Bitcoin Research with a Transaction Graph Dataset

TL;DR

This paper presents a comprehensive large-scale Bitcoin transaction graph dataset with labeled nodes and edges, enabling advanced research in blockchain analysis and graph neural networks.

Contribution

It introduces the largest publicly available Bitcoin transaction dataset with labels and benchmarks, facilitating new research opportunities.

Findings

Graph neural networks achieve baseline performance on node classification tasks.

The dataset covers 13 years of Bitcoin transactions with detailed timestamped data.

Multiple use cases demonstrate the dataset's versatility beyond Bitcoin analysis.

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.