# Bitcoin Research with a Transaction Graph Dataset

arXiv: https://arxiv.org/abs/2411.10325 · HTML: https://mlsanity.com/paper/2411.10325/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 40 references that are in the map.
- First posted on arXiv 15 Nov 2024.

## Builds on (most cited first, 19 of 19)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [Inductive Representation Learning on Large Graphs](https://mlsanity.com/paper/1706.02216/) (2017)
- [How Powerful are Graph Neural Networks?](https://mlsanity.com/paper/1810.00826/) (2018)
- [Graph Convolutional Neural Networks for Web-Scale Recommender Systems](https://mlsanity.com/paper/1806.01973/) (2018)
- [Evaluating the Logical Reasoning Ability of ChatGPT and GPT-4](https://mlsanity.com/paper/2304.03439/) (2023)
- [Anti-Money Laundering in Bitcoin: Experimenting with Graph Convolutional Networks for Financial Forensics](https://mlsanity.com/paper/1908.02591/) (2019)
- [DistDGL: Distributed Graph Neural Network Training for Billion-Scale Graphs](https://mlsanity.com/paper/2010.05337/) (2020)
- [Ransomware Payments in the Bitcoin Ecosystem](https://mlsanity.com/paper/1804.04080/) (2018)
- [The Shape of Money Laundering: Subgraph Representation Learning on the Blockchain with the Elliptic2 Dataset](https://mlsanity.com/paper/2404.19109/) (2024)
- [On the Economic Significance of Ransomware Campaigns: A Bitcoin Transactions Perspective](https://mlsanity.com/paper/1804.01341/) (2018)
- [BABD: A Bitcoin Address Behavior Dataset for Pattern Analysis](https://mlsanity.com/paper/2204.05746/) (2022)
- [Spams meet Cryptocurrencies: Sextortion in the Bitcoin Ecosystem](https://mlsanity.com/paper/1908.01051/) (2019)
- [Watch Your Back: Identifying Cybercrime Financial Relationships in Bitcoin through Back-and-Forth Exploration](https://mlsanity.com/paper/2206.00375/) (2022)
- [Bitcoin-NG: A Scalable Blockchain Protocol](https://mlsanity.com/paper/1510.02037/) (2015)
- [Heuristics for Detecting CoinJoin Transactions on the Bitcoin Blockchain](https://mlsanity.com/paper/2311.12491/) (2023)
- [Pattern Analysis of Money Flow in the Bitcoin Blockchain](https://mlsanity.com/paper/2207.07315/) (2022)
- [Assessing the Efficacy of Heuristic-Based Address Clustering for Bitcoin](https://mlsanity.com/paper/2403.00523/) (2024)

## Built on by (most cited first, 3 of 3)

- [Algorithmic Compliance and Regulatory Loss in Digital Assets](https://mlsanity.com/paper/2603.04328/) (2026)
- [A Depth-Aware Comparative Study of Euclidean and Hyperbolic Graph Neural Networks on Bitcoin Transaction Systems](https://mlsanity.com/paper/2603.16080/) (2026)
- [Refining Heuristic-Based Bitcoin Address Clustering with Graph Neural Networks](https://mlsanity.com/paper/2609.01942/) (2026)

## Abstract (arXiv)

Bitcoin, launched in 2008 by Satoshi Nakamoto, established a new digital economy where value can be stored and transferred in a fully decentralized manner - alleviating the need for a central authority. This paper introduces a large scale dataset in the form of a transactions graph representing transactions between Bitcoin users along with a set of tasks and baselines. The graph includes 252 million nodes and 785 million edges, covering a time span of nearly 13 years of and 670 million transactions. Each node and edge is timestamped. As for supervised tasks we provide two labeled sets i. a 33,000 nodes based on entity type and ii. nearly 100,000 Bitcoin addresses labeled with an entity name and an entity type. This is the largest publicly available data set of bitcoin transactions designed to facilitate advanced research and exploration in this domain, overcoming the limitations of existing datasets. Various graph neural network models are trained to predict node labels, establishing a baseline for future research. In addition, several use cases are presented to demonstrate the dataset's applicability beyond Bitcoin analysis. Finally, all data and source code is made publicly available to enable reproducibility of the results.