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Machine Learning for Blockchain Data Analysis: Progress and Opportunities

Poupak Azad, Cuneyt Gurcan Akcora, Arijit Khan

TL;DR

This survey tackles the challenge of applying machine learning to blockchain data by providing a comprehensive taxonomy of methods, data models, and applications developed since 2018. It organizes the literature along three non-exclusive ML approaches—graph ML, temporal ML, and ML for smart contracts—mapping them to blockchain data models (e.g., UTXO vs account graphs, temporal graphs, and contract code structures) and to practical applications such as e-crime detection, fraud analytics, and security auditing. The authors also catalog datasets and tools (e.g., Elliptic, BitcoinHeist, Chartalist, NFTGraph) and identify pivotal challenges, including anonymity, data dynamics, label scarcity, and cross-chain interoperability, while outlining a roadmap for scalable, explainable, and cross-chain analytics. The paper highlights opportunities to advance the field through cross-chain analyses, continuous learning, scalable inference, and the potential of large language models to interpret natural language data and assist in code understanding and generation for smart contracts.

Abstract

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.

Machine Learning for Blockchain Data Analysis: Progress and Opportunities

TL;DR

This survey tackles the challenge of applying machine learning to blockchain data by providing a comprehensive taxonomy of methods, data models, and applications developed since 2018. It organizes the literature along three non-exclusive ML approaches—graph ML, temporal ML, and ML for smart contracts—mapping them to blockchain data models (e.g., UTXO vs account graphs, temporal graphs, and contract code structures) and to practical applications such as e-crime detection, fraud analytics, and security auditing. The authors also catalog datasets and tools (e.g., Elliptic, BitcoinHeist, Chartalist, NFTGraph) and identify pivotal challenges, including anonymity, data dynamics, label scarcity, and cross-chain interoperability, while outlining a roadmap for scalable, explainable, and cross-chain analytics. The paper highlights opportunities to advance the field through cross-chain analyses, continuous learning, scalable inference, and the potential of large language models to interpret natural language data and assist in code understanding and generation for smart contracts.

Abstract

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.
Paper Structure (22 sections, 1 figure, 1 table)

This paper contains 22 sections, 1 figure, 1 table.

Table of Contents

  1. Introduction
  2. Taxonomy
  3. Machine Learning Methods
  4. Blockchain Components
  5. Blockchain Data Models
  6. Applications of Blockchain Data Analysis
  7. Challenges of Machine Learning for Blockchain Data Analysis
  8. Survey: Blockchain Data Models, Machine Learning Methods, and Applications
  9. Graph Machine Learning on Blockchains
  10. Graph Data Models
  11. Graph Machine Learning Methods
  12. Open Questions and Challenges
  13. Temporal Machine Learning on Blockchains
  14. Temporal Data Models
  15. Temporal Machine Learning Methods
  16. ...and 7 more sections

Figures (1)

  • Figure 1: The timeline of machine learning for Blockchain research.