# Machine Learning for Blockchain Data Analysis: Progress and Opportunities

arXiv: https://arxiv.org/abs/2404.18251 · HTML: https://mlsanity.com/paper/2404.18251/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 41 of its 126 references that are in the map.
- First posted on arXiv 28 Apr 2024.

## Builds on (most cited first, 30 of 41)

- [Anti-Money Laundering in Bitcoin: Experimenting with Graph Convolutional Networks for Financial Forensics](https://mlsanity.com/paper/1908.02591/) (2019)
- [Graph Learning: A Survey](https://mlsanity.com/paper/2105.00696/) (2021)
- [Deep Learning for Time Series Forecasting: Tutorial and Literature Survey](https://mlsanity.com/paper/2004.10240/) (2020)
- [Empirical Review of Automated Analysis Tools on 47,587 Ethereum Smart Contracts](https://mlsanity.com/paper/1910.10601/) (2019)
- [Combining Graph Neural Networks with Expert Knowledge for Smart Contract Vulnerability Detection](https://mlsanity.com/paper/2107.11598/) (2021)
- [Smart Contract Vulnerability Detection: From Pure Neural Network to Interpretable Graph Feature and Expert Pattern Fusion](https://mlsanity.com/paper/2106.09282/) (2021)
- [Detecting Mixing Services via Mining Bitcoin Transaction Network with Hybrid Motifs](https://mlsanity.com/paper/2001.05233/) (2020)
- [Deep Learning for Code Intelligence: Survey, Benchmark and Toolkit](https://mlsanity.com/paper/2401.00288/) (2023)
- [Dissecting Ponzi schemes on Ethereum: identification, analysis, and impact](https://mlsanity.com/paper/1703.03779/) (2017)
- [Large Sequence Models for Sequential Decision-Making: A Survey](https://mlsanity.com/paper/2306.13945/) (2023)
- [Blockchain Large Language Models](https://mlsanity.com/paper/2304.12749/) (2023)
- [Anomaly Detection in Blockchain Networks: A Comprehensive Survey](https://mlsanity.com/paper/2112.06089/) (2021)
- [Charting the Landscape of Online Cryptocurrency Manipulation](https://mlsanity.com/paper/2001.10289/) (2020)
- [LSTM Based Sentiment Analysis for Cryptocurrency Prediction](https://mlsanity.com/paper/2103.14804/) (2021)
- [Inspection-L: Self-Supervised GNN Node Embeddings for Money Laundering Detection in Bitcoin](https://mlsanity.com/paper/2203.10465/) (2022)
- [Live Graph Lab: Towards Open, Dynamic and Real Transaction Graphs with NFT](https://mlsanity.com/paper/2310.11709/) (2023)
- [The Unreasonable Effectiveness of Address Clustering](https://mlsanity.com/paper/1605.06369/) (2016)
- [Identifying and Analyzing Cryptocurrency Manipulations in Social Media](https://mlsanity.com/paper/1902.03110/) (2019)
- [ChainNet: Learning on Blockchain Graphs with Topological Features](https://mlsanity.com/paper/1908.06971/) (2019)
- [Knowledge Discovery in Cryptocurrency Transactions: A Survey](https://mlsanity.com/paper/2010.01031/) (2020)
- [TSGN: Transaction Subgraph Networks for Identifying Ethereum Phishing Accounts](https://mlsanity.com/paper/2104.08767/) (2021)
- [Tracing Transactions Across Cryptocurrency Ledgers](https://mlsanity.com/paper/1810.12786/) (2018)
- [Analysis of Cryptocurrency Transactions from a Network Perspective: An Overview](https://mlsanity.com/paper/2011.09318/) (2020)
- [Blockchain Networks: Data Structures of Bitcoin, Monero, Zcash, Ethereum, Ripple and Iota](https://mlsanity.com/paper/2103.08712/) (2021)
- [BABD: A Bitcoin Address Behavior Dataset for Pattern Analysis](https://mlsanity.com/paper/2204.05746/) (2022)
- [Detecting Anomalous Cryptocurrency Transactions: an AML/CFT Application of Machine Learning-based Forensics](https://mlsanity.com/paper/2206.04803/) (2022)
- [Evolve Path Tracer: Early Detection of Malicious Addresses in Cryptocurrency](https://mlsanity.com/paper/2301.05412/) (2023)
- [EX-Graph: A Pioneering Dataset Bridging Ethereum and X](https://mlsanity.com/paper/2310.01015/) (2023)
- [Time-aware Metapath Feature Augmentation for Ponzi Detection in Ethereum](https://mlsanity.com/paper/2210.16863/) (2022)
- [SmartBugs 2.0: An Execution Framework for Weakness Detection in Ethereum Smart Contracts](https://mlsanity.com/paper/2306.05057/) (2023)

## Built on by (most cited first, 2 of 2)

- [Regulatory Graphs and GenAI for Real-Time Transaction Monitoring and Compliance Explanation in Banking](https://mlsanity.com/paper/2506.01093/) (2025)
- [ETH-TraceBench: A Large-Scale Event-Stream Benchmark for Ethereum DeFi under Temporal, Protocol, and Contract Shift](https://mlsanity.com/paper/2609.23659/) (2026)

## Abstract (arXiv)

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.