构建比特币资金流动的时序异构图，助力机器学习研究
The Temporal Graph of Bitcoin Transactions
- 基于UTxO模型重建资金流，构建包含24亿节点、397.2亿边的时序图
- 覆盖超10.8亿笔交易，支持异常检测、地址分类等应用
- 提供采样工具与数据库快照，适合大规模图机器学习研究
自2009年创世区块以来，比特币网络已处理超过10.8亿笔交易，涉及超过87.2亿比特币，为机器学习研究提供了丰富数据；然而，其基于UTxO的设计导致用户伪匿名性及资金流向模糊，使数据难以用于机器学习。为此，我们提出一种可兼容机器学习的图模型，重构比特币经济拓扑结构。该时序异构图涵盖至区块863000的完整交易历史，包含超过24亿个节点和397.2亿条边。此外，我们提供定制采样方法，生成社区的节点与边特征向量，以及用于在专用图数据库中加载和分析比特币图数据的工具与即用型数据库快照。该全面数据集与工具包使机器学习社区得以规模化研究比特币复杂生态系统，推动异常检测、地址分类、市场分析及大规模图机器学习基准测试的发展。数据与代码见 https://github.com/B1AAB/EBA
原文摘要 · Abstract (English)
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
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