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A percolation model for the emergence of the Bitcoin Lightning Network
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s41598-020-61137-5.pdf | Published version | 1.84 MB | Adobe PDF | View/Open |
Title: | A percolation model for the emergence of the Bitcoin Lightning Network |
Authors: | Bartolucci, S Caccioli, F Vivo, P |
Item Type: | Journal Article |
Abstract: | The Lightning Network is a so-called second-layer technology built on top of the Bitcoin blockchain to provide "off-chain" fast payment channels between users, which means that not all transactions are settled and stored on the main blockchain. In this paper, we model the emergence of the Lightning Network as a (bond) percolation process and we explore how the distributional properties of the volume and size of transactions per user may impact its feasibility. The agents are all able to reciprocally transfer Bitcoins using the main blockchain and also - if economically convenient - to open a channel on the Lightning Network and transact "off chain". We base our approach on fitness-dependent network models: as in real life, a Lightning channel is opened with a probability that depends on the "fitness" of the concurring nodes, which in turn depends on wealth and volume of transactions. The emergence of a connected component is studied numerically and analytically as a function of the parameters, and the phase transition separating regions in the phase space where the Lightning Network is sustainable or not is elucidated. We characterize the phase diagram determining the minimal volume of transactions that would make the Lightning Network sustainable for a given level of fees or, alternatively, the maximal cost the Lightning ecosystem may impose for a given average volume of transactions. The model includes parameters that could be in principle estimated from publicly available data once the evolution of the Lighting Network will have reached a stationary operable state, and is fairly robust against different choices of the distributions of parameters and fitness kernels. |
Issue Date: | 11-Mar-2020 |
Date of Acceptance: | 17-Feb-2020 |
URI: | http://hdl.handle.net/10044/1/80954 |
DOI: | 10.1038/s41598-020-61137-5 |
ISSN: | 2045-2322 |
Publisher: | Nature Publishing Group |
Journal / Book Title: | Scientific Reports |
Volume: | 10 |
Issue: | 1 |
Copyright Statement: | © 2020 The Author(s). This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Cre-ative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not per-mitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/. |
Publication Status: | Published |
Conference Place: | England |
Open Access location: | https://www.nature.com/articles/s41598-020-61137-5 |
Article Number: | ARTN 4488 |
Appears in Collections: | Imperial College Business School |
This item is licensed under a Creative Commons License