The Temporal Graph of Bitcoin Transactions

TL;DR

This paper introduces a comprehensive, ML-compatible temporal graph model of Bitcoin transactions, enabling advanced analysis of its economic topology and facilitating research in anomaly detection, classification, and market insights.

Contribution

The authors reconstruct a large-scale, temporal, heterogeneous graph of Bitcoin transactions, providing datasets, tools, and methods for ML research on Bitcoin's ecosystem.

Findings

Reconstructed a graph with over 2.4 billion nodes and 39.72 billion edges.

Provided sampling methods and tools for analyzing Bitcoin transaction data.

Enabled large-scale graph ML benchmarking and anomaly detection.

Abstract

Since its 2009 genesis block, the Bitcoin network has processed >1.08 billion (B) transactions representing >8.72B BTC, offering rich potential for machine learning (ML); yet, its pseudonymity and obscured flow of funds inherent in its UTxO-based design, have rendered this data largely inaccessible for ML research. Addressing this gap, we present an ML-compatible graph modeling the Bitcoin's economic topology by reconstructing the flow of funds. This temporal, heterogeneous graph encompasses complete transaction history up to block 863000, consisting of >2.4B nodes and >39.72B edges. Additionally, we provide custom sampling methods yielding node and edge feature vectors of sampled communities, tools to load and analyze the Bitcoin graph data within specialized graph databases, and ready-to-use database snapshots. This comprehensive dataset and toolkit empower the ML community to tackle Bitcoin's intricate ecosystem at scale, driving progress in applications such as anomaly detection, address classification, market analysis, and large-scale graph ML benchmarking. Dataset and code available at https://github.com/B1AAB/EBA