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From Bitcoin to Bitcoin Cash: a network analysis

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  • Marco Alberto Javarone
  • Craig Steven Wright

Abstract

Bitcoins and Blockchain technologies are attracting the attention of different scientific communities. In addition, their widespread industrial applications and the continuous introduction of cryptocurrencies are also stimulating the attention of the public opinion. The underlying structure of these technologies constitutes one of their core concepts. In particular, they are based on peer-to-peer networks. Accordingly, all nodes lie at the same level, so that there is no place for privileged actors as, for instance, banking institutions in classical financial networks. In this work, we perform a preliminary investigation on two kinds of network, i.e. the Bitcoin network and the Bitcoin Cash network. Notably, we analyze their global structure and we try to evaluate if they are provided with a small-world behavior. Results suggest that the principle known as 'fittest-gets-richer', combined with a continuous increasing of connections, might constitute the mechanism leading these networks to reach their current structure. Moreover, further observations open the way to new investigations into this direction.

Suggested Citation

  • Marco Alberto Javarone & Craig Steven Wright, 2018. "From Bitcoin to Bitcoin Cash: a network analysis," Papers 1804.02350, arXiv.org, revised Jul 2018.
    • Handle: RePEc:arx:papers:1804.02350
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    Cited by:

    1. A. Mikhailov Yu. & А. Михайлов Ю., 2020. "Развитие рынка криптовалют: метод Херста // Cryptocurrency Market Development: Hurst Method," Финансы: теория и практика/Finance: Theory and Practice // Finance: Theory and Practice, ФГОБУВО Финансовый университет при Правительстве Российской Федерации // Financial University under The Government of Russian Federation, vol. 24(3), pages 81-91.
    2. Laura Alessandretti & Abeer ElBahrawy & Luca Maria Aiello & Andrea Baronchelli, 2018. "Anticipating cryptocurrency prices using machine learning," Papers 1805.08550, arXiv.org, revised Nov 2018.
    3. Laura Alessandretti & Abeer ElBahrawy & Luca Maria Aiello & Andrea Baronchelli, 2018. "Anticipating Cryptocurrency Prices Using Machine Learning," Complexity, Hindawi, vol. 2018, pages 1-16, November.
    4. Chengyi Tu & Paolo DOdorico & Samir Suweis, 2018. "Critical slowing down associated with critical transition and risk of collapse in cryptocurrency," Papers 1806.08386, arXiv.org, revised Nov 2019.
    5. Kong, Xiaolin & Ma, Chaoqun & Ren, Yi-Shuai & Baltas, Konstantinos & Narayan, Seema, 2024. "A comparative analysis of the price explosiveness in Bitcoin and forked coins," Finance Research Letters, Elsevier, vol. 61(C).
    6. Higor Y. D. Sigaki & Matjaz Perc & Haroldo V. Ribeiro, 2019. "Clustering patterns in efficiency and the coming-of-age of the cryptocurrency market," Papers 1901.04967, arXiv.org.
    7. Lin, Jian-Hong & Marchese, Emiliano & Tessone, Claudio J. & Squartini, Tiziano, 2022. "The weighted Bitcoin Lightning Network," Chaos, Solitons & Fractals, Elsevier, vol. 164(C).

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