Bitcoin Research with a Transaction Graph Dataset

Paper: arXiv 2411.10325

Authors: Hugo Schnoering, Michalis Vazirgiannis

Abstract

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.

Complexity vs Empirical Score

• Math Complexity: 2.5/10
• Empirical Rigor: 8.0/10
• Quadrant: Street Traders — practical and empirical, lighter on theory

Why this score: This paper presents a massive, highly valuable public dataset mapping 13 years of Bitcoin transaction graphs with temporal and node-level entity annotations. While mathematically lightweight, it features exceptional empirical utility, clear graph-construction heuristics, and fully open-sourced code and data for benchmarking machine learning models.

Research Flowchart

  flowchart TD
    A[Research Goal: Create a large-scale Bitcoin dataset & benchmark for research] --> B[Data Collection: 13 years of raw Bitcoin blockchain data]
    B --> C[Graph Construction: 252M nodes & 785M edges with timestamps & ground-truth labels]
    C --> D[Model Training: Train Graph Neural Networks for node classification]
    D --> E[Key Outcomes: Benchmark baselines, public dataset release & multi-domain use cases]

Cited References (28)

1. Tracking ransomware end-to-end (2018)
2. Bitcoin: A peer-to-peer electronic cash system (2008)
3. Assessing the Efficacy of Heuristic-Based Address Clustering for Bitcoin — → 2403.00523
4. Heuristics for detecting coinjoin transactions on the bitcoin blockchain — → 2311.12491
5. Colored Coins (2024)
6. Language models are few-shot learners (2020)
7. Introducing ChatGPT (2024)
8. CoinMarketCap CEXs (2024)

2 of 28 cited works are in the corpus; resolved via arXiv source bundles, Semantic Scholar and OpenAlex. Corpus-wide: 2518 linked papers.