Scholarlink

<p>The Lightning Network (LN) is the most widely adopted second-layer solution for Bitcoin, enabling fast, low-cost transactions through a decentralized payment channel network. Despite its growing importance and the increasing interest from researchers across disciplines, progress in LN research is often impeded by limited access to structured, validated, and reproducible network data. In this paper, we present a curated collection of LN network snapshots spanning from January 2019 to July 2023, reconstructed from publicly available gossip message archives. We apply rigorous consistency checks, and enrich node metadata with city-level geolocation data derived from public IP addresses. The resulting dataset captures the temporal and spatial evolution of the LN, addresses a critical research gap, and provides a reproducible foundation for future empirical studies on network structure and dynamics – accessible not only to the computer science community but also to researchers in cryptocurrency and economics.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Geolocated Lightning Network topology snapshots: A dataset covering 2019–2023

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年12月10日

查看全文
  • Danila Valko,
  • Jorge Marx Gómez

摘要

The Lightning Network (LN) is the most widely adopted second-layer solution for Bitcoin, enabling fast, low-cost transactions through a decentralized payment channel network. Despite its growing importance and the increasing interest from researchers across disciplines, progress in LN research is often impeded by limited access to structured, validated, and reproducible network data. In this paper, we present a curated collection of LN network snapshots spanning from January 2019 to July 2023, reconstructed from publicly available gossip message archives. We apply rigorous consistency checks, and enrich node metadata with city-level geolocation data derived from public IP addresses. The resulting dataset captures the temporal and spatial evolution of the LN, addresses a critical research gap, and provides a reproducible foundation for future empirical studies on network structure and dynamics – accessible not only to the computer science community but also to researchers in cryptocurrency and economics.