{"id":"https://openalex.org/W4414270462","doi":"https://doi.org/10.1109/access.2025.3611308","title":"Hypergraph Neural Networks Based on Enclosing Subgraph Extraction for Temporal Link Prediction","display_name":"Hypergraph Neural Networks Based on Enclosing Subgraph Extraction for Temporal Link Prediction","publication_year":2025,"publication_date":"2025-01-01","ids":{"openalex":"https://openalex.org/W4414270462","doi":"https://doi.org/10.1109/access.2025.3611308"},"language":"en","primary_location":{"id":"doi:10.1109/access.2025.3611308","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2025.3611308","pdf_url":null,"source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.1109/access.2025.3611308","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5101976139","display_name":"Ying Zhao","orcid":"https://orcid.org/0000-0001-6228-5433"},"institutions":[{"id":"https://openalex.org/I75390827","display_name":"Beijing University of Chemical Technology","ror":"https://ror.org/00df5yc52","country_code":"CN","type":"education","lineage":["https://openalex.org/I75390827"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Ying Zhao","raw_affiliation_strings":["College of Information Science and Technology, Beijing University of Chemical Technology, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"College of Information Science and Technology, Beijing University of Chemical Technology, Beijing, China","institution_ids":["https://openalex.org/I75390827"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Xiangxu Meng","orcid":"https://orcid.org/0009-0008-9557-4539"},"institutions":[{"id":"https://openalex.org/I75390827","display_name":"Beijing University of Chemical Technology","ror":"https://ror.org/00df5yc52","country_code":"CN","type":"education","lineage":["https://openalex.org/I75390827"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiangxu Meng","raw_affiliation_strings":["College of Information Science and Technology, Beijing University of Chemical Technology, Beijing, China"],"raw_orcid":"https://orcid.org/0009-0008-9557-4539","affiliations":[{"raw_affiliation_string":"College of Information Science and Technology, Beijing University of Chemical Technology, Beijing, China","institution_ids":["https://openalex.org/I75390827"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Liang Chen","orcid":"https://orcid.org/0009-0000-1775-363X"},"institutions":[{"id":"https://openalex.org/I75390827","display_name":"Beijing University of Chemical Technology","ror":"https://ror.org/00df5yc52","country_code":"CN","type":"education","lineage":["https://openalex.org/I75390827"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Liang Chen","raw_affiliation_strings":["College of Information Science and Technology, Beijing University of Chemical Technology, Beijing, China"],"raw_orcid":"https://orcid.org/0009-0000-1775-363X","affiliations":[{"raw_affiliation_string":"College of Information Science and Technology, Beijing University of Chemical Technology, Beijing, China","institution_ids":["https://openalex.org/I75390827"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5011153174","display_name":"Junjun Chen","orcid":"https://orcid.org/0009-0001-7186-3845"},"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":"Junjun Chen","raw_affiliation_strings":["Computer Center, Peking University, Beijing, China"],"raw_orcid":"https://orcid.org/0009-0001-7186-3845","affiliations":[{"raw_affiliation_string":"Computer Center, Peking University, Beijing, China","institution_ids":["https://openalex.org/I20231570"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5005494115","display_name":"Atul Sajjanhar","orcid":"https://orcid.org/0000-0002-0445-0573"},"institutions":[{"id":"https://openalex.org/I149704539","display_name":"Deakin University","ror":"https://ror.org/02czsnj07","country_code":"AU","type":"education","lineage":["https://openalex.org/I149704539"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Atul Sajjanhar","raw_affiliation_strings":["School of Information Technology, Deakin University, Geelong, Australia","School of Information Technology, Deakin University, Geelong, VIC, Australia"],"raw_orcid":"https://orcid.org/0000-0002-0445-0573","affiliations":[{"raw_affiliation_string":"School of Information Technology, Deakin University, Geelong, Australia","institution_ids":["https://openalex.org/I149704539"]},{"raw_affiliation_string":"School of Information Technology, Deakin University, Geelong, VIC, Australia","institution_ids":["https://openalex.org/I149704539"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":{"value":1850,"currency":"USD","value_usd":1850},"apc_paid":{"value":1850,"currency":"USD","value_usd":1850},"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.1247339,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"13","issue":null,"first_page":"163466","last_page":"163480"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T13083","display_name":"Advanced Text Analysis Techniques","score":0.9746000170707703,"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/T13083","display_name":"Advanced Text Analysis Techniques","score":0.9746000170707703,"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/T10064","display_name":"Complex Network Analysis Techniques","score":0.9320999979972839,"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/hypergraph","display_name":"Hypergraph","score":0.8518000245094299},{"id":"https://openalex.org/keywords/link","display_name":"Link (geometry)","score":0.5813000202178955},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.5651999711990356},{"id":"https://openalex.org/keywords/cluster-analysis","display_name":"Cluster analysis","score":0.5196999907493591},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.4147999882698059},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.34869998693466187},{"id":"https://openalex.org/keywords/dual","display_name":"Dual (grammatical number)","score":0.34200000762939453}],"concepts":[{"id":"https://openalex.org/C2781221856","wikidata":"https://www.wikidata.org/wiki/Q840247","display_name":"Hypergraph","level":2,"score":0.8518000245094299},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6862999796867371},{"id":"https://openalex.org/C2778753846","wikidata":"https://www.wikidata.org/wiki/Q6554239","display_name":"Link (geometry)","level":2,"score":0.5813000202178955},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.5651999711990356},{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.5196999907493591},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.41920000314712524},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.4147999882698059},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3797999918460846},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.3723999857902527},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.34869998693466187},{"id":"https://openalex.org/C2780980858","wikidata":"https://www.wikidata.org/wiki/Q110022","display_name":"Dual (grammatical number)","level":2,"score":0.34200000762939453},{"id":"https://openalex.org/C204241405","wikidata":"https://www.wikidata.org/wiki/Q461499","display_name":"Transformation (genetics)","level":3,"score":0.3393999934196472},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.3075999915599823},{"id":"https://openalex.org/C131992880","wikidata":"https://www.wikidata.org/wiki/Q2528185","display_name":"Subgraph isomorphism problem","level":3,"score":0.30399999022483826},{"id":"https://openalex.org/C88230418","wikidata":"https://www.wikidata.org/wiki/Q131476","display_name":"Graph theory","level":2,"score":0.2985000014305115},{"id":"https://openalex.org/C22047676","wikidata":"https://www.wikidata.org/wiki/Q898680","display_name":"Clustering coefficient","level":3,"score":0.28630000352859497},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.2757999897003174},{"id":"https://openalex.org/C2777212361","wikidata":"https://www.wikidata.org/wiki/Q5127848","display_name":"Class (philosophy)","level":2,"score":0.25780001282691956}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.1109/access.2025.3611308","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2025.3611308","pdf_url":null,"source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:c94901e97d8149cf9576c4c2a940bd85","is_oa":true,"landing_page_url":"https://doaj.org/article/c94901e97d8149cf9576c4c2a940bd85","pdf_url":null,"source":{"id":"https://openalex.org/S4306401280","display_name":"DOAJ (DOAJ: Directory of Open Access Journals)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"IEEE Access, Vol 13, Pp 163466-163480 (2025)","raw_type":"article"},{"id":"pmh:oai:figshare.com:article/30261091","is_oa":true,"landing_page_url":"https://figshare.com/articles/journal_contribution/Hypergraph_Neural_Networks_Based_on_Enclosing_Subgraph_Extraction_for_Temporal_Link_Prediction/30261091","pdf_url":null,"source":{"id":"https://openalex.org/S4377196282","display_name":"Figshare","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I4210132348","host_organization_name":"Figshare (United Kingdom)","host_organization_lineage":["https://openalex.org/I4210132348"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Text"}],"best_oa_location":{"id":"doi:10.1109/access.2025.3611308","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2025.3611308","pdf_url":null,"source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":39,"referenced_works":["https://openalex.org/W1556758605","https://openalex.org/W2026417691","https://openalex.org/W2154454189","https://openalex.org/W2163032501","https://openalex.org/W2561779919","https://openalex.org/W2565330852","https://openalex.org/W2742503211","https://openalex.org/W2788919350","https://openalex.org/W2963241486","https://openalex.org/W2964015378","https://openalex.org/W2998116985","https://openalex.org/W2998313947","https://openalex.org/W3035666843","https://openalex.org/W3085990079","https://openalex.org/W3117256259","https://openalex.org/W3132840818","https://openalex.org/W3160381762","https://openalex.org/W3177564060","https://openalex.org/W3204508881","https://openalex.org/W4205568429","https://openalex.org/W4282926996","https://openalex.org/W4385245566","https://openalex.org/W4387101694","https://openalex.org/W4388427850","https://openalex.org/W4388947331","https://openalex.org/W4390589313","https://openalex.org/W4393972734","https://openalex.org/W4396735777","https://openalex.org/W4400768551","https://openalex.org/W4401093660","https://openalex.org/W4401164050","https://openalex.org/W4401863534","https://openalex.org/W4402187046","https://openalex.org/W4403724224","https://openalex.org/W4404848216","https://openalex.org/W4406632986","https://openalex.org/W4407319120","https://openalex.org/W4409364135","https://openalex.org/W4409364207"],"related_works":[],"abstract_inverted_index":{"The":[0,98],"Graph":[1],"Neural":[2],"Network":[3],"(GNN)":[4],"methods":[5,44],"based":[6,93],"on":[7,94],"enclosing":[8,95,104],"subgraph":[9,96,105],"extraction":[10,106],"have":[11],"achieved":[12],"excellent":[13],"results":[14,141],"in":[15,70,154],"static":[16,71],"graph":[17,72,134],"link":[18,42,73,88,121,156,160],"prediction":[19,43,74,89,138,157,161],"tasks.":[20,75,162],"However,":[21],"most":[22],"real-world":[23],"networks":[24],"are":[25],"dynamic":[26],"and":[27,60,107,116,125,158],"evolve":[28],"over":[29],"time;":[30],"the":[31,39,57,80,113,128,132,137,151],"traditional":[32],"models":[33,153],"cannot":[34],"capture":[35],"temporal":[36,41,87,117],"evolution.":[37],"Moreover,":[38],"existing":[40],"mainly":[45],"use":[46],"simple":[47],"GNNs":[48],"to":[49,78,111,122,135,150],"learn":[50,112],"spatial":[51,115],"embeddings,":[52],"which":[53,91],"not":[54],"only":[55],"ignore":[56],"high-order":[58,114],"relationships":[59],"local":[61],"clustering":[62],"structures":[63],"between":[64],"node,":[65],"but":[66],"also":[67],"perform":[68],"poorly":[69],"In":[76],"order":[77],"address":[79],"above":[81],"problems,":[82],"we":[83],"propose":[84],"Hypergraph":[85],"for":[86],"(HGTLP)":[90],"is":[92],"extraction.":[97],"proposed":[99],"HGTLP":[100,144],"constructs":[101],"hypergraph":[102,109],"through":[103],"dual":[108],"transformation":[110],"embeddings":[118],"of":[119,131],"each":[120],"be":[123],"predicted,":[124],"then":[126],"combines":[127],"global":[129],"knowledge":[130],"collapsed":[133],"improve":[136],"accuracy.":[139],"Experimental":[140],"demonstrate":[142],"that":[143],"achieves":[145],"significant":[146],"performance":[147],"improvement":[148],"compared":[149],"state-of-the-art":[152],"both":[155],"new":[159]},"counts_by_year":[],"updated_date":"2026-03-27T05:58:40.876381","created_date":"2025-10-10T00:00:00"}