Learning a Scalable Algorithm for Improving Betweenness in the Lightning Network

This paper presents a scalable algorithm for solving the Maximum Betweenness Improvement Problem as it appears in the Bitcoin Lightning Network. In this approach, each node is embedded with a feature vector whereby an Advantage Actor-Critic model identifies key nodes in the network that a joining node should open channels with to maximize its own expected routing opportunities. This model is trained using a custom built environment, lightning-gym, which can randomly generate small scale-free networks or import snapshots of the Lightning Network. After 100 training episodes on networks with 128 nodes, this A2C agent can recommend channels in the Lightning Network that consistently outperform recommendations from centrality based heuristics and in less time. This approach gives nodes in the network access to a fast, low resource, algorithm to increase their expected routing opportunities.