In this journal version, we extended our previous conference paper published by Springer in Financial Cryptography and Data Security 2022 by 40%.
We gathered recent data and updated our results.
Our enhanced work includes an analysis with multi-path payments, introducing new results and comparisons.
We discussed decentralization in payment channels and the policy significance in the paper.
Abstract
Blockchain technology has a huge impact on our digital society by enabling a more decentralized economy and policy making. This decentralization is also pivotal in payment Payment channel networks (PCNs), including the Lightning Network, have emerged as a promising solution to the scalability challenges that many blockchain-based cryptocurrencies, like Bitcoin, grapple with. These PCNs, while innovative, also inherit the rigorous dependability demands of the blockchain. A pivotal aspect of this dependability is the need for a high degree of decentralization, essential for mitigating liquidity bottlenecks and on-path attacks.
Driven by this imperative, our research embarks on an empirical centrality analysis of the Lightning Network, with a keen focus on the betweenness centrality distribution of its routing system. Utilizing an extensive dataset, sourced from several millions of broadcasted messages via the gossip protocol, we introduce the TimeMachine tool, an innovative method that allows for a temporal exploration of the network’s evolution.
Our findings reveal that while the Lightning Network exhibits a commendable level of decentralization, there is a discernible skew: a limited set of nodes command a significant portion of the transactions. Alarmingly, over the past two years, the network’s centrality has surged, with the inequality, as gauged by the Gini index, rising by over 15 uptick of approximately 5 in. This research not only uncovers critical insights into the Lightning Network’s structural dynamics but also raises the question about strategies and policies that ensure its sustained decentralization in the face of evolving challenges such as security vulnerabilities, potential monopolistic tendencies, liquidity bottlenecks, the risk of transaction censorship and many more.
Thanks for the work, i will re-read it again when i have more time:
Two questions:
For the 'Betweenness centrality' in the paper, what does 'shortest path' mean in your context? Is it really in terms of number of hops? I have found that using this notion of 'shortest path' is quickly limited in what information it brings, since it considers that every channel is equal (in capacity to route any amount, in any direction, at any moment of time, and in cost to route that payment). We are not in a purely mathematical graph, but on the lightning network. For me, it seems that shortest path in LN should more be a measure of 'cheapest path', AND, 'path that can actually route the payment', not just a number of hops. (Spoiler alert about what might be in next part of my articles about what i learnt building a LN channel advisor i guess)
Is that historical channel update data openly available so i can also use it for my LN channel advisor, instead of just relying on immediate LN network state?