A GNN-Based Autopilot Recommendation Strategy to Mitigate Payment Channel Imbalance Problem in Bitcoin Lightning Network

The Bitcoin Lightning Network, as a second-layer solution for enhancing the scalability of Bitcoin transactions, facilitates transactions through payment channels between nodes. However, the rapid growth of the network and rising transaction volumes have exacerbated the challenge of managing payment channel imbalances. Payment channel imbalance, characterized by the concentration of liquidity in one direction, leads to a decrease in payment success rates, a reduction in the effective lifespan of payment channels, and a decline in the network’s overall efficiency and throughput. This study introduces a graph neural network-based recommendation strategy designed to enhance the Lightning Network’s autopilot system. The proposed approach proactively mitigates channel imbalances by optimizing channel recommendations, enabling dynamic and scalable liquidity management for network users. Simulations conducted using the CLoTH tool demonstrate a 45% increase in payment success rates, a 46% reduction in imbalanced channels, and a 14% increase in the lifespan of payment channels across the network compared to the existing autopilot recommendation strategies, and when compared with the commonly adopted circular rebalancing method, the proposed strategy achieves a 27% improvement in payment success rates. Additionally, we offer a comparative topological analysis between two snapshots of the LN, taken in November 2021 and August 2023, to facilitate unsupervised learning tasks. The results highlight an increase in network centralization alongside a decrease in the number of network size, emphasizing the growing need for decentralization strategies in the LN, such as the approach proposed in this study.