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A percolation model for the emergence of the Bitcoin Lightning Network

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...

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Autores principales: Bartolucci, Silvia, Caccioli, Fabio, Vivo, Pierpaolo
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7066163/
https://www.ncbi.nlm.nih.gov/pubmed/32161323
http://dx.doi.org/10.1038/s41598-020-61137-5
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author Bartolucci, Silvia
Caccioli, Fabio
Vivo, Pierpaolo
author_facet Bartolucci, Silvia
Caccioli, Fabio
Vivo, Pierpaolo
author_sort Bartolucci, Silvia
collection PubMed
description 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.
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spelling pubmed-70661632020-03-19 A percolation model for the emergence of the Bitcoin Lightning Network Bartolucci, Silvia Caccioli, Fabio Vivo, Pierpaolo Sci Rep Article 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. Nature Publishing Group UK 2020-03-11 /pmc/articles/PMC7066163/ /pubmed/32161323 http://dx.doi.org/10.1038/s41598-020-61137-5 Text en © The Author(s) 2020 Open Access 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 Creative 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 permitted 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/.
spellingShingle Article
Bartolucci, Silvia
Caccioli, Fabio
Vivo, Pierpaolo
A percolation model for the emergence of the Bitcoin Lightning Network
title A percolation model for the emergence of the Bitcoin Lightning Network
title_full A percolation model for the emergence of the Bitcoin Lightning Network
title_fullStr A percolation model for the emergence of the Bitcoin Lightning Network
title_full_unstemmed A percolation model for the emergence of the Bitcoin Lightning Network
title_short A percolation model for the emergence of the Bitcoin Lightning Network
title_sort percolation model for the emergence of the bitcoin lightning network
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7066163/
https://www.ncbi.nlm.nih.gov/pubmed/32161323
http://dx.doi.org/10.1038/s41598-020-61137-5
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