An Exposition of Pathfinding Strategies Within Lightning Network Clients

Abstract

The Lightning Network is a peer-to-peer network designed to address Bitcoin’s scalability challenges, facilitating rapid, cost-effective, and instantaneous transactions through bidirectional, blockchain-backed payment channels among network peers. Due to a source-based routing of payments, different pathfinding strategies are used in practice, trading off different objectives for each other such as payment reliability and routing fees. This paper explores differences within pathfinding strategies used by prominent Lightning Network node implementations, which include different underlying cost functions and different constraints, as well as different greedy algorithms of shortest path-type. Surprisingly, we observe that the pathfinding problems that most LN node implementations attempt to solve are NP-complete, and cannot be guaranteed to be optimally solved by the variants of Dijkstra’s algorithm currently deployed in production. Through comparative analysis and simulations, we evaluate efficacy of different pathfinding strategies across metrics such as success rate, fees, path length, and timelock. Our experiments indicate that the strategies used by LND tend to be advantageous in terms of payment reliability, Eclair tends to result in paths with low fees, and that LDK exhibits average reliability with larger fee levels for smaller payment amounts; furthermore, CLN stands out for its minimal timelock paths. Additionally, we investigate the impact of Lightning node connectivity levels on routing efficiency. The findings of our analysis provide insights towards future improvements of pathfinding strategies and algorithms used within the Lightning Network.

Publication
2025 IEEE International Conference on Blockchain and Cryptocurrency (ICBC), Pisa, Italy, 2025, pp. 1-17, doi: 10.1109/ICBC64466.2025.11114515
Sindura Saraswathi
Sindura Saraswathi
Ph.D. Student
Department of Computer Science
Institute for Artificial Intelligence

Broadly, I care about how to make Bitcoin actually usable at scale faster payments, more reliable routing, and safer ways to hold coins. Right now my research focuses on two main threads. On the Lightning side, I study pathfinding and multi-part payments when channel liquidity is uncertain - how to pick routes that are both cheap and likely to succeed. On the custody side, I work on optimal threshold signature schemes for Bitcoin, asking how to choose m-of-n setups that balance security, and usability complexity.

Christian Kümmerle
Christian Kümmerle
Assistant Professor
School of Data, Mathematical, and Statistical Sciences
Department of Computer Science
Institute for Artificial Intelligence

I am passionate about the potential of AI and operations research for Lightning network operations and about efficiency gains within AI models that can be unlocked by combining smoothing, parsimony, structured optimization.