Empirical evaluation of nodes and channels of the lightning network

Abstract

Off-chain networks provide an attractive solution to the scalability challenges faced by cryptocurrencies such as Bitcoin. While first interesting networks are emerging, we currently have relatively limited insights into the structure and distribution of these networks. Such knowledge, however, is useful, when reasoning about possible performance improvements or the security of the network. For example, information about the different node types and implementations in the network can help when planning the distribution of critical software updates. This paper reports on a large measurement study of Lightning, a leading off-chain network, considering recorded network messages over a period of more than two years. In particular, we present an approach to classify the node types (LND, C-Lightning and Eclair) in the network, and find that we can determine the implementation of 99.9% of nodes correctly in our data set. We then report on geographical aspects of the Lightning Network, showing that proximity is less relevant, and that the Lightning Network is particularly predominant in metropolitan areas. Furthermore, we address various aspects of channels in the Lightning Network combined with the data we classified. We also demonstrate that channel endpoints behave very fairly and rarely cheat, that the same channel endpoints tend not to reconnect after the channel connection has closed and that there are more inactive than active channels in the Lightning Network. As a contribution to the research community, we will release our experimental data together with this paper.