Predicting Channel Closures in the Lightning Network with Machine Learning

TL;DR

This paper investigates predicting Lightning Network channel closures using machine learning on gossip data, revealing that simple models based on temporal and behavioral features outperform complex graph-based approaches.

Contribution

It introduces a dataset and benchmarks various ML models for predicting channel closures, highlighting the importance of temporal and behavioral signals over network topology.

Findings

Temporal and behavioral features are the most predictive signals.

Simple MLP models outperform complex graph neural networks.

Privacy constraints limit the predictability of channel closures.

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.