{"id":"https://openalex.org/W7167956907","doi":"https://doi.org/10.48550/arxiv.2607.08703","title":"MPFlow: Learning Budgeted Max-Flow Optimization on the Lightning Network with Deep Graph Reinforcement Learning","display_name":"MPFlow: Learning Budgeted Max-Flow Optimization on the Lightning Network with Deep Graph Reinforcement Learning","publication_year":2026,"publication_date":"2026-07-09","ids":{"openalex":"https://openalex.org/W7167956907","doi":"https://doi.org/10.48550/arxiv.2607.08703"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2607.08703","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2607.08703","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2607.08703","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5114439128","display_name":"Harrison Rush","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Rush, Harrison","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5053105471","display_name":"Vincent Davis","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Davis, Vincent","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5051404372","display_name":"Simone Antonelli","orcid":"https://orcid.org/0000-0002-5230-9689"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Antonelli, Simone","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5140401950","display_name":"Vikash Singh","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Singh, Vikash","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5114439129","display_name":"Jesse Shrader","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Shrader, Jesse","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5140436085","display_name":"Emanuele Rossi","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Rossi, Emanuele","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10270","display_name":"Blockchain Technology Applications and Security","score":0.3361000120639801,"subfield":{"id":"https://openalex.org/subfields/1710","display_name":"Information Systems"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10270","display_name":"Blockchain Technology Applications and Security","score":0.3361000120639801,"subfield":{"id":"https://openalex.org/subfields/1710","display_name":"Information Systems"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10714","display_name":"Software-Defined Networks and 5G","score":0.1527000069618225,"subfield":{"id":"https://openalex.org/subfields/1705","display_name":"Computer Networks and Communications"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10273","display_name":"IoT and Edge/Fog Computing","score":0.09380000084638596,"subfield":{"id":"https://openalex.org/subfields/1705","display_name":"Computer Networks and Communications"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/reinforcement-learning","display_name":"Reinforcement learning","score":0.7181000113487244},{"id":"https://openalex.org/keywords/routing","display_name":"Routing (electronic design automation)","score":0.4893999993801117},{"id":"https://openalex.org/keywords/heuristic","display_name":"Heuristic","score":0.47940000891685486},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.44279998540878296},{"id":"https://openalex.org/keywords/margin","display_name":"Margin (machine learning)","score":0.43160000443458557},{"id":"https://openalex.org/keywords/network-topology","display_name":"Network topology","score":0.3619999885559082},{"id":"https://openalex.org/keywords/lightning","display_name":"Lightning (connector)","score":0.3499999940395355},{"id":"https://openalex.org/keywords/node","display_name":"Node (physics)","score":0.34380000829696655}],"concepts":[{"id":"https://openalex.org/C97541855","wikidata":"https://www.wikidata.org/wiki/Q830687","display_name":"Reinforcement learning","level":2,"score":0.7181000113487244},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.646399974822998},{"id":"https://openalex.org/C74172769","wikidata":"https://www.wikidata.org/wiki/Q1446839","display_name":"Routing (electronic design automation)","level":2,"score":0.4893999993801117},{"id":"https://openalex.org/C173801870","wikidata":"https://www.wikidata.org/wiki/Q201413","display_name":"Heuristic","level":2,"score":0.47940000891685486},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.4449999928474426},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.44279998540878296},{"id":"https://openalex.org/C774472","wikidata":"https://www.wikidata.org/wiki/Q6760393","display_name":"Margin (machine learning)","level":2,"score":0.43160000443458557},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.40619999170303345},{"id":"https://openalex.org/C199845137","wikidata":"https://www.wikidata.org/wiki/Q145490","display_name":"Network topology","level":2,"score":0.3619999885559082},{"id":"https://openalex.org/C69398868","wikidata":"https://www.wikidata.org/wiki/Q129052","display_name":"Lightning (connector)","level":3,"score":0.3499999940395355},{"id":"https://openalex.org/C62611344","wikidata":"https://www.wikidata.org/wiki/Q1062658","display_name":"Node (physics)","level":2,"score":0.34380000829696655},{"id":"https://openalex.org/C197115733","wikidata":"https://www.wikidata.org/wiki/Q1003136","display_name":"Forcing (mathematics)","level":2,"score":0.3156999945640564},{"id":"https://openalex.org/C162307627","wikidata":"https://www.wikidata.org/wiki/Q204833","display_name":"Enhanced Data Rates for GSM Evolution","level":2,"score":0.3125999867916107},{"id":"https://openalex.org/C56086750","wikidata":"https://www.wikidata.org/wiki/Q6042592","display_name":"Integer programming","level":2,"score":0.3000999987125397},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.2759999930858612},{"id":"https://openalex.org/C2780009758","wikidata":"https://www.wikidata.org/wiki/Q6804172","display_name":"Measure (data warehouse)","level":2,"score":0.2718999981880188},{"id":"https://openalex.org/C188116033","wikidata":"https://www.wikidata.org/wiki/Q2664563","display_name":"Q-learning","level":3,"score":0.2662999927997589},{"id":"https://openalex.org/C42475967","wikidata":"https://www.wikidata.org/wiki/Q194292","display_name":"Operations research","level":1,"score":0.2605000138282776},{"id":"https://openalex.org/C2775924081","wikidata":"https://www.wikidata.org/wiki/Q55608371","display_name":"Control (management)","level":2,"score":0.25999999046325684},{"id":"https://openalex.org/C189950617","wikidata":"https://www.wikidata.org/wiki/Q937228","display_name":"Property (philosophy)","level":2,"score":0.2596000134944916},{"id":"https://openalex.org/C146380142","wikidata":"https://www.wikidata.org/wiki/Q1137726","display_name":"Directed graph","level":2,"score":0.2515000104904175}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2607.08703","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2607.08703","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2607.08703","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2607.08703","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/10","score":0.42279893159866333,"display_name":"Reduced inequalities"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"We":[0,25],"address":[1],"liquidity":[2],"placement":[3,95],"in":[4,130],"the":[5,80,90,116],"Bitcoin":[6],"Lightning":[7,105],"Network":[8,106],"(LN):":[9],"given":[10],"a":[11,17,29,44,61,77],"fixed":[12],"budget,":[13],"which":[14],"channels":[15],"should":[16],"node":[18],"open":[19],"to":[20,92],"maximize":[21,41],"its":[22],"routing":[23,48],"capacity?":[24],"cast":[26],"this":[27],"as":[28],"budget-constrained":[30],"combinatorial":[31],"optimization":[32,68],"problem":[33],"on":[34,103,115],"graphs,":[35],"selecting":[36],"$k$":[37],"edge":[38],"additions":[39],"that":[40,139],"$s$--$t$":[42],"max-flow,":[43],"theory-grounded":[45],"measure":[46],"of":[47],"capacity,":[49],"and":[50,70,73,122],"solve":[51],"it":[52],"with":[53,65],"graph":[54],"reinforcement":[55],"learning.":[56],"Our":[57],"lightweight":[58],"agent":[59,126],"combines":[60],"message-passing":[62],"policy":[63,67,91],"network":[64],"proximal":[66],"(PPO)":[69],"action":[71],"masking,":[72],"is":[74],"trained":[75],"under":[76],"hub-exclusion":[78],"curriculum:":[79],"network's":[81],"top":[82],"hubs":[83],"are":[84],"removed":[85],"from":[86],"training":[87],"subgraphs,":[88],"forcing":[89],"learn":[93],"capacity-aware":[94],"rather":[96],"than":[97],"hub":[98],"attachment.":[99],"In":[100],"extensive":[101],"experiments":[102],"real":[104],"snapshots,":[107],"our":[108],"method":[109],"consistently":[110],"outperforms":[111],"strong":[112],"heuristic":[113],"baselines":[114],"max-flow":[117],"objective":[118],"across":[119,147],"multiple":[120],"seeds":[121],"unseen":[123],"graphs.":[124],"The":[125],"has":[127],"been":[128],"deployed":[129],"production":[131],"for":[132],"peer":[133],"recommendations,":[134],"executing":[135],"4640":[136],"channel-open":[137],"decisions":[138],"cumulatively":[140],"allocate":[141],"267.3":[142],"BTC":[143],"over":[144],"$16":[145],"million":[146],"30":[148],"managed":[149],"nodes.":[150]},"counts_by_year":[],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2026-07-11T00:00:00"}