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Investigating Similarities Across Decentralized Financial (DeFi) Services


  • Junliang Luo
  • Stefan Kitzler
  • Pietro Saggese


We explore the adoption of graph representation learning (GRL) algorithms to investigate similarities across services offered by Decentralized Finance (DeFi) protocols. Following existing literature, we use Ethereum transaction data to identify the DeFi building blocks. These are sets of protocol-specific smart contracts that are utilized in combination within single transactions and encapsulate the logic to conduct specific financial services such as swapping or lending cryptoassets. We propose a method to categorize these blocks into clusters based on their smart contract attributes and the graph structure of their smart contract calls. We employ GRL to create embedding vectors from building blocks and agglomerative models for clustering them. To evaluate whether they are effectively grouped in clusters of similar functionalities, we associate them with eight financial functionality categories and use this information as the target label. We find that in the best-case scenario purity reaches .888. We use additional information to associate the building blocks with protocol-specific target labels, obtaining comparable purity (.864) but higher V-Measure (.571); we discuss plausible explanations for this difference. In summary, this method helps categorize existing financial products offered by DeFi protocols, and can effectively automatize the detection of similar DeFi services, especially within protocols.

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  • Junliang Luo & Stefan Kitzler & Pietro Saggese, 2024. "Investigating Similarities Across Decentralized Financial (DeFi) Services," Papers 2404.00034,
  • Handle: RePEc:arx:papers:2404.00034

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    References listed on IDEAS

    1. Jiahua Xu & Krzysztof Paruch & Simon Cousaert & Yebo Feng, 2021. "SoK: Decentralized Exchanges (DEX) with Automated Market Maker (AMM) Protocols," Papers 2103.12732,, revised Mar 2023.
    2. Jiahua Xu & Nikhil Vadgama, 2021. "From banks to DeFi: the evolution of the lending market," Papers 2104.00970,, revised Dec 2022.
    3. Nadia Pocher & Mirko Zichichi & Fabio Merizzi & Muhammad Zohaib Shafiq & Stefano Ferretti, 2023. "Detecting anomalous cryptocurrency transactions: An AML/CFT application of machine learning-based forensics," Electronic Markets, Springer;IIM University of St. Gallen, vol. 33(1), pages 1-17, December.
    4. Robin Fritsch & Samuel Kaser & Roger Wattenhofer, 2022. "The Economics of Automated Market Makers," Papers 2206.04634,
    5. Amani Moin & Emin Gun Sirer & Kevin Sekniqi, 2019. "A Classification Framework for Stablecoin Designs," Papers 1910.10098,
    6. Fionn Murtagh & Pierre Legendre, 2014. "Ward’s Hierarchical Agglomerative Clustering Method: Which Algorithms Implement Ward’s Criterion?," Journal of Classification, Springer;The Classification Society, vol. 31(3), pages 274-295, October.
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