Blockchain technology has rapidly emerged to mainstream attention, while 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's 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 both opportunities and challenges for machine learning on blockchain data. On 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 the improvement of blockchain technology such as e-crime detection and trends prediction. On the other hand, we shed light on the pivotal role of blockchain by providing vast datasets and tools that can catalyze the growth of the evolving machine learning ecosystem. This paper serves as a comprehensive resource for researchers, practitioners, and policymakers, offering a roadmap for navigating this dynamic and transformative field.

翻译：区块链技术已迅速进入主流视野，其公开可访问、异构、海量且具有时序特征的数据，让人联想到过去十年大数据时代所经历的复杂动态变化。与任何先前数据源不同，区块链数据集涵盖现实世界实体（如人类用户、自主程序和智能合约）之间多个层次的交互。此外，区块链与加密货币的融合引入了前所未有的规模和复杂性的金融层面，例如去中心化金融、稳定币、非同质化代币和中央银行数字货币。这些独特特征为区块链数据的机器学习带来了机遇与挑战。一方面，我们审视了利用机器学习进行区块链数据分析（对改进区块链技术至关重要，例如电子犯罪检测和趋势预测）的现有解决方案、应用及未来方向；另一方面，我们揭示了区块链通过提供可催化机器学习生态系统发展的海量数据集和工具所发挥的关键作用。本文旨在为研究人员、从业者和政策制定者提供全面资源，为探索这一动态且变革性的领域绘制路线图。