[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-119267-en":3,"doc-seo-119267-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},119267,7971461740886,"Theodore","https://ap-avatar.wpscdn.com/davatar_3d24733baf745e90a7e4bdd5f77d97b2",8,"Research & Report","Channel Balance Interpolation in the Lightning Network via Machine Learning","The Bitcoin Lightning Network is a Layer 2 payment protocol that improves Bitcoin scalability by enabling fast, low-cost transactions through payment channels. This research studies whether machine learning models can interpolate channel balances using node and channel information to support more accurate network pathfinding. It addresses an open gap, since prior work focused on balance probing and multipath payment protocols rather than direct balance prediction from features. The study compares six ML models against two heuristic baselines and evaluates predictive feature relevance, achieving about a 10% improvement over an equal-split baseline.","Channel Balance Interpolation in the Lightning Network via Machine Learning  \nVincent  \nAmboss Technologies Nashville, USA [v@amboss.tech](v@amboss.tech)  \nEmanuele Rossi* Amboss Technologies and VantAI  \nBarcelona, Spain [emanuele.rossi1909@gmail.com](emanuele.rossi1909@gmail.com)  \nVikash Singh*  \nStillmark San Francisco, USA [vikash@stillmark.com](vikash@stillmark.com)  \narXiv :2405 . 12087v1 [ cs .LG] 20 May 2024  \nAbstract—The Bitcoin Lightning Network is a Layer 2 payment protocol that addresses Bitcoin’s scalability by facilitating quick and cost-effective transactions through payment channels. This research explores the feasibility of using machine learning models to interpolate channel balances within the network, which can be used for optimizing the network’s pathfinding algorithms. While there has been much exploration in balance probing and multipath payment protocols, predicting channel balances using solely node and channel features remains an uncharted area. This paper evaluates the performance of several machine learning models against two heuristic baselines and investigates the predictive capabilities of various features. Our model performs favorably in experimental evaluation, outperforming by 10% against an equal split baseline where both edges are assigned half of the channel capacity.  \nIndex Terms—Bitcoin, Lightning Network, Pathfinding, Machine Learning, Payment Channel Network  \nI. INTRODUCTION  \nThe Lightning Network is a layer-2 protocol on the Bitcoin blockchain that enables rapid and cost-effective payments. Central to optimizing its efficiency is the accurate estimation of channel balances, which directly informs pathfinding strategies. Effective pathfinding circumvents the need for a cumbersome trial-and-error process in identifying viable payment routes, thereby streamlining operations within the network. While there have been advances in understanding balance generation [1], along with the exploration of reinforcement learning (RL) for pathfinding [2] and the development of multipath payment protocols [3], the prediction of channel balances is less explored territory.  \nThis gap in the literature motivates the current study, which seeks to determine whether it is feasible to interpolate the balances of channels within the Lightning Network accurately. It further aims to identify the most predictive features for balance interpolation, questioning whether these predictions can rely solely on node features, channel features, or a combination of both, and possibly enhanced by topological information of the network. To tackle these questions, the study evaluates the performance of two baseline methods against six machine learning (ML) models, each with a varying subset of features. The investigation reveals that ML models are indeed capable of predicting channel balances with a better degree of accuracy than heuristic methods like the equal split assumption. This  \n* Jointly supervised  \noutcome suggests that the predictive power of machine learning models could be leveraged to refine pathfinding algorithms. This study sheds light on the potential of specific features and how machine learning can enhance the network’s pathfinding efficiency.  \nThe remainder of this paper is organized as follows. Section II provides background information on Bitcoin and the Lightning Network. Section III elaborates on the specific problem and the motivation for improved channel balance interpolation. Section IV reviews related work in areas such as pathfinding algorithms and multipath payment protocols for the Lightning Network. Sections V,VI, and VII formally define the problem statement, the data collection and preprocessing steps, and the machine learning models and features evaluated. Section VIII outlines the methodology and metrics. Sections IX and X present and analyze the performance of the various models. Finally, Section XI explores potential future work, such as implementing an enhanced pathfinding algorit","cbCaipDngqRdXHv0","https://ap.wps.com/l/cbCaipDngqRdXHv0","pdf",473982,1,7,"English","en",105,"# Introduction\n## Background: Bitcoin\n## Background: Lightning Network\n# Problem and Motivation\n## Data and Preprocessing\n## Machine Learning Models and Features\n# Methodology and Metrics\n## Performance Analysis\n## Future Work","[{\"question\":\"What problem does the paper address in the Lightning Network?\",\"answer\":\"It investigates whether channel balances can be interpolated accurately using machine learning, so pathfinding algorithms can select viable payment routes without heavy trial-and-error.\"},{\"question\":\"What baselines and models are compared?\",\"answer\":\"The study evaluates six machine learning models against two heuristic baselines, including an equal-split assumption where each edge is assigned half the channel capacity.\"},{\"question\":\"Which features does the study examine for predicting channel balances?\",\"answer\":\"It tests whether predictions can rely on node features alone, channel features alone, or combinations of both, and considers whether additional topological information can enhance predictive power.\"}]","Channel Balance Interpolation in the Lightning Network via Machine Learning | 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