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English(EN) Population-Calibrated Graph Screening at 835-Million-Address Scale, with Label-Free Transfer to New Chains

使用跨五条EVM链的交易图分析筛选区块链地址

研究人员开发了一种新颖的区块链地址筛选系统,该系统通过分析地址在交易图中的位置来进行筛选,而不是依赖传统的标签查找。该系统构建了一个庞大的图谱,涵盖了五条以太坊虚拟机(EVM)链上的8.35亿个地址和超过150亿个边,包括Ethereum、Tron、Arbitrum和Gnosis。该方法证明了将学习到的模式无标签地有效迁移到新链的能力,并已成功在公开指定之前显著标记出外部注册事件。 AI

影响 这项研究通过实现对潜在非法地址更强大、更主动的识别,有望改善区块链生态系统的合规性和安全性。

排序理由 该条目描述了一篇研究论文,其中详细介绍了一种新的区块链地址筛选系统。[lever_c_demoted from research: ic=1 ai=0.4]

在 Hugging Face Daily Papers 阅读 →

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

使用跨五条EVM链的交易图分析筛选区块链地址

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该条目描述了一篇研究论文,其中详细介绍了一种新的区块链地址筛选系统。[lever_c_demoted from research: ic=1 ai=0.4]
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报道来源 [1]

  1. Hugging Face Daily Papers TIER_1 English(EN) ·

    人口校准图谱筛选,规模达8.35亿地址,实现无标签迁移至新链

    Compliance screening of blockchain addresses is, in practice, a lookup against sanctions registries plus clustering heuristics; it fails on unlabelled addresses and on chains with no label coverage at all. We describe a deployed system that scores an address by its position in a …