利用机器学习预测闪电网络中的通道关闭
Predicting Channel Closures in the Lightning Network with Machine Learning
AI总结：
本文研究如何通过公开的 gossip 数据预测闪电网络通道关闭类型，发现时间行为特征比网络拓扑更有效，且简单 MLP 模型优于图方法。
摘要：
闪电网络（LN）是一种用于比特币的第二层协议，旨在实现快速且低成本的离线交易。LN 中的通道可通过双方协议或单方面强制关闭关闭，这会锁定资金并降低网络可靠性。本文研究如何从公开的 gossip 数据中预测通道关闭类型，将其框架为动态通道图上的时间链接分类任务。我们构建了一个覆盖两年 LN 活动的数据库，并基准测试了多种机器学习方法，包括 MLP、时序图神经网络和谱编码。实验发现，主导的预测信号是时间和行为特征，即每个端点最近的活跃程度和节点的历史关闭记录，而周围网络拓扑没有额外帮助。我们发现，一个基于边级特征、节点级事件计数和时间模式的简单 MLP 模型优于所有图方法，并讨论了 LN 内在隐私问题如何限制了仅从 gossip 数据中预测关闭的可预测性。我们公开发布数据集和代码以鼓励进一步研究此实际相关任务。
英文摘要：
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.
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