{"id":"https://openalex.org/W7162441105","doi":"https://doi.org/10.48550/arxiv.2605.24064","title":"Generative Representation Learning on Hyper-relational Knowledge Graphs via Masked Discrete Diffusion","display_name":"Generative Representation Learning on Hyper-relational Knowledge Graphs via Masked Discrete Diffusion","publication_year":2026,"publication_date":"2026-05-22","ids":{"openalex":"https://openalex.org/W7162441105","doi":"https://doi.org/10.48550/arxiv.2605.24064"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2605.24064","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.24064","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"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":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"article"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2605.24064","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5100719136","display_name":"Jaejun Lee","orcid":"https://orcid.org/0000-0002-6948-6462"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Lee, Jaejun","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5017543910","display_name":"\uae40\uc138\ud5cc","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Kim, Seheon","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5090741314","display_name":"Joyce Jiyoung Whang","orcid":"https://orcid.org/0000-0002-4773-3194"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Whang, Joyce Jiyoung","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":3,"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/T11273","display_name":"Advanced Graph Neural Networks","score":0.9919999837875366,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"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/T11273","display_name":"Advanced Graph Neural Networks","score":0.9919999837875366,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"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/T10887","display_name":"Bioinformatics and Genomic Networks","score":0.001500000013038516,"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"}},{"id":"https://openalex.org/T10064","display_name":"Complex Network Analysis Techniques","score":0.0013000000035390258,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.6481000185012817},{"id":"https://openalex.org/keywords/link","display_name":"Link (geometry)","score":0.6144000291824341},{"id":"https://openalex.org/keywords/generative-grammar","display_name":"Generative grammar","score":0.5906000137329102},{"id":"https://openalex.org/keywords/simple","display_name":"Simple (philosophy)","score":0.5701000094413757},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.5613999962806702},{"id":"https://openalex.org/keywords/generative-model","display_name":"Generative model","score":0.5364000201225281},{"id":"https://openalex.org/keywords/bridge","display_name":"Bridge (graph theory)","score":0.4203999936580658},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.3596000075340271}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7107999920845032},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.6481000185012817},{"id":"https://openalex.org/C2778753846","wikidata":"https://www.wikidata.org/wiki/Q6554239","display_name":"Link (geometry)","level":2,"score":0.6144000291824341},{"id":"https://openalex.org/C39890363","wikidata":"https://www.wikidata.org/wiki/Q36108","display_name":"Generative grammar","level":2,"score":0.5906000137329102},{"id":"https://openalex.org/C2780586882","wikidata":"https://www.wikidata.org/wiki/Q7520643","display_name":"Simple (philosophy)","level":2,"score":0.5701000094413757},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.5613999962806702},{"id":"https://openalex.org/C167966045","wikidata":"https://www.wikidata.org/wiki/Q5532625","display_name":"Generative model","level":3,"score":0.5364000201225281},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.47679999470710754},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.46959999203681946},{"id":"https://openalex.org/C100776233","wikidata":"https://www.wikidata.org/wiki/Q2532492","display_name":"Bridge (graph theory)","level":2,"score":0.4203999936580658},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.37950000166893005},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.3596000075340271},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.35910001397132874},{"id":"https://openalex.org/C2778089247","wikidata":"https://www.wikidata.org/wiki/Q368951","display_name":"Blank","level":2,"score":0.35350000858306885},{"id":"https://openalex.org/C98763669","wikidata":"https://www.wikidata.org/wiki/Q176645","display_name":"Markov chain","level":2,"score":0.34880000352859497},{"id":"https://openalex.org/C1173588","wikidata":"https://www.wikidata.org/wiki/Q6554294","display_name":"Link analysis","level":2,"score":0.323199987411499},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.31290000677108765},{"id":"https://openalex.org/C168167062","wikidata":"https://www.wikidata.org/wiki/Q1117970","display_name":"Component (thermodynamics)","level":2,"score":0.3125999867916107},{"id":"https://openalex.org/C149441793","wikidata":"https://www.wikidata.org/wiki/Q200726","display_name":"Probability distribution","level":2,"score":0.3125},{"id":"https://openalex.org/C47822265","wikidata":"https://www.wikidata.org/wiki/Q854457","display_name":"Complex system","level":2,"score":0.2824999988079071},{"id":"https://openalex.org/C161301231","wikidata":"https://www.wikidata.org/wiki/Q3478658","display_name":"Knowledge representation and reasoning","level":2,"score":0.2662999927997589},{"id":"https://openalex.org/C9357733","wikidata":"https://www.wikidata.org/wiki/Q6878417","display_name":"Missing data","level":2,"score":0.26570001244544983},{"id":"https://openalex.org/C165064840","wikidata":"https://www.wikidata.org/wiki/Q1321061","display_name":"Matching (statistics)","level":2,"score":0.26429998874664307},{"id":"https://openalex.org/C2776235265","wikidata":"https://www.wikidata.org/wiki/Q18392052","display_name":"Fragment (logic)","level":2,"score":0.25769999623298645},{"id":"https://openalex.org/C159886148","wikidata":"https://www.wikidata.org/wiki/Q176645","display_name":"Markov process","level":2,"score":0.2529999911785126}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2605.24064","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.24064","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"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":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"article"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2605.24064","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.24064","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"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":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Hyper-relational":[0],"knowledge":[1,11],"graphs":[2],"(HKGs)":[3],"effectively":[4],"represent":[5],"complex":[6],"facts.":[7,191],"While":[8],"inferring":[9],"new":[10],"in":[12,54,57,186],"HKGs":[13,115,136],"is":[14],"a":[15,23,35,41,66,78,84,95,101,138,169],"critical":[16],"problem,":[17],"current":[18],"methods":[19],"cast":[20],"it":[21],"as":[22],"simple":[24],"link":[25,163,179],"prediction,":[26],"assuming":[27],"that":[28,116],"nearly":[29],"all":[30],"entities":[31],"and":[32,132,152,165,182,189],"relations":[33],"within":[34,168],"fact":[36,67,81,87,98,102,130,166],"are":[37],"known,":[38],"leaving":[39],"only":[40],"single":[42,170],"blank":[43],"to":[44,118],"be":[45,69],"filled.":[46],"However,":[47],"this":[48,74],"restricted":[49],"assumption":[50],"may":[51,68],"not":[52],"hold":[53],"real-world":[55],"scenarios":[56],"which":[58],"multiple,":[59],"or":[60,99],"even":[61],"all,":[62],"constituent":[63],"components":[64,125,131],"of":[65,123,135],"missing":[70,124],"simultaneously.":[71],"To":[72],"bridge":[73],"gap,":[75],"we":[76],"introduce":[77],"task":[79],"called":[80],"generation:":[82],"generating":[83,100,187],"valid":[85],"hyper-relational":[86],"from":[88,103],"an":[89],"arbitrarily":[90],"masked":[91,139],"query,":[92],"i.e.,":[93],"completing":[94],"partially":[96],"observed":[97],"scratch.":[104],"We":[105],"propose":[106],"KREPE,":[107],"the":[108,120,128,145],"first":[109],"generative":[110],"representation":[111],"learning":[112],"method":[113],"for":[114],"learns":[117],"model":[119],"probability":[121],"distributions":[122],"conditioned":[126],"on":[127,176],"local":[129],"global":[133],"structure":[134],"via":[137],"discrete":[140],"diffusion.":[141],"KREPE":[142,160],"models":[143],"both":[144],"intra-fact":[146],"dependencies":[147],"by":[148,155],"contextual":[149],"message":[150],"passing":[151],"inter-fact":[153],"correlations":[154],"aggregating":[156],"stochastically":[157],"sampled":[158],"contexts.":[159],"seamlessly":[161],"unifies":[162],"prediction":[164,180],"generation":[167],"training":[171],"framework,":[172],"achieving":[173],"state-of-the-art":[174],"performance":[175],"standard":[177],"HKG":[178],"benchmarks":[181],"outperforming":[183],"LLM-based":[184],"baselines":[185],"novel":[188],"correct":[190]},"counts_by_year":[],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2026-05-27T00:00:00"}