Channel Balance Interpolation in the Lightning Network via Machine Learning
This work addresses the optimization of pathfinding algorithms for the Lightning Network, but it is incremental as it builds on existing balance probing and multipath payment protocols.
This research tackled the problem of predicting channel balances in the Bitcoin Lightning Network using machine learning models, resulting in a model that outperformed a baseline by 10% in experimental evaluation.
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.