Author ORCID Identifier
Date Available
12-19-2022
Year of Publication
2022
Degree Name
Master of Science (MS)
Document Type
Master's Thesis
College
Engineering
Department/School/Program
Computer Science
First Advisor
Dr. Brent Harrison
Abstract
This paper presents a scalable algorithm for solving the Maximum Betweenness Improvement Problem as it occurs 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 perform competitively with recommendations from centrality based heuristics and in less time. This approach provides a fast, low resource, algorithm for nodes to increase their expected routing opportunities in the Lightning Network.
Digital Object Identifier (DOI)
https://doi.org/10.13023/etd.2022.432
Recommended Citation
Davis, Vincent, "Learning a Scalable Algorithm for Improving Betweenness in the Lightning Network" (2022). Theses and Dissertations--Computer Science. 123.
https://uknowledge.uky.edu/cs_etds/123
