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Managing social networks: Applying the percolation theory methodology to understand individuals' attitudes and moods

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  • Zhukov, Dmitry
  • Khvatova, Tatiana
  • Lesko, Sergey
  • Zaltcman, Anastasia

Abstract

A disruptive technology is an unexpected technological breakthrough which destroys existing markets, shakes up an industry and induces organisations to radically change their business models. Digital technologies, and in particular social media are, without a doubt, the most disruptive innovations to have emerged over the past few decades. Social media virtually rule society: people's moods and opinions, spreading openly and rapidly within networks, can affect almost any business or brand in a positive or negative way.

Suggested Citation

  • Zhukov, Dmitry & Khvatova, Tatiana & Lesko, Sergey & Zaltcman, Anastasia, 2018. "Managing social networks: Applying the percolation theory methodology to understand individuals' attitudes and moods," Technological Forecasting and Social Change, Elsevier, vol. 129(C), pages 297-307.
    • Handle: RePEc:eee:tefoso:v:129:y:2018:i:c:p:297-307
    DOI: 10.1016/j.techfore.2017.09.039
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    References listed on IDEAS

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    1. de Masi, G. & Iori, G. & Caldarelli, G., 2006. "A fitness model for the Italian interbank money market," Working Papers 06/08, Department of Economics, City University London.
    2. Hirokazu Kawamoto & Hideki Takayasu & Henrik Jeldtoft Jensen & Misako Takayasu, 2015. "Precise Calculation of a Bond Percolation Transition and Survival Rates of Nodes in a Complex Network," PLOS ONE, Public Library of Science, vol. 10(4), pages 1-16, April.
    3. Chen, Yiping & Paul, Gerald & Cohen, Reuven & Havlin, Shlomo & Borgatti, Stephen P. & Liljeros, Fredrik & Eugene Stanley, H., 2007. "Percolation theory and fragmentation measures in social networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 378(1), pages 11-19.
    4. Ueno, Hiromichi & Mizuno, Takayuki & Takayasu, Misako, 2007. "Analysis of Japanese banks’ historical tree diagram," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 383(1), pages 164-168.
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    Cited by:

    1. Hwang, Jeong Seop & Rho, Jae Jeung & Hwang, Yoon Min, 2023. "Influence of cognitive and social change factors on E-vehicle switching intention: Evidence from Korea," Technology in Society, Elsevier, vol. 74(C).
    2. Guo, Jingni & Xu, Junxiang & He, Zhenggang & Liao, Wei, 2021. "Research on risk propagation method of multimodal transport network under uncertainty," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 563(C).
    3. Zhukov, Dmitry & Khvatova, Tatiana & Millar, Carla & Andrianova, Elena, 2022. "Beyond big data – new techniques for forecasting elections using stochastic models with self-organisation and memory," Technological Forecasting and Social Change, Elsevier, vol. 175(C).
    4. Zhukov, Dmitry & Khvatova, Tatiana & Millar, Carla & Zaltcman, Anastasia, 2020. "Modelling the stochastic dynamics of transitions between states in social systems incorporating self-organization and memory," Technological Forecasting and Social Change, Elsevier, vol. 158(C).

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