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Understanding Polkadot Through Graph Analysis: Transaction Model, Network Properties, and Insights

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Financial Cryptography and Data Security (FC 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13951))

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Abstract

In recent years, considerable efforts have been directed toward investigating the large amount of public transaction data in prominent cryptocurrencies. Nevertheless, aside from Bitcoin and Ethereum, little efforts have been made to investigate other cryptocurrencies, even though the market now comprises thousands, with more than 50 exceeding one billion dollars of capitalization, and some of them sporting innovative technical solutions and governance. This is the case for Polkadot, a relatively new blockchain that promises to solve the shortcomings in scalability and interoperability that encumber many existing blockchain-based systems. In particular, Polkadot relies on a novel multi-chain construction that promises to enable interoperability among heterogeneous blockchains.

This paper presents the first study to formally model and investigate user transactions in the Polkadot network. Our contributions are multifolds: After defining proper and pseudo-spam transactions, we built the transaction graph based on data collected from the launch of the network, in May 2020, until July 2022. The dataset consists of roughly 11 million blocks, including 2 million user accounts and 7.6 million transactions. We applied a selected set of graph metrics, such as degree distribution, strongly/weakly connected components, density, and several centrality measures, to the collected data. In addition, we also investigated a few interesting idiosyncratic indicators, such as the accounts’ balance over time and improper transactions. Our results shed light on the topology of the network, which resembles a heavy-tailed power-law distribution, demonstrate that Polkadot is affected by the rich get richer conundrum, and provide other insights into the financial ecosystem of the network. The approach, methodology, and metrics proposed in this work, while being applied to Polkadot, can also be applied to other cryptocurrencies, hence having a high potential impact and the possibility to further research in the cryptocurrency field.

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Notes

  1. 1.

    Data sourced from https://coinmarketcap.com/.

  2. 2.

    https://github.com/m-caprolu/Polkadot-graph-analysis.

  3. 3.

    https://networkx.org/.

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Acknowledgments

This publication was partially supported by the Qatar National Research Fund (QNRF), a member of The Qatar Foundation, through the awards [NPRP-S-11-0109-180242] and [NPRP11C-1229-170007]. The information and views set out in this publication are those of the authors and do not necessarily reflect the official opinion of the QNRF.* Dr. Roberto Di Pietro produced part of his contributions while he was at HBKU-CSE.

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Authors and Affiliations

  1. Hamad Bin Khalifa University (HBKU) - College of Science and Engineering (CSE), Division of Information and Computing Technology (ICT), Doha, Qatar

    Hanaa Abbas & Maurantonio Caprolu

  2. RC3 Center, CEMSE Division, King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia

    Roberto Di Pietro

Authors
  1. Hanaa Abbas
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  2. Maurantonio Caprolu
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  3. Roberto Di Pietro
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Correspondence to Hanaa Abbas .

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Editors and Affiliations

  1. George Mason University, Fairfax, VA, USA

    Foteini Baldimtsi

  2. University of Bern, Bern, Switzerland

    Christian Cachin

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Abbas, H., Caprolu, M., Di Pietro, R. (2024). Understanding Polkadot Through Graph Analysis: Transaction Model, Network Properties, and Insights. In: Baldimtsi, F., Cachin, C. (eds) Financial Cryptography and Data Security. FC 2023. Lecture Notes in Computer Science, vol 13951. Springer, Cham. https://doi.org/10.1007/978-3-031-47751-5_15

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  • DOI: https://doi.org/10.1007/978-3-031-47751-5_15

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