. 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.
    8. 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.
    9. 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.
    10. Takesue, Hirofumi, 2023. "Relative opinion similarity leads to the emergence of large clusters in opinion formation models," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 622(C).
    11. Fan, Kangqi & Pedrycz, Witold, 2016. "Opinion evolution influenced by informed agents," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 462(C), pages 431-441.
    12. Antonio Parravano & Ascensión Andina-Díaz & Miguel A Meléndez-Jiménez, 2016. "Bounded Confidence under Preferential Flip: A Coupled Dynamics of Structural Balance and Opinions," PLOS ONE, Public Library of Science, vol. 11(10), pages 1-23, October.
    13. Hou, Jian & Li, Wenshan & Jiang, Mingyue, 2021. "Opinion dynamics in modified expressed and private model with bounded confidence," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 574(C).
    14. 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.
    15. Li, Tingyu & Zhu, Hengmin, 2020. "Effect of the media on the opinion dynamics in online social networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 551(C).
    16. Catherine A. Glass & David H. Glass, 2021. "Social Influence of Competing Groups and Leaders in Opinion Dynamics," Computational Economics, Springer;Society for Computational Economics, vol. 58(3), pages 799-823, October.
    17. Teo Victor Silva & Sebastián Gonçalves & Bruno Requião Cunha, 2024. "Bounded confidence opinion dynamics with Asch-like social conformity in complex networks," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 97(9), pages 1-10, September.
    18. Xi Chen & Shen Zhao & Wei Li, 2019. "Opinion Dynamics Model Based on Cognitive Styles: Field-Dependence and Field-Independence," Complexity, Hindawi, vol. 2019, pages 1-12, February.
    19. Glass, Catherine A. & Glass, David H., 2021. "Opinion dynamics of social learning with a conflicting source," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 563(C).
    20. Shang, Yilun, 2018. "Hybrid consensus for averager–copier–voter networks with non-rational agents," Chaos, Solitons & Fractals, Elsevier, vol. 110(C), pages 244-251.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:phsmap:v:559:y:2020:i:c:s0378437120305380. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/physica-a-statistical-mechpplications/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.