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使用交易图形数据集进行比特币研究
Hugo Schnoering1,2, Michalis Vazirgiannis3,4
1LIX, Ecole Polytechnique, Palaiseau, 91120, France. hschnoering@coinshares.com.
Scientific data
|March 8, 2025
概括
本研究介绍了有史以来最大的比特币交易图表数据集,其中包括时间注释和标记子集,用于高级区块链分析. 该数据集使得使用图形神经网络进行欺诈检测和网络分析的新研究成为可能.
科学领域:
- 计算机科学 计算机科学
- 数据科学数据科学数据科学
- 区块链技术 区块链技术
背景情况:
- 比特币的分散性需要强大的分析工具.
- 现有的数据集缺乏全面的区块链研究的规模和时间细节.
- 图形分析的进步可以揭示加密货币交易中的复杂模式.
研究的目的:
- 为了呈现比特币交易的最大的公开可用的,暂时注释的图形数据集.
- 促进区块链分析,网络分析和监督学习方面的研究.
- 为实体识别和节点分类任务提供标记子集.
主要方法:
- 构建一个大规模的图形数据集,有25200万个节点和7.85亿个边缘.
- 所有节点和边缘的时间标记用于时间分析.
- 标记子集的创建:34,000个实体类型的节点和100,000个名称/类型的地址.
主要成果:
- 该数据集是比特币交易网络分析的最广泛的资源.
- 基于使用图形神经网络进行节点分类而建立的基线性能.
- 在欺诈检测,网络分析和时间图学习方面已证明适用性.
结论:
- 发布的数据集显著推进了区块链分析领域.
- 它支持广泛的应用,从加密货币取证到时间网络研究.
- 对数据集,代码和基准的开放访问促进了进一步的研究和开发.
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