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Evidence of Ostwald Ripening in opinion driven dynamics of mutually competitive social networks

Author

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  • Munjal, Puja
  • Kumar, Lalit
  • Kumar, Sandeep
  • Banati, Hema

Abstract

Models based on opinion exchange aim to describe the dynamics of social networks and to uncover the intrinsic mechanisms in mutually competitive social networks (MCSN). However, the limited capability of the existing opinion exchange models for characterizing the real world social networks has triggered the need for more realistic models. In this paper, the natural phenomenon of Ostwald ripening is explored to explain the generally observed process related to growth of bigger social networks at the expense of smaller social networks. A novel model called Ostwald Ripening Social Network (ORSN) is developed, for understanding the opinion driven growth dynamics of real-world MCSN. Ostwald ripening phenomenon has been extensively used to explain the growth of particles at nanoscale. In order to get better insight of the phenomenon, we synthesized Zinc oxide (ZnO) nanoparticles and a thorough investigation is done to understand the growth dynamics under varied condition of temperature. Strong experimentation is done on the extensive dataset collected from actual social network, based on the opinion formation under the influence of factors like propaganda and network ties. The results of the growth dynamics from actually synthesized nanoparticles are found in corroboration with the pattern of growth dynamics of the MCSN. This is a vital attempt to complement the existing approaches, with a conceptual understanding that would help the supervision, prediction and control of dynamic processes in social networks.

Suggested Citation

  • Munjal, Puja & Kumar, Lalit & Kumar, Sandeep & Banati, Hema, 2019. "Evidence of Ostwald Ripening in opinion driven dynamics of mutually competitive social networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 522(C), pages 182-194.
    • Handle: RePEc:eee:phsmap:v:522:y:2019:i:c:p:182-194
    DOI: 10.1016/j.physa.2019.01.109
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    References listed on IDEAS

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    1. Zhu, Hengmin & Kong, Yuehan & Wei, Jing & Ma, Jing, 2018. "Effect of users’ opinion evolution on information diffusion in online social networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 492(C), pages 2034-2045.
    2. Cai, Meng & Wang, Wei & Cui, Ying & Stanley, H. Eugene, 2018. "Multiplex network analysis of employee performance and employee social relationships," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 490(C), pages 1-12.
    3. David Anzola & Peter Barbrook-Johnson & Juan I. Cano, 2017. "Self-organization and social science," Computational and Mathematical Organization Theory, Springer, vol. 23(2), pages 221-257, June.
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