https://ledgerjournal.org/ojs/ledger <p><em>Ledger</em> is a peer-reviewed scholarly journal that publishes full-length original research articles on the subjects of cryptocurrency and blockchain technology, as well as any relevant intersections with mathematics, computer science, engineering, law, and economics.<em>&nbsp;&nbsp;</em>It is published online by the University Library System, University of Pittsburgh.</p> <p>The journal<em> Ledger</em>:</p> <ul> <li class="show">is open access to all readers,</li> <li class="show">does not charge fees to independent authors or authors with no institutional support,</li> <li class="show">employs a transparent peer-review process,</li> <li class="show">encourages authors to <a href="/ojs/public/journals/1/simplesign.html">digitally sign their manuscripts</a></li> </ul> <p>Authors planning to submit their work to the journal are strongly advised to examine <a href="/ojs/index.php/ledger/about/submissions#authorGuidelines">the Author Guidelines section of the website.</a></p> University Library System, University of Pittsburgh en-US Ledger 2379-5980 <p>Authors who publish with this journal agree to the following terms:</p> <ol> <li>The Author retains copyright in the Work, where the term “Work” shall include all digital objects that may result in subsequent electronic publication or distribution.</li> <li>Upon acceptance of the Work, the author shall grant to the Publisher the right of first publication of the Work.</li> <li>The Author shall grant to the Publisher and its agents the nonexclusive perpetual right and license to publish, archive, and make accessible the Work in whole or in part in all forms of media now or hereafter known under a <a title="CC-BY" href="http://creativecommons.org/licenses/by/4.0/">Creative Commons Attribution 4.0 International License</a>&nbsp;or its equivalent, which, for the avoidance of doubt, allows others to copy, distribute, and transmit the Work under the following conditions: <ol type="a"> <li>Attribution—other users must attribute the Work in the manner specified by the author as indicated on the journal Web site;</li> </ol> with the understanding that the above condition can be waived with permission from the Author and that where the Work or any of its elements is in the public domain under applicable law, that status is in no way affected by the license.</li> <li>The Author is able to enter into separate, additional contractual arrangements for the nonexclusive distribution of the journal's published version of the Work (e.g., post it to an institutional repository or publish it in a book), as long as there is provided in the document an acknowledgement of its initial publication in this journal.</li> <li>Authors are permitted and encouraged to post online a prepublication manuscript (but not the Publisher’s final formatted PDF version of the Work) in institutional repositories or on their Websites prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work. 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Revision Description: Removed outdated link.&nbsp;</span></p> https://ledgerjournal.org/ojs/ledger/article/view/402 <p>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, similarly to “financial LEGO bricks”, 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) and 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.</p> Junliang Luo Stefan Kitzler Pietro Saggese Copyright (c) 2025 Junliang Luo, Stefan Kitzler, Pietro Saggese https://creativecommons.org/licenses/by/4.0 2025-03-04 2025-03-04 10 10.5195/ledger.2025.402