AbstractAbstract

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  AmbossTech/ln-channel-closure-prediction to encourage further research on this practically relevant task.

IntroductionIntroduction

The Lightning Network (LN) is a second-layer protocol on top of Bitcoin that moves most payments off-chain. Two users open a payment channel by jointly locking Bitcoin on-chain, route an arbitrary number of off-chain payments through it, and eventually settle back on-chain by closing the channel.



Figure 1:Overview of the channel closure prediction task. Left: the Lightning Network evolves over time as channels open and close, forming a temporal graph. Right: given the current graph state at time t, we predict whether each open channel will remain open, close cooperatively (mutual), or be force-closed within a window Δt.

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