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ChainNet: Learning on Blockchain Graphs with Topological Features

Author

Listed:
  • Nazmiye Ceren Abay
  • Cuneyt Gurcan Akcora
  • Yulia R. Gel
  • Umar D. Islambekov
  • Murat Kantarcioglu
  • Yahui Tian
  • Bhavani Thuraisingham

Abstract

With emergence of blockchain technologies and the associated cryptocurrencies, such as Bitcoin, understanding network dynamics behind Blockchain graphs has become a rapidly evolving research direction. Unlike other financial networks, such as stock and currency trading, blockchain based cryptocurrencies have the entire transaction graph accessible to the public (i.e., all transactions can be downloaded and analyzed). A natural question is then to ask whether the dynamics of the transaction graph impacts the price of the underlying cryptocurrency. We show that standard graph features such as degree distribution of the transaction graph may not be sufficient to capture network dynamics and its potential impact on fluctuations of Bitcoin price. In contrast, the new graph associated topological features computed using the tools of persistent homology, are found to exhibit a high utility for predicting Bitcoin price dynamics. %explain higher order interactions among the nodes in Blockchain graphs and can be used to build much more accurate price prediction models. Using the proposed persistent homology-based techniques, we offer a new elegant, easily extendable and computationally light approach for graph representation learning on Blockchain.

Suggested Citation

  • Nazmiye Ceren Abay & Cuneyt Gurcan Akcora & Yulia R. Gel & Umar D. Islambekov & Murat Kantarcioglu & Yahui Tian & Bhavani Thuraisingham, 2019. "ChainNet: Learning on Blockchain Graphs with Topological Features," Papers 1908.06971, arXiv.org.
    • Handle: RePEc:arx:papers:1908.06971
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    File URL: http://arxiv.org/pdf/1908.06971
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    References listed on IDEAS

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    1. Nino Antulov-Fantulin & Dijana Tolic & Matija Piskorec & Zhang Ce & Irena Vodenska, 2018. "Inferring short-term volatility indicators from Bitcoin blockchain," Papers 1809.07856, arXiv.org.
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    Cited by:

    1. Xiao Li & Linda Du, 2023. "Bitcoin daily price prediction through understanding blockchain transaction pattern with machine learning methods," Journal of Combinatorial Optimization, Springer, vol. 45(1), pages 1-24, January.
    2. Panpan Li & Shengbo Gong & Shaocong Xu & Jiajun Zhou & Yu Shanqing & Qi Xuan, 2022. "Cross Cryptocurrency Relationship Mining for Bitcoin Price Prediction," Papers 2205.00974, arXiv.org.
    3. Rico-Peña, Juan Jesús & Arguedas-Sanz, Raquel & López-Martin, Carmen, 2023. "Models used to characterise blockchain features. A systematic literature review and bibliometric analysis," Technovation, Elsevier, vol. 123(C).
    4. Fan Fang & Carmine Ventre & Michail Basios & Leslie Kanthan & David Martinez-Rego & Fan Wu & Lingbo Li, 2022. "Cryptocurrency trading: a comprehensive survey," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 8(1), pages 1-59, December.
    5. Xiao Li & Weili Wu, 2020. "A Blockchain Transaction Graph based Machine Learning Method for Bitcoin Price Prediction," Papers 2008.09667, arXiv.org.
    6. Yitao Li & Umar Islambekov & Cuneyt Akcora & Ekaterina Smirnova & Yulia R. Gel & Murat Kantarcioglu, 2019. "Dissecting Ethereum Blockchain Analytics: What We Learn from Topology and Geometry of Ethereum Graph," Papers 1912.10105, arXiv.org.

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