Channel Balance Interpolation in the Lightning Network via Machine Learning

TL;DR

This paper investigates using machine learning to predict channel balances in the Lightning Network, aiming to improve pathfinding and network efficiency by leveraging node and channel features.

Contribution

It introduces a novel approach to channel balance interpolation using machine learning, outperforming heuristic baselines in predictive accuracy.

Findings

ML models outperform heuristics by 10% in accuracy

Predictive features effectively estimate channel balances

Model performance suggests potential for network optimization

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.