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Application of Graph Theory for Blockchain Technologies

Author

Listed:
  • Guruprakash Jayabalasamy

    (Department of Computer Science and Engineering, Amrita School of Computing, Amrita Vishwa Vidyapeetham, Coimbatore 641112, India)

  • Cyril Pujol

    (École Normale Supérieure Paris-Saclay, 91190 Gif-sur-Yvette, France)

  • Krithika Latha Bhaskaran

    (School of Computer Science Engineering and Information Systems, Vellore Institute of Technology, Vellore 632014, India)

Abstract

Blockchain technology, serving as the backbone for decentralized systems, facilitates secure and transparent transactional data storage across a distributed network of nodes. Blockchain platforms rely on distributed ledgers to enable secure peer-to-peer transactions without central oversight. As these systems grow in complexity, analyzing their topological structure and vulnerabilities requires robust mathematical frameworks. This paper explores applications of graph theory for modeling blockchain networks to evaluate decentralization, security, privacy, scalability and NFT Mapping. We use graph metrics like degree distribution and betweenness centrality to quantify node connectivity, identify network bottlenecks, trace asset flows and detect communities. Attack vectors are assessed by simulating adversarial scenarios within graph models of blockchain systems. Overall, translating blockchain ecosystems into graph representations allows comprehensive analytical insights to guide the development of efficient, resilient decentralized infrastructures.

Suggested Citation

  • Guruprakash Jayabalasamy & Cyril Pujol & Krithika Latha Bhaskaran, 2024. "Application of Graph Theory for Blockchain Technologies," Mathematics, MDPI, vol. 12(8), pages 1-45, April.
  • Handle: RePEc:gam:jmathe:v:12:y:2024:i:8:p:1133-:d:1372842
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    References listed on IDEAS

    as
    1. Derek Liu & Francesco Piccoli & Katie Chen & Adrina Tang & Victor Fang, 2023. "NFT Wash Trading Detection," Papers 2305.01543, arXiv.org.
    2. Tetsuya Takaishi, 2017. "Statistical properties and multifractality of Bitcoin," Papers 1707.07618, arXiv.org, revised May 2018.
    3. Soohoon Maeng & Meryam Essaid & Changhyun Lee & Sejin Park & Hongteak Ju, 2021. "Visualization of Ethereum P2P network topology and peer properties," International Journal of Network Management, John Wiley & Sons, vol. 31(6), November.
    4. Hassan Raza & Sunny Kumar Sharma & Muhammad Azeem & Gaetano Luciano, 2023. "On Domatic Number of Some Rotationally Symmetric Graphs," Journal of Mathematics, Hindawi, vol. 2023, pages 1-11, February.
    5. Takaishi, Tetsuya, 2018. "Statistical properties and multifractality of Bitcoin," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 506(C), pages 507-519.
    6. Jesse Yli-Huumo & Deokyoon Ko & Sujin Choi & Sooyong Park & Kari Smolander, 2016. "Where Is Current Research on Blockchain Technology?—A Systematic Review," PLOS ONE, Public Library of Science, vol. 11(10), pages 1-27, October.
    7. A. El-Mesady & Omar Bazighifan & Mehar Ali Malik, 2022. "Construction of Mutually Orthogonal Graph Squares Using Novel Product Techniques," Journal of Mathematics, Hindawi, vol. 2022, pages 1-16, October.
    Full references (including those not matched with items on IDEAS)

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