Channel Balance Interpolation in the Lightning Network via Machine Learning

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 use of 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, reducing the relative error by 27% compared to an equal split baseline where both edges are assigned half of the channel capacity.