{"id":"https://openalex.org/W4392543831","doi":"https://doi.org/10.1109/tnnls.2024.3371382","title":"T-HyperGNNs: Hypergraph Neural Networks via Tensor Representations","display_name":"T-HyperGNNs: Hypergraph Neural Networks via Tensor Representations","publication_year":2024,"publication_date":"2024-03-07","ids":{"openalex":"https://openalex.org/W4392543831","doi":"https://doi.org/10.1109/tnnls.2024.3371382","pmid":"https://pubmed.ncbi.nlm.nih.gov/38451750"},"language":"en","primary_location":{"id":"doi:10.1109/tnnls.2024.3371382","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tnnls.2024.3371382","pdf_url":null,"source":{"id":"https://openalex.org/S4210175523","display_name":"IEEE Transactions on Neural Networks and Learning Systems","issn_l":"2162-237X","issn":["2162-237X","2162-2388"],"is_oa":false,"is_in_doaj":false,"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":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Neural Networks and Learning Systems","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","pubmed"],"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/A5101914875","display_name":"Fuli Wang","orcid":"https://orcid.org/0000-0002-7062-9432"},"institutions":[{"id":"https://openalex.org/I86501945","display_name":"University of Delaware","ror":"https://ror.org/01sbq1a82","country_code":"US","type":"education","lineage":["https://openalex.org/I86501945"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Fuli Wang","raw_affiliation_strings":["Institute for Financial Services Analytics, University of Delaware, Newark, DE, USA"],"affiliations":[{"raw_affiliation_string":"Institute for Financial Services Analytics, University of Delaware, Newark, DE, USA","institution_ids":["https://openalex.org/I86501945"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5033581148","display_name":"Karelia Pena-Pena","orcid":"https://orcid.org/0000-0001-8214-6852"},"institutions":[{"id":"https://openalex.org/I86501945","display_name":"University of Delaware","ror":"https://ror.org/01sbq1a82","country_code":"US","type":"education","lineage":["https://openalex.org/I86501945"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Karelia Pena-Pena","raw_affiliation_strings":["Department of Electrical and Computer Engineering, University of Delaware, Newark, DE, USA"],"affiliations":[{"raw_affiliation_string":"Department of Electrical and Computer Engineering, University of Delaware, Newark, DE, USA","institution_ids":["https://openalex.org/I86501945"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5057648628","display_name":"Wei Qian","orcid":"https://orcid.org/0000-0003-1022-1141"},"institutions":[{"id":"https://openalex.org/I86501945","display_name":"University of Delaware","ror":"https://ror.org/01sbq1a82","country_code":"US","type":"education","lineage":["https://openalex.org/I86501945"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Wei Qian","raw_affiliation_strings":["Department of Applied Economics and Statistics, University of Delaware, Newark, DE, USA"],"affiliations":[{"raw_affiliation_string":"Department of Applied Economics and Statistics, University of Delaware, Newark, DE, USA","institution_ids":["https://openalex.org/I86501945"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5005357824","display_name":"Gonzalo R. Arce","orcid":"https://orcid.org/0000-0001-7163-7111"},"institutions":[{"id":"https://openalex.org/I86501945","display_name":"University of Delaware","ror":"https://ror.org/01sbq1a82","country_code":"US","type":"education","lineage":["https://openalex.org/I86501945"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Gonzalo R. Arce","raw_affiliation_strings":["Department of Electrical and Computer Engineering, University of Delaware, Newark, DE, USA"],"affiliations":[{"raw_affiliation_string":"Department of Electrical and Computer Engineering, University of Delaware, Newark, DE, USA","institution_ids":["https://openalex.org/I86501945"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5101914875"],"corresponding_institution_ids":["https://openalex.org/I86501945"],"apc_list":null,"apc_paid":null,"fwci":7.0081,"has_fulltext":false,"cited_by_count":23,"citation_normalized_percentile":{"value":0.97906977,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":94,"max":100},"biblio":{"volume":"36","issue":"3","first_page":"5044","last_page":"5058"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12303","display_name":"Tensor decomposition and applications","score":0.9988999962806702,"subfield":{"id":"https://openalex.org/subfields/2605","display_name":"Computational Mathematics"},"field":{"id":"https://openalex.org/fields/26","display_name":"Mathematics"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T12303","display_name":"Tensor decomposition and applications","score":0.9988999962806702,"subfield":{"id":"https://openalex.org/subfields/2605","display_name":"Computational Mathematics"},"field":{"id":"https://openalex.org/fields/26","display_name":"Mathematics"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11273","display_name":"Advanced Graph Neural Networks","score":0.9954000115394592,"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/T11304","display_name":"Advanced Neuroimaging Techniques and Applications","score":0.9890000224113464,"subfield":{"id":"https://openalex.org/subfields/2741","display_name":"Radiology, Nuclear Medicine and Imaging"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/hypergraph","display_name":"Hypergraph","score":0.9357580542564392},{"id":"https://openalex.org/keywords/convolution","display_name":"Convolution (computer science)","score":0.6587550044059753},{"id":"https://openalex.org/keywords/tensor","display_name":"Tensor (intrinsic definition)","score":0.5996531844139099},{"id":"https://openalex.org/keywords/node","display_name":"Node (physics)","score":0.5487129092216492},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.49352729320526123},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.40843552350997925},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.40545517206192017},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.3631983697414398},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.3253245949745178},{"id":"https://openalex.org/keywords/discrete-mathematics","display_name":"Discrete mathematics","score":0.24477127194404602},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.16278395056724548},{"id":"https://openalex.org/keywords/pure-mathematics","display_name":"Pure mathematics","score":0.12954586744308472}],"concepts":[{"id":"https://openalex.org/C2781221856","wikidata":"https://www.wikidata.org/wiki/Q840247","display_name":"Hypergraph","level":2,"score":0.9357580542564392},{"id":"https://openalex.org/C45347329","wikidata":"https://www.wikidata.org/wiki/Q5166604","display_name":"Convolution (computer science)","level":3,"score":0.6587550044059753},{"id":"https://openalex.org/C155281189","wikidata":"https://www.wikidata.org/wiki/Q3518150","display_name":"Tensor (intrinsic definition)","level":2,"score":0.5996531844139099},{"id":"https://openalex.org/C62611344","wikidata":"https://www.wikidata.org/wiki/Q1062658","display_name":"Node (physics)","level":2,"score":0.5487129092216492},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.49352729320526123},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.40843552350997925},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.40545517206192017},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.3631983697414398},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3253245949745178},{"id":"https://openalex.org/C118615104","wikidata":"https://www.wikidata.org/wiki/Q121416","display_name":"Discrete mathematics","level":1,"score":0.24477127194404602},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.16278395056724548},{"id":"https://openalex.org/C202444582","wikidata":"https://www.wikidata.org/wiki/Q837863","display_name":"Pure mathematics","level":1,"score":0.12954586744308472},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.0},{"id":"https://openalex.org/C66938386","wikidata":"https://www.wikidata.org/wiki/Q633538","display_name":"Structural engineering","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/tnnls.2024.3371382","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tnnls.2024.3371382","pdf_url":null,"source":{"id":"https://openalex.org/S4210175523","display_name":"IEEE Transactions on Neural Networks and Learning Systems","issn_l":"2162-237X","issn":["2162-237X","2162-2388"],"is_oa":false,"is_in_doaj":false,"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":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Neural Networks and Learning Systems","raw_type":"journal-article"},{"id":"pmid:38451750","is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/38451750","pdf_url":null,"source":{"id":"https://openalex.org/S4306525036","display_name":"PubMed","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE transactions on neural networks and learning systems","raw_type":null}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G4212854570","display_name":null,"funder_award_id":"CCF-2230161","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G5069501518","display_name":null,"funder_award_id":"DMS-1916376","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G5194925859","display_name":null,"funder_award_id":"FA9550-22-1-0362","funder_id":"https://openalex.org/F4320338279","funder_display_name":"Air Force Office of Scientific Research"},{"id":"https://openalex.org/G8469022128","display_name":null,"funder_award_id":"CCF-2230162","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"}],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"},{"id":"https://openalex.org/F4320338279","display_name":"Air Force Office of Scientific Research","ror":"https://ror.org/011e9bt93"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":70,"referenced_works":["https://openalex.org/W1644641054","https://openalex.org/W1662382123","https://openalex.org/W1920022804","https://openalex.org/W1992426838","https://openalex.org/W2021122545","https://openalex.org/W2043571470","https://openalex.org/W2237805507","https://openalex.org/W2557781680","https://openalex.org/W2793768763","https://openalex.org/W2799162093","https://openalex.org/W2892880750","https://openalex.org/W2907492528","https://openalex.org/W2963414755","https://openalex.org/W2964015378","https://openalex.org/W2964182926","https://openalex.org/W2964255072","https://openalex.org/W2997848713","https://openalex.org/W3006296570","https://openalex.org/W3021853564","https://openalex.org/W3036159734","https://openalex.org/W3037346199","https://openalex.org/W3085990079","https://openalex.org/W3094071795","https://openalex.org/W3104030692","https://openalex.org/W3105663757","https://openalex.org/W3106228791","https://openalex.org/W3120623710","https://openalex.org/W3172291036","https://openalex.org/W3177994886","https://openalex.org/W3190664711","https://openalex.org/W3194210786","https://openalex.org/W3199723057","https://openalex.org/W4200597037","https://openalex.org/W4207057190","https://openalex.org/W4211075818","https://openalex.org/W4225565607","https://openalex.org/W4283260867","https://openalex.org/W4287113446","https://openalex.org/W4293107381","https://openalex.org/W4302293334","https://openalex.org/W4306910185","https://openalex.org/W4319990429","https://openalex.org/W4324125125","https://openalex.org/W4362513574","https://openalex.org/W4365597529","https://openalex.org/W4367835120","https://openalex.org/W4376851410","https://openalex.org/W4382119054","https://openalex.org/W4382119297","https://openalex.org/W4383899689","https://openalex.org/W6637178625","https://openalex.org/W6720006811","https://openalex.org/W6738964360","https://openalex.org/W6743676628","https://openalex.org/W6762490134","https://openalex.org/W6763422710","https://openalex.org/W6779339669","https://openalex.org/W6784312798","https://openalex.org/W6796924431","https://openalex.org/W6797571766","https://openalex.org/W6801525198","https://openalex.org/W6838461310","https://openalex.org/W6839120580","https://openalex.org/W6839907422","https://openalex.org/W6845902106","https://openalex.org/W6846355620","https://openalex.org/W6846828087","https://openalex.org/W6846981623","https://openalex.org/W6849999388","https://openalex.org/W6851578199"],"related_works":["https://openalex.org/W4376608589","https://openalex.org/W1537073411","https://openalex.org/W1948107826","https://openalex.org/W3138003926","https://openalex.org/W1630514295","https://openalex.org/W4300037846","https://openalex.org/W4288275998","https://openalex.org/W2963081352","https://openalex.org/W4376608938","https://openalex.org/W2472555608"],"abstract_inverted_index":{"Hypergraph":[0],"neural":[1,9],"networks":[2,10],"(HyperGNNs)":[3],"are":[4,189],"a":[5,20,23,28,37,83,143,152,181,192],"family":[6],"of":[7,96,178,186,195],"deep":[8],"designed":[11],"to":[12,118,135,158],"perform":[13],"inference":[14],"on":[15,36,51],"hypergraphs.":[16],"HyperGNNs":[17,43,103],"follow":[18],"either":[19],"spectral":[21,120],"or":[22,30],"spatial":[24],"approach,":[25],"in":[26,191],"which":[27],"convolution":[29,107,133,139],"message-passing":[31],"operation":[32],"is":[33,109,202],"conducted":[34],"based":[35],"hypergraph":[38,62,78,171,197],"algebraic":[39],"descriptor.":[40],"While":[41],"many":[42],"have":[44,55],"been":[45,56],"proposed":[46],"and":[47,65,81,101,140,173],"achieved":[48],"state-of-the-art":[49,160],"performance":[50],"broad":[52],"applications,":[53],"there":[54],"limited":[57],"attempts":[58],"at":[59,204],"exploring":[60],"high-dimensional":[61],"descriptors":[63],"(tensors)":[64],"joint":[66,176],"node":[67],"interactions":[68,90],"carried":[69],"by":[70,150],"hyperedges.":[71],"In":[72],"this":[73],"article,":[74],"we":[75,129],"depart":[76],"from":[77],"matrix":[79],"representations":[80],"present":[82],"new":[84],"tensor-HyperGNN":[85],"(T-HyperGNN)":[86],"framework":[87,94],"with":[88],"cross-node":[89],"(CNIs).":[91],"The":[92,105,199],"T-HyperGNN":[93],"consists":[95],"T-spectral":[97,106,132],"convolution,":[98,100],"T-spatial":[99,138],"T-message-passing":[102],"(T-MPHN).":[104],"HyperGNN":[108],"defined":[110],"under":[111],"the":[112,119,131,137,159,175],"t-product":[113],"algebra":[114],"that":[115],"closely":[116],"connects":[117],"space.":[121],"To":[122],"improve":[123],"computational":[124],"efficiency":[125],"for":[126,147],"large":[127],"hypergraphs,":[128],"localize":[130],"approach":[134],"formulate":[136],"further":[141],"devise":[142],"novel":[144],"tensor-message-passing":[145],"algorithm":[146],"practical":[148],"implementation":[149,200],"studying":[151],"compressed":[153],"adjacency":[154],"tensor":[155],"representation.":[156],"Compared":[157],"approaches,":[161],"our":[162,187],"T-HyperGNNs":[163,188],"preserve":[164],"intrinsic":[165],"high-order":[166],"network":[167],"structures":[168],"without":[169],"any":[170],"reduction":[172],"model":[174],"effects":[177],"nodes":[179],"through":[180],"CNI":[182],"layer.":[183],"These":[184],"advantages":[185],"demonstrated":[190],"wide":[193],"range":[194],"real-world":[196],"datasets.":[198],"code":[201],"available":[203],"https://github.com/wangfuli/T-HyperGNNs.git.":[205]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2025,"cited_by_count":16},{"year":2024,"cited_by_count":3},{"year":2023,"cited_by_count":2}],"updated_date":"2026-03-17T09:09:15.849793","created_date":"2025-10-10T00:00:00"}