{"id":"https://openalex.org/W7214916062","doi":"https://doi.org/10.48550/arxiv.2609.37957","title":"Multiscale Reconstruction of Weighted Networks from Coarse-Grained Data","display_name":"Multiscale Reconstruction of Weighted Networks from Coarse-Grained Data","publication_year":2026,"publication_date":"2026-09-29","ids":{"openalex":"https://openalex.org/W7214916062","doi":"https://doi.org/10.48550/arxiv.2609.37957"},"language":"en","primary_location":{"id":"pmh:oai:arXiv.org:2609.37957","doi":null,"is_oa":true,"landing_page_url":"https://arxiv.org/abs/2609.37957","pdf_url":"https://arxiv.org/pdf/2609.37957","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,"listed_in":[],"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":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"volume":null,"type":"preprint","is_traditional":true,"indexed_in":["arxiv","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2609.37957","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5123242942","display_name":"Mattia Marzi","orcid":"https://orcid.org/0000-0003-4004-3390","observed_orcids":["https://orcid.org/0000-0003-4004-3390"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Marzi, Mattia","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5079323137","display_name":"F. P. Pijpers","orcid":"https://orcid.org/0000-0001-7572-9435","observed_orcids":["https://orcid.org/0000-0001-7572-9435","https://orcid.org/0000-0001-6772-8416"]},"institutions":[{"id":"https://openalex.org/I1322731696","display_name":"Centraal Bureau voor de Statistiek","ror":"https://ror.org/0408v4c28","country_code":"NL","type":"other","lineage":["https://openalex.org/I1322731696"]},{"id":"https://openalex.org/I887064364","display_name":"University of Amsterdam","ror":"https://ror.org/04dkp9463","country_code":"NL","type":"education","lineage":["https://openalex.org/I887064364"]}],"countries":["NL"],"is_corresponding":false,"raw_author_name":"Pijpers, Frank P.","raw_affiliation_strings":["Korteweg -de Vries Institute for Mathematics , University of Amsterdam , Amsterdam ( the Netherlands","Statistics Netherlands , Henri Faasdreef 312 , 2492 JP Den Haag ( the Netherlands)"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Korteweg -de Vries Institute for Mathematics , University of Amsterdam , Amsterdam ( the Netherlands","institution_ids":["https://openalex.org/I887064364"]},{"raw_affiliation_string":"Statistics Netherlands , Henri Faasdreef 312 , 2492 JP Den Haag ( the Netherlands)","institution_ids":["https://openalex.org/I1322731696"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5041273626","display_name":"Diego Garlaschelli","orcid":"https://orcid.org/0000-0001-6035-1783","observed_orcids":["https://orcid.org/0000-0001-6035-1783"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Garlaschelli, Diego","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":true,"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/T10064","display_name":"Complex Network Analysis Techniques","score":0.9984999895095825,"subfield":{"id":"https://openalex.org/subfields/3109","display_name":"Statistical and Nonlinear Physics"},"field":{"id":"https://openalex.org/fields/31","display_name":"Physics and Astronomy"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10064","display_name":"Complex Network Analysis Techniques","score":0.9984999895095825,"subfield":{"id":"https://openalex.org/subfields/3109","display_name":"Statistical and Nonlinear Physics"},"field":{"id":"https://openalex.org/fields/31","display_name":"Physics and Astronomy"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11270","display_name":"Complex Systems and Time Series Analysis","score":0.0008999999845400453,"subfield":{"id":"https://openalex.org/subfields/2002","display_name":"Economics and Econometrics"},"field":{"id":"https://openalex.org/fields/20","display_name":"Economics, Econometrics and Finance"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T10887","display_name":"Bioinformatics and Genomic Networks","score":0.0,"subfield":{"id":"https://openalex.org/subfields/1312","display_name":"Molecular Biology"},"field":{"id":"https://openalex.org/fields/13","display_name":"Biochemistry, Genetics and Molecular Biology"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/network-inference","display_name":"network inference","score":0.828000009059906},{"id":"https://openalex.org/keywords/weighted-networks","display_name":"weighted networks","score":0.9760000109672546},{"id":"https://openalex.org/keywords/multi-scale-network","display_name":"multi-scale network","score":0.7239999771118164},{"id":"https://openalex.org/keywords/coarse-graining","display_name":"coarse-graining","score":0.9639999866485596},{"id":"https://openalex.org/keywords/probabilistic-inference","display_name":"probabilistic inference","score":0.6449999809265137},{"id":"https://openalex.org/keywords/international-trade-networks","display_name":"international trade networks","score":0.6819999814033508},{"id":"https://openalex.org/keywords/production-networks","display_name":"production networks","score":0.6570000052452087},{"id":"https://openalex.org/keywords/hierarchical-classification","display_name":"hierarchical classification","score":0.5690000057220459},{"id":"https://openalex.org/keywords/macroregions","display_name":"macroregions","score":0.6399999856948853}],"concepts":[{"id":"https://openalex.org/C4679612","wikidata":"https://www.wikidata.org/wiki/Q866298","display_name":"Aggregate (composite)","level":2,"score":0.6850000023841858},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6610000133514404},{"id":"https://openalex.org/C49937458","wikidata":"https://www.wikidata.org/wiki/Q2599292","display_name":"Probabilistic logic","level":2,"score":0.6172000169754028},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.6108999848365784},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.5936999917030334},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.5378999710083008},{"id":"https://openalex.org/C2778755073","wikidata":"https://www.wikidata.org/wiki/Q10858537","display_name":"Scale (ratio)","level":2,"score":0.5164999961853027},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.4684999883174896},{"id":"https://openalex.org/C48372109","wikidata":"https://www.wikidata.org/wiki/Q3913","display_name":"Binary number","level":2,"score":0.45509999990463257},{"id":"https://openalex.org/C2779227376","wikidata":"https://www.wikidata.org/wiki/Q6505497","display_name":"Layer (electronics)","level":2,"score":0.39890000224113464},{"id":"https://openalex.org/C32848918","wikidata":"https://www.wikidata.org/wiki/Q845789","display_name":"Observable","level":2,"score":0.35179999470710754},{"id":"https://openalex.org/C141379421","wikidata":"https://www.wikidata.org/wiki/Q6094427","display_name":"Iterative reconstruction","level":2,"score":0.33239999413490295},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.2955000102519989},{"id":"https://openalex.org/C159620131","wikidata":"https://www.wikidata.org/wiki/Q1938983","display_name":"Spatial analysis","level":2,"score":0.28839999437332153},{"id":"https://openalex.org/C104122410","wikidata":"https://www.wikidata.org/wiki/Q1416406","display_name":"Network model","level":2,"score":0.2809000015258789},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.27900001406669617},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.2782999873161316},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.2721000015735626},{"id":"https://openalex.org/C147764199","wikidata":"https://www.wikidata.org/wiki/Q6865248","display_name":"Minification","level":2,"score":0.25440001487731934},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.25220000743865967},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.2515000104904175}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:oai:arXiv.org:2609.37957","doi":null,"is_oa":true,"landing_page_url":"https://arxiv.org/abs/2609.37957","pdf_url":"https://arxiv.org/pdf/2609.37957","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,"listed_in":[],"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":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},{"id":"doi:10.48550/arxiv.2609.37957","doi":"https://doi.org/10.48550/arxiv.2609.37957","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2609.37957","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,"listed_in":[],"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":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2609.37957","doi":null,"is_oa":true,"landing_page_url":"https://arxiv.org/abs/2609.37957","pdf_url":"https://arxiv.org/pdf/2609.37957","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,"listed_in":[],"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":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"sustainable_development_goals":[],"study_designs":[],"awards":[{"id":"https://openalex.org/G7062377519","display_name":"Redefining renormalization for complex networks","funder_award_id":"OCENW.M.24.039","funder_id":"https://openalex.org/F4320321800","funder_display_name":"Nederlandse Organisatie voor Wetenschappelijk Onderzoek"},{"id":"https://openalex.org/G8239494964","display_name":"Network renormalization: from theoretical physics to the resilience of societies","funder_award_id":"NWA.1418.24.029","funder_id":"https://openalex.org/F4320321800","funder_display_name":"Nederlandse Organisatie voor Wetenschappelijk Onderzoek"}],"funders":[{"id":"https://openalex.org/F4320321800","display_name":"Nederlandse Organisatie voor Wetenschappelijk Onderzoek","ror":"https://ror.org/04jsz6e67"}],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W7214916062.pdf","grobid_xml":"https://content.openalex.org/works/W7214916062.grobid-xml"},"referenced_works_count":3,"referenced_works":["https://openalex.org/W1824928965","https://openalex.org/W3143563038","https://openalex.org/W4405715767"],"related_works":["https://openalex.org/W7215125712","https://openalex.org/W4405715767","https://openalex.org/W3160203949","https://openalex.org/W2754868470","https://openalex.org/W159033365","https://openalex.org/W4417005251","https://openalex.org/W7014803724","https://openalex.org/W3006073215","https://openalex.org/W4408668209","https://openalex.org/W2054297533"],"abstract_inverted_index":{"Network":[0],"reconstruction":[1,163,169],"from":[2],"partial":[3],"information":[4,24,181],"is":[5,25,67,122,191],"usually":[6],"performed":[7],"at":[8,13,32,173,183,188],"the":[9,28,39,96,107,118,126,131,140,174,184,201],"same":[10,185],"resolution":[11],"level":[12],"which":[14,189],"constraints":[15],"are":[16,112,137],"observable.":[17],"This":[18],"becomes":[19],"problematic":[20],"when":[21],"only":[22],"coarse-grained":[23,120],"available,":[26],"while":[27,216],"relevant":[29],"process":[30],"occurs":[31],"a":[33,47,54,166],"finer":[34,90,150],"scale.":[35],"Here":[36],"we":[37,158],"employ":[38],"multiscale":[40,162],"model":[41,66,170],"of":[42],"weighted":[43,59,168],"networks":[44,60],"introduced":[45],"in":[46,220],"companion":[48],"paper":[49],"and":[50,117,155,177,212],"turn":[51],"it":[52],"into":[53,114],"probabilistic":[55],"framework":[56],"for":[57],"reconstructing":[58],"across":[61,139],"arbitrary":[62],"aggregation":[63,104],"levels.":[64],"The":[65],"built":[68],"to":[69,89,124,148],"preserve":[70],"its":[71],"functional":[72],"form":[73],"under":[74],"coarse-graining,":[75],"so":[76],"that":[77],"global":[78],"parameters":[79],"calibrated":[80,171],"on":[81,98],"an":[82],"observable":[83],"aggregate":[84],"layer":[85,121],"can":[86],"be":[87],"transferred":[88],"layers":[91,147],"without":[92],"refitting.":[93],"We":[94],"test":[95],"method":[97,164,199],"two":[99],"empirical":[100],"systems":[101],"with":[102,205],"different":[103],"mechanisms.":[105],"In":[106,130,152],"International":[108],"Trade":[109],"Network,":[110],"countries":[111],"aggregated":[113],"geographic":[115],"macro-regions":[116],"observed":[119],"used":[123],"infer":[125,149],"underlying":[127],"country-level":[128],"network.":[129],"Dutch":[132],"production":[133],"network,":[134],"sectoral":[135,146,156],"flows":[136],"reconstructed":[138],"hierarchical":[141],"industrial":[142],"classification,":[143],"using":[144,179],"coarser":[145],"ones.":[151],"both":[153],"geographical":[154],"settings,":[157],"benchmark":[159],"our":[160,198],"genuinely":[161],"against":[165],"state-of-the-art":[167],"directly":[172],"target":[175],"resolution,":[176],"therefore":[178],"additional":[180],"available":[182],"(finer)":[186],"scale":[187],"performance":[190],"evaluated.":[192],"Remarkably,":[193],"despite":[194],"this":[195],"informational":[196],"disadvantage,":[197],"recovers":[200],"fine-scale":[202],"binary":[203],"structure":[204],"high":[206],"accuracy,":[207],"improving":[208],"precision,":[209],"specificity,":[210],"accuracy":[211],"maximum":[213],"degree-error":[214],"diagnostics,":[215],"remaining":[217],"nearly":[218],"equivalent":[219],"sensitivity.":[221]},"counts_by_year":[],"updated_date":"2026-11-07T11:25:07.637180","created_date":"2026-10-01T00:00:00"}