Predicting Channel Closures in the Lightning Network with Machine Learning

2026-05-12

AI Summary

This study addresses the prediction of payment channel closure types—cooperative or unilateral—in the Lightning Network, aiming to mitigate fund lock-up risks and enhance network reliability. The task is formulated as a temporal link classification problem on dynamic graphs, leveraging a two-year dataset derived from publicly available gossip data. The authors systematically evaluate various approaches, including MLPs, temporal graph neural networks, and spectral encodings. Experimental results reveal that predictive signals primarily stem from node-level behavioral and temporal features—such as recent activity and historical closure patterns—rather than global topological structure. Due to the protocol’s privacy-preserving design, fundamental limits exist on predictability using only gossip data. Notably, a simple MLP outperforms more complex graph-based models, achieving state-of-the-art performance on public data. Code and datasets are publicly released to support future research.

Abstract

The Lightning Network (LN) is a second-layer protocol for Bitcoin designed to enable fast and cost-efficient off-chain transactions. Channels in the LN can be closed either by mutual agreement or unilaterally through a forced closure, which locks the involved capital for an extended period and degrades network reliability. In this paper, we study the problem of predicting channel closure types from publicly available gossip data, framing it as a temporal link classification task over the evolving channel graph. We construct a dataset spanning over two years of LN activity and benchmark a range of machine learning approaches, from MLPs to temporal graph neural networks and spectral encodings. Our experiments reveal that the dominant predictive signals are temporal and behavioural, namely how recently each endpoint was active and the per-node history of past closures, while the surrounding network topology provides no additional benefit. We find that a simple MLP operating on edge-level features, node-level event counts, and temporal patterns outperforms all graph-based approaches, and discuss how the inherent privacy of the LN, where critical information such as channel balances and payment flows remains hidden, fundamentally limits the predictability of closures from gossip data alone. We publicly release the dataset and code at https://github.com/AmbossTech/ln-channel-closure-prediction to encourage further research on this practically relevant task.

Problem

• Lightning Network
• channel closure prediction
• machine learning
• temporal link classification
• off-chain transactions

Innovation

• Lightning Network
• channel closure prediction
• temporal link classification
• machine learning
• graph neural networks