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Bitcoin Research with a Transaction Graph Dataset

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  • 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.

Suggested Citation

  • Hugo Schnoering & Michalis Vazirgiannis, 2024. "Bitcoin Research with a Transaction Graph Dataset," Papers 2411.10325, arXiv.org.
    • Handle: RePEc:arx:papers:2411.10325
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    File URL: http://arxiv.org/pdf/2411.10325
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    References listed on IDEAS

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    1. Hugo Schnoering & Michalis Vazirgiannis, 2023. "Heuristics for Detecting CoinJoin Transactions on the Bitcoin Blockchain," Papers 2311.12491, arXiv.org.
    2. Hugo Schnoering & Pierre Porthaux & Michalis Vazirgiannis, 2024. "Assessing the Efficacy of Heuristic-Based Address Clustering for Bitcoin," Papers 2403.00523, arXiv.org.
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