{"id":"https://openalex.org/W4395447445","doi":"https://doi.org/10.1109/tkde.2024.3389783","title":"Graphusion: Latent Diffusion for Graph Generation","display_name":"Graphusion: Latent Diffusion for Graph Generation","publication_year":2024,"publication_date":"2024-04-25","ids":{"openalex":"https://openalex.org/W4395447445","doi":"https://doi.org/10.1109/tkde.2024.3389783"},"language":"en","primary_location":{"id":"doi:10.1109/tkde.2024.3389783","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tkde.2024.3389783","pdf_url":null,"source":{"id":"https://openalex.org/S30698027","display_name":"IEEE Transactions on Knowledge and Data Engineering","issn_l":"1041-4347","issn":["1041-4347","1558-2191","2326-3865"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320439","host_organization_name":"IEEE Computer Society","host_organization_lineage":["https://openalex.org/P4310320439","https://openalex.org/P4310319808"],"host_organization_lineage_names":["IEEE Computer Society","Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Knowledge and Data Engineering","raw_type":"journal-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5022659405","display_name":"L. Yang","orcid":"https://orcid.org/0000-0003-1905-8053"},"institutions":[{"id":"https://openalex.org/I20231570","display_name":"Peking University","ror":"https://ror.org/02v51f717","country_code":"CN","type":"education","lineage":["https://openalex.org/I20231570"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Ling Yang","raw_affiliation_strings":["Institute of Medical Technology, Peking University Health Science Center, Beijing, China","National Institute of Health Data Science, Peking University, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0003-1905-8053","affiliations":[{"raw_affiliation_string":"Institute of Medical Technology, Peking University Health Science Center, Beijing, China","institution_ids":["https://openalex.org/I20231570"]},{"raw_affiliation_string":"National Institute of Health Data Science, Peking University, Beijing, China","institution_ids":["https://openalex.org/I20231570"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101113352","display_name":"Zhilin Huang","orcid":"https://orcid.org/0000-0003-3417-743X"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhilin Huang","raw_affiliation_strings":["Tsinghua University, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0003-3417-743X","affiliations":[{"raw_affiliation_string":"Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101764890","display_name":"Zhilong Zhang","orcid":"https://orcid.org/0009-0009-9307-8440"},"institutions":[{"id":"https://openalex.org/I20231570","display_name":"Peking University","ror":"https://ror.org/02v51f717","country_code":"CN","type":"education","lineage":["https://openalex.org/I20231570"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhilong Zhang","raw_affiliation_strings":["Institute of Medical Technology, Peking University Health Science Center, Beijing, China","National Institute of Health Data Science, Peking University, Beijing, China"],"raw_orcid":"https://orcid.org/0009-0009-9307-8440","affiliations":[{"raw_affiliation_string":"Institute of Medical Technology, Peking University Health Science Center, Beijing, China","institution_ids":["https://openalex.org/I20231570"]},{"raw_affiliation_string":"National Institute of Health Data Science, Peking University, Beijing, China","institution_ids":["https://openalex.org/I20231570"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5023862460","display_name":"Zhongyi Liu","orcid":"https://orcid.org/0000-0001-9478-8107"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhongyi Liu","raw_affiliation_strings":["Ant Group, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0001-9478-8107","affiliations":[{"raw_affiliation_string":"Ant Group, Beijing, China","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5080648149","display_name":"Shenda Hong","orcid":"https://orcid.org/0000-0001-7521-5127"},"institutions":[{"id":"https://openalex.org/I20231570","display_name":"Peking University","ror":"https://ror.org/02v51f717","country_code":"CN","type":"education","lineage":["https://openalex.org/I20231570"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Shenda Hong","raw_affiliation_strings":["Institute of Medical Technology, Peking University Health Science Center, Beijing, China","National Institute of Health Data Science, Peking University, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0001-7521-5127","affiliations":[{"raw_affiliation_string":"Institute of Medical Technology, Peking University Health Science Center, Beijing, China","institution_ids":["https://openalex.org/I20231570"]},{"raw_affiliation_string":"National Institute of Health Data Science, Peking University, Beijing, China","institution_ids":["https://openalex.org/I20231570"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5008772211","display_name":"Wentao Zhang","orcid":"https://orcid.org/0000-0002-7532-5550"},"institutions":[{"id":"https://openalex.org/I20231570","display_name":"Peking University","ror":"https://ror.org/02v51f717","country_code":"CN","type":"education","lineage":["https://openalex.org/I20231570"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wentao Zhang","raw_affiliation_strings":["Peking University, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0002-7532-5550","affiliations":[{"raw_affiliation_string":"Peking University, Beijing, China","institution_ids":["https://openalex.org/I20231570"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5026184280","display_name":"Wenming Yang","orcid":"https://orcid.org/0000-0002-2506-1286"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wenming Yang","raw_affiliation_strings":["Tsinghua University, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0002-2506-1286","affiliations":[{"raw_affiliation_string":"Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5062357883","display_name":"Bin Cui","orcid":"https://orcid.org/0000-0003-1681-4677"},"institutions":[{"id":"https://openalex.org/I20231570","display_name":"Peking University","ror":"https://ror.org/02v51f717","country_code":"CN","type":"education","lineage":["https://openalex.org/I20231570"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Bin Cui","raw_affiliation_strings":["Peking University, Beijing, China","School of CS & Key Lab of High Confidence Software Technologies (MOE), Institute of Computational Social Science, Peking University, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0003-1681-4677","affiliations":[{"raw_affiliation_string":"Peking University, Beijing, China","institution_ids":["https://openalex.org/I20231570"]},{"raw_affiliation_string":"School of CS & Key Lab of High Confidence Software Technologies (MOE), Institute of Computational Social Science, Peking University, Beijing, China","institution_ids":["https://openalex.org/I20231570"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5063338849","display_name":"Luxia Zhang","orcid":"https://orcid.org/0000-0003-2349-2936"},"institutions":[{"id":"https://openalex.org/I20231570","display_name":"Peking University","ror":"https://ror.org/02v51f717","country_code":"CN","type":"education","lineage":["https://openalex.org/I20231570"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Luxia Zhang","raw_affiliation_strings":["Institute of Medical Technology, Peking University Health Science Center, Beijing, China","National Institute of Health Data Science, Peking University, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0003-2349-2936","affiliations":[{"raw_affiliation_string":"Institute of Medical Technology, Peking University Health Science Center, Beijing, China","institution_ids":["https://openalex.org/I20231570"]},{"raw_affiliation_string":"National Institute of Health Data Science, Peking University, Beijing, China","institution_ids":["https://openalex.org/I20231570"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":4.4455,"has_fulltext":false,"cited_by_count":16,"citation_normalized_percentile":{"value":0.95015551,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":96,"max":99},"biblio":{"volume":"36","issue":"11","first_page":"6358","last_page":"6369"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10028","display_name":"Topic Modeling","score":0.9998000264167786,"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/T10028","display_name":"Topic Modeling","score":0.9998000264167786,"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/T11273","display_name":"Advanced Graph Neural Networks","score":0.9998000264167786,"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/T10181","display_name":"Natural Language Processing Techniques","score":0.9976999759674072,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7323064208030701},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.45101314783096313},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.325771689414978}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7323064208030701},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.45101314783096313},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.325771689414978}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tkde.2024.3389783","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tkde.2024.3389783","pdf_url":null,"source":{"id":"https://openalex.org/S30698027","display_name":"IEEE Transactions on Knowledge and Data Engineering","issn_l":"1041-4347","issn":["1041-4347","1558-2191","2326-3865"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320439","host_organization_name":"IEEE Computer Society","host_organization_lineage":["https://openalex.org/P4310320439","https://openalex.org/P4310319808"],"host_organization_lineage_names":["IEEE Computer Society","Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Knowledge and Data Engineering","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G4431910321","display_name":null,"funder_award_id":"72125009","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":80,"referenced_works":["https://openalex.org/W1959608418","https://openalex.org/W2027482274","https://openalex.org/W2080635178","https://openalex.org/W2144000913","https://openalex.org/W2153959628","https://openalex.org/W2187089797","https://openalex.org/W2765811365","https://openalex.org/W2786103815","https://openalex.org/W2887447356","https://openalex.org/W2907492528","https://openalex.org/W2994860160","https://openalex.org/W2998755720","https://openalex.org/W3034637015","https://openalex.org/W3036527662","https://openalex.org/W3099414221","https://openalex.org/W3152893301","https://openalex.org/W3167139504","https://openalex.org/W3216759837","https://openalex.org/W4224035735","https://openalex.org/W4226254615","https://openalex.org/W4245798716","https://openalex.org/W4289436753","https://openalex.org/W4312198153","https://openalex.org/W4312933868","https://openalex.org/W4322614756","https://openalex.org/W4367859989","https://openalex.org/W4372283128","https://openalex.org/W4387195417","https://openalex.org/W6640963894","https://openalex.org/W6679045638","https://openalex.org/W6680132778","https://openalex.org/W6730084236","https://openalex.org/W6738543193","https://openalex.org/W6738964360","https://openalex.org/W6747927160","https://openalex.org/W6748556633","https://openalex.org/W6752306858","https://openalex.org/W6762720510","https://openalex.org/W6765775151","https://openalex.org/W6771848067","https://openalex.org/W6774689120","https://openalex.org/W6779823529","https://openalex.org/W6780593937","https://openalex.org/W6783713337","https://openalex.org/W6786375611","https://openalex.org/W6788624270","https://openalex.org/W6788867622","https://openalex.org/W6789743973","https://openalex.org/W6790369153","https://openalex.org/W6791026688","https://openalex.org/W6795288823","https://openalex.org/W6795986329","https://openalex.org/W6796588791","https://openalex.org/W6797906067","https://openalex.org/W6801817941","https://openalex.org/W6801860107","https://openalex.org/W6802810016","https://openalex.org/W6802841199","https://openalex.org/W6809609761","https://openalex.org/W6809885388","https://openalex.org/W6809904528","https://openalex.org/W6809966130","https://openalex.org/W6810026998","https://openalex.org/W6810093873","https://openalex.org/W6810511157","https://openalex.org/W6810576860","https://openalex.org/W6810802740","https://openalex.org/W6810940779","https://openalex.org/W6810983538","https://openalex.org/W6811424244","https://openalex.org/W6838815585","https://openalex.org/W6838915749","https://openalex.org/W6840815571","https://openalex.org/W6842762852","https://openalex.org/W6843464061","https://openalex.org/W6846015844","https://openalex.org/W6847388479","https://openalex.org/W6852093416","https://openalex.org/W6852583479","https://openalex.org/W7047410558"],"related_works":["https://openalex.org/W4391375266","https://openalex.org/W2899084033","https://openalex.org/W2748952813","https://openalex.org/W2390279801","https://openalex.org/W4391913857","https://openalex.org/W2358668433","https://openalex.org/W4396701345","https://openalex.org/W2376932109","https://openalex.org/W2001405890","https://openalex.org/W4396696052"],"abstract_inverted_index":{"Graph":[0],"generation":[1,121,158],"is":[2,89],"a":[3,75,92,114],"fundamental":[4],"task":[5],"in":[6,35,48,60],"machine":[7],"learning":[8],"with":[9,99,173],"broad":[10],"impacts":[11],"on":[12,160],"numerous":[13],"real-world":[14],"applications":[15],"such":[16],"as":[17],"biomedical":[18],"discovery":[19],"and":[20,64,77,108,117,163,170],"social":[21],"science.":[22],"Most":[23],"recently,":[24],"generative":[25,81],"models,":[26],"especially":[27],"diffusion":[28,45],"models":[29],"(DMs),":[30],"have":[31],"shown":[32],"great":[33],"promise":[34],"synthesizing":[36],"realistic":[37],"graphs.":[38],"However,":[39],"existing":[40],"DMs":[41,110],"methods":[42],"typically":[43],"conduct":[44],"processes":[46],"directly":[47],"complex":[49],"graph":[50,80,94,120,145,157,165],"space":[51,102,127],"(i.e.,":[52],"node":[53],"feature,":[54],"adjacency":[55],"matrix,":[56],"or":[57],"both),":[58],"resulting":[59],"high":[61],"modeling":[62],"complexity":[63],"poor":[65],"multimodal":[66],"distribution":[67,141],"coverage.":[68],"In":[69],"this":[70],"paper,":[71],"we":[72,129],"propose":[73],"Graphusion,":[74],"novel":[76],"unified":[78],"latent-based":[79],"framework":[82,152],"to":[83,103,124,135],"address":[84],"the":[85,125,138,143,168],"problems.":[86],"Specifically,":[87],"Graphusion":[88,151],"composed":[90],"of":[91,142],"variational":[93],"autoencoder":[95],"mapping":[96],"raw":[97],"graphs":[98],"high-dimensional":[100],"discrete":[101],"low-dimensional":[104],"topology-injected":[105],"latent":[106,109,133],"space,":[107],"running":[111],"there,":[112],"producing":[113],"smoother,":[115],"faster,":[116],"more":[118],"expressive":[119],"procedure.":[122],"Thanks":[123],"latest":[126],"modeling,":[128],"further":[130,174],"develop":[131],"principled":[132],"self-guidance":[134],"sufficiently":[136],"cover":[137],"whole":[139],"semantical":[140],"unlabeled":[144],"set.":[146],"Experiments":[147],"show":[148],"that":[149],"our":[150],"can":[153],"consistently":[154],"outperform":[155],"previous":[156],"baselines":[159],"both":[161],"generic":[162],"molecular":[164],"datasets,":[166],"demonstrating":[167],"generality":[169],"extensibility":[171],"along":[172],"analytical":[175],"justifications.":[176]},"counts_by_year":[{"year":2026,"cited_by_count":4},{"year":2025,"cited_by_count":9},{"year":2024,"cited_by_count":3}],"updated_date":"2026-03-27T05:58:40.876381","created_date":"2025-10-10T00:00:00"}
