发表机构
University of Rome “Tor Vergata”; University of Camerino; Aalto University; IMDEA Networks（罗马托尔维加塔大学; 卡梅里诺大学; 阿尔托大学; IMDEA网络研究所）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文通过爬取比特币和狗狗币P2P网络数据，分析对等表分布并模拟网络演化，发现比特币维护规则产生重尾可见性，但网络仍连接良好且结构稳健。
AI 中文摘要
现代加密货币所依托的P2P网络的全局结构在设计上是隐藏的：每个节点仅知道其邻居，并维护一个本地的IP地址“对等表”。节点的对等表可视为该节点对当前网络中节点集合的“局部视图”：它不断用从邻居收集的信息进行更新，并在需要时用于建立新连接。节点对等表的维护规则决定了网络的全局结构及其演化。尽管网络的全局结构对任何网络节点以及任何外部观察者都是未知的，但邻居之间信息交换的规则与网络“爬虫”的设计和使用兼容，这些爬虫可以查询节点并提取关于其对等表的一些信息。在本文中，我们首先分析了从比特币和狗狗币P2P网络节点爬取的数据，以收集对等表中IP地址分布的信息，并估计网络规模和流失率的演化；然后，我们利用这些估计来设定比特币网络模拟的参数，并分析从模拟中得到的网络结构的演化。总体而言，我们的结果表明，比特币的对等表维护规则导致了非均匀、重尾的可见性模式，同时仍然生成了一个连接良好且结构稳健的网络。
英文摘要
The global structure of P2P networks underlying modern cryptocurrencies is hidden by design: each node only knows its neighbors and maintains a local \textit{peer table} of IP addresses. The peer table of a node can be seen as the node's ``local view'' of the set of nodes currently in the network: it is constantly updated with information collected from the neighbors and it is used by the node to establish new connections when needed. The maintenance rules of the nodes' peer tables determine the global structure of the network and its evolution. Even though the global structure is unknown to any node of the network as well as to any external observer, the rules for the exchange of information between neighbors are compatible with the design and use of network \textit{crawlers} that can query the nodes and extract some information about their peer tables. In this paper we first analyze the data we crawled from nodes of the Bitcoin and Dogecoin P2P networks, to collect information about the distribution of IP addresses in the peer tables and to estimate the evolution of network size and churn rate; we then use the estimates to set up the parameters for a simulation of the Bitcoin network and we analyze the evolution of the network structure that we get from the simulation. Overall, our results show that Bitcoin's peer-table maintenance rules induce non-uniform, heavy-tailed visibility patterns while nevertheless generating a well-connected and structurally robust network.
