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English(EN) Refining Heuristic-Based Bitcoin Address Clustering with Graph Neural Networks

图神经网络改进比特币地址聚类

研究人员开发了一种使用图神经网络(GNNs)改进比特币地址聚类的新方法。该方法旨在提高识别属于同一用户的地址的准确性,解决了现有基于启发式方法可能导致错误的局限性。这项工作包括发布一个新的比特币交易图数据集、一种用于学习与启发式方法对齐的地址嵌入的方法,以及一种用于更精细分析和检测可疑合并的层次聚类技术。 AI

影响 通过提高在假名网络上识别用户级别活动的准确性,增强了区块链分析能力。

排序理由 该集群包含一篇学术论文,详细介绍了使用图神经网络改进比特币地址聚类的新方法和数据集。[lever_c_demoted from research: ic=1 ai=0.7]

在 arXiv cs.LG 阅读 →

AI 生成摘要 · Google Gemini · 来自 1 个来源。 我们如何撰写摘要 →

图神经网络改进比特币地址聚类

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该集群包含一篇学术论文,详细介绍了使用图神经网络改进比特币地址聚类的新方法和数据集。[lever_c_demoted from research: ic=1 ai=0.7]
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报道来源 [1]

  1. arXiv cs.LG TIER_1 English(EN) · Hugo Schnoering, Roman Bresson, Michalis Vazirgiannis ·

    使用图神经网络精炼基于启发式的比特币地址聚类

    arXiv:2609.01942v1 Announce Type: new Abstract: Bitcoin's pseudonymous nature makes it challenging to analyze user-level activity, since a single user may control multiple identifiers (addresses). Existing heuristic-based methods attempt to identify addresses belonging to the sam…