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Ledger Technol."],"published-print":{"date-parts":[[2026,3,31]]},"abstract":"<jats:p>Blockchain technology has rapidly emerged to mainstream attention. At the same time, its publicly accessible, heterogeneous, massive-volume, and temporal data are reminiscent of the complex dynamics encountered during the last decade of big data. Unlike any prior data source, blockchain datasets encompass multiple layers of interactions across real-world entities, e.g., human users, autonomous programs, and smart contracts. Furthermore, blockchain\u2019s integration with cryptocurrencies has introduced financial aspects of unprecedented scale and complexity, such as decentralized finance, stablecoins, non-fungible tokens, and central bank digital currencies. These unique characteristics present opportunities and challenges for machine learning on blockchain data.<\/jats:p>\n                  <jats:p>On the one hand, we examine the state-of-the-art solutions, applications, and future directions associated with leveraging machine learning for blockchain data analysis critical for improving blockchain technology, such as e-crime detection and trends prediction. On the other hand, we shed light on blockchain\u2019s pivotal role by providing vast datasets and tools that can catalyze the growth of the evolving machine learning ecosystem. This article is a comprehensive resource for researchers, practitioners, and policymakers, offering a roadmap for navigating this dynamic and transformative field.<\/jats:p>","DOI":"10.1145\/3728474","type":"journal-article","created":{"date-parts":[[2025,4,8]],"date-time":"2025-04-08T14:24:09Z","timestamp":1744122249000},"page":"1-27","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":14,"title":["Machine Learning for Blockchain Data Analysis: Progress and Opportunities"],"prefix":"10.1145","volume":"5","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0607-5073","authenticated-orcid":false,"given":"Poupak","family":"Azad","sequence":"first","affiliation":[{"name":"Department of Computer Science, University of Manitoba, Winnipeg, Manitoba, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2882-6950","authenticated-orcid":false,"given":"Cuneyt Gurcan","family":"Akcora","sequence":"additional","affiliation":[{"name":"Department of Finance, University of Central Florida, Orlando, Florida, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7312-6312","authenticated-orcid":false,"given":"Arijit","family":"Khan","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Aalborg University, Aalborg, Denmark"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2025,12,10]]},"reference":[{"key":"e_1_3_2_2_2","doi-asserted-by":"crossref","first-page":"946","DOI":"10.1109\/ICDM.2019.00105","volume-title":"2019 IEEE International Conference on Data Mining (ICDM \u201919)","author":"Abay Nazmiye Ceren","year":"2019","unstructured":"Nazmiye Ceren Abay, Cuneyt Gurcan Akcora, Yulia R. 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