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Channel Balance Interpolation in the Lightning Network via Machine Learning

Vincent Davis, Emanuele Rossi, Vikash Singh

TL;DR

This work addresses the problem of predicting channel balance proportions $p_{(u,v)}$ in the Lightning Network to improve multi-hop pathfinding. It evaluates six ML models, with a joint model that incorporates node features, edge features, and graph-position information via Laplacian eigenvectors, outperforming heuristic baselines such as Equal Split and Local HTLC. Using a December 2023 LN snapshot and crowdsourced data, the best model achieves MAE$_p$ = 0.259 and MAE$_y$ = 1.08 with a correlation $R$ = 0.612 and $R^2$ = 0.365, highlighting the strong predictive value of topology-aware features. The work also finds Positional Encoding to be the most influential feature, indicating the critical role of network structure in balance distribution; it discusses privacy and overhead improvements over probing and outlines a pathfinding integration that leverages predicted balances to reduce failed payments in practice.

Abstract

The Bitcoin Lightning Network is a Layer 2 payment protocol that addresses Bitcoin's scalability by facilitating quick and cost effective transactions through payment channels. This research explores the feasibility of using machine learning models to interpolate channel balances within the network, which can be used for optimizing the network's pathfinding algorithms. While there has been much exploration in balance probing and multipath payment protocols, predicting channel balances using solely node and channel features remains an uncharted area. This paper evaluates the performance of several machine learning models against two heuristic baselines and investigates the predictive capabilities of various features. Our model performs favorably in experimental evaluation, outperforming by 10% against an equal split baseline where both edges are assigned half of the channel capacity.

Channel Balance Interpolation in the Lightning Network via Machine Learning

TL;DR

This work addresses the problem of predicting channel balance proportions in the Lightning Network to improve multi-hop pathfinding. It evaluates six ML models, with a joint model that incorporates node features, edge features, and graph-position information via Laplacian eigenvectors, outperforming heuristic baselines such as Equal Split and Local HTLC. Using a December 2023 LN snapshot and crowdsourced data, the best model achieves MAE = 0.259 and MAE = 1.08 with a correlation = 0.612 and = 0.365, highlighting the strong predictive value of topology-aware features. The work also finds Positional Encoding to be the most influential feature, indicating the critical role of network structure in balance distribution; it discusses privacy and overhead improvements over probing and outlines a pathfinding integration that leverages predicted balances to reduce failed payments in practice.

Abstract

The Bitcoin Lightning Network is a Layer 2 payment protocol that addresses Bitcoin's scalability by facilitating quick and cost effective transactions through payment channels. This research explores the feasibility of using machine learning models to interpolate channel balances within the network, which can be used for optimizing the network's pathfinding algorithms. While there has been much exploration in balance probing and multipath payment protocols, predicting channel balances using solely node and channel features remains an uncharted area. This paper evaluates the performance of several machine learning models against two heuristic baselines and investigates the predictive capabilities of various features. Our model performs favorably in experimental evaluation, outperforming by 10% against an equal split baseline where both edges are assigned half of the channel capacity.
Paper Structure (32 sections, 12 equations, 5 figures, 3 tables)

This paper contains 32 sections, 12 equations, 5 figures, 3 tables.

Table of Contents

  1. Introduction
  2. Background
  3. Bitcoin
  4. Lightning Network
  5. Motivation
  6. Related Work
  7. Problem Formulation
  8. Data Collection and Preprocessing
  9. Preprocessing Steps
  10. Preliminary Analysis
  11. Feature Analysis
  12. Methodology
  13. Modeling
  14. Node Features
  15. Edge Features
  16. ...and 17 more sections

Figures (5)

  • Figure 1: Distribution of channel capacities, crowdsourced edge balances, and normalized edge balances in the Lightning Network.
  • Figure 2: Histogram of errors, a scatterplot of the actual vs. predicted values, and a confusion matrix of the best performing model.
  • Figure :
  • Figure : Features and their correlations to channel balance.
  • Figure : Model Performance