{"id":"https://openalex.org/W7140135426","doi":"https://doi.org/10.1109/tkde.2026.3676748","title":"Fairness-Aware Hypergraph Self-Supervised Learning With Sampling-Efficient Signals","display_name":"Fairness-Aware Hypergraph Self-Supervised Learning With Sampling-Efficient Signals","publication_year":2026,"publication_date":"2026-03-23","ids":{"openalex":"https://openalex.org/W7140135426","doi":"https://doi.org/10.1109/tkde.2026.3676748"},"language":null,"primary_location":{"id":"doi:10.1109/tkde.2026.3676748","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tkde.2026.3676748","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/A5130387698","display_name":"Fan Li","orcid":null},"institutions":[{"id":"https://openalex.org/I31746571","display_name":"UNSW Sydney","ror":"https://ror.org/03r8z3t63","country_code":"AU","type":"education","lineage":["https://openalex.org/I31746571"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Fan Li","raw_affiliation_strings":["School of Computer Science and Engineering, The University of New South Wales, Sydney, NSW, Australia"],"raw_orcid":"https://orcid.org/0009-0004-2951-2226","affiliations":[{"raw_affiliation_string":"School of Computer Science and Engineering, The University of New South Wales, Sydney, NSW, Australia","institution_ids":["https://openalex.org/I31746571"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5130361656","display_name":"Xiaoyang Wang","orcid":null},"institutions":[{"id":"https://openalex.org/I31746571","display_name":"UNSW Sydney","ror":"https://ror.org/03r8z3t63","country_code":"AU","type":"education","lineage":["https://openalex.org/I31746571"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Xiaoyang Wang","raw_affiliation_strings":["School of Computer Science and Engineering, The University of New South Wales, Sydney, NSW, Australia"],"raw_orcid":"https://orcid.org/0000-0003-3554-3219","affiliations":[{"raw_affiliation_string":"School of Computer Science and Engineering, The University of New South Wales, Sydney, NSW, Australia","institution_ids":["https://openalex.org/I31746571"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5112386538","display_name":"Dawei Cheng","orcid":null},"institutions":[{"id":"https://openalex.org/I116953780","display_name":"Tongji University","ror":"https://ror.org/03rc6as71","country_code":"CN","type":"education","lineage":["https://openalex.org/I116953780"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Dawei Cheng","raw_affiliation_strings":["Department of Computer Science and Technology, Tongji University, Shanghai, China"],"raw_orcid":"https://orcid.org/0000-0002-5877-7387","affiliations":[{"raw_affiliation_string":"Department of Computer Science and Technology, Tongji University, Shanghai, China","institution_ids":["https://openalex.org/I116953780"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5130343910","display_name":"Ying Zhang","orcid":null},"institutions":[{"id":"https://openalex.org/I75059550","display_name":"Zhejiang Gongshang University","ror":"https://ror.org/0569mkk41","country_code":"CN","type":"education","lineage":["https://openalex.org/I75059550"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Ying Zhang","raw_affiliation_strings":["Zhejiang Gongshang University, Hangzhou, China"],"raw_orcid":"https://orcid.org/0009-0006-4418-7024","affiliations":[{"raw_affiliation_string":"Zhejiang Gongshang University, Hangzhou, China","institution_ids":["https://openalex.org/I75059550"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5130397863","display_name":"Wenjie Zhang","orcid":null},"institutions":[{"id":"https://openalex.org/I31746571","display_name":"UNSW Sydney","ror":"https://ror.org/03r8z3t63","country_code":"AU","type":"education","lineage":["https://openalex.org/I31746571"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Wenjie Zhang","raw_affiliation_strings":["School of Computer Science and Engineering, The University of New South Wales, Sydney, NSW, Australia"],"raw_orcid":"https://orcid.org/0000-0001-6572-2600","affiliations":[{"raw_affiliation_string":"School of Computer Science and Engineering, The University of New South Wales, Sydney, NSW, Australia","institution_ids":["https://openalex.org/I31746571"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5101972030","display_name":"X. Sheldon Lin","orcid":"https://orcid.org/0009-0000-0686-5206"},"institutions":[{"id":"https://openalex.org/I183067930","display_name":"Shanghai Jiao Tong University","ror":"https://ror.org/0220qvk04","country_code":"CN","type":"education","lineage":["https://openalex.org/I183067930"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xuemin Lin","raw_affiliation_strings":["Antai College of Economics and Management, Shanghai Jiao Tong University, Shanghai, China"],"raw_orcid":"https://orcid.org/0000-0003-2396-7225","affiliations":[{"raw_affiliation_string":"Antai College of Economics and Management, Shanghai Jiao Tong University, Shanghai, China","institution_ids":["https://openalex.org/I183067930"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":4,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.29695588,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"38","issue":"6","first_page":"3611","last_page":"3625"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10883","display_name":"Ethics and Social Impacts of AI","score":0.4706000089645386,"subfield":{"id":"https://openalex.org/subfields/3311","display_name":"Safety Research"},"field":{"id":"https://openalex.org/fields/33","display_name":"Social Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},"topics":[{"id":"https://openalex.org/T10883","display_name":"Ethics and Social Impacts of AI","score":0.4706000089645386,"subfield":{"id":"https://openalex.org/subfields/3311","display_name":"Safety Research"},"field":{"id":"https://openalex.org/fields/33","display_name":"Social Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T11273","display_name":"Advanced Graph Neural Networks","score":0.16369999945163727,"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/T11704","display_name":"Mobile Crowdsensing and Crowdsourcing","score":0.04500000178813934,"subfield":{"id":"https://openalex.org/subfields/1706","display_name":"Computer Science Applications"},"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/hypergraph","display_name":"Hypergraph","score":0.7232999801635742},{"id":"https://openalex.org/keywords/signal-processing","display_name":"Signal processing","score":0.34769999980926514},{"id":"https://openalex.org/keywords/electronic-mail","display_name":"Electronic mail","score":0.2955000102519989},{"id":"https://openalex.org/keywords/algorithm-design","display_name":"Algorithm design","score":0.29019999504089355},{"id":"https://openalex.org/keywords/data-modeling","display_name":"Data modeling","score":0.28690001368522644},{"id":"https://openalex.org/keywords/boolean-function","display_name":"Boolean function","score":0.2791999876499176},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.2773999869823456}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7369999885559082},{"id":"https://openalex.org/C2781221856","wikidata":"https://www.wikidata.org/wiki/Q840247","display_name":"Hypergraph","level":2,"score":0.7232999801635742},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.4810999929904938},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3749000132083893},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3499999940395355},{"id":"https://openalex.org/C104267543","wikidata":"https://www.wikidata.org/wiki/Q208163","display_name":"Signal processing","level":3,"score":0.34769999980926514},{"id":"https://openalex.org/C3020028006","wikidata":"https://www.wikidata.org/wiki/Q9158","display_name":"Electronic mail","level":2,"score":0.2955000102519989},{"id":"https://openalex.org/C106516650","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm design","level":2,"score":0.29019999504089355},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.28690001368522644},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.2833999991416931},{"id":"https://openalex.org/C187455244","wikidata":"https://www.wikidata.org/wiki/Q942353","display_name":"Boolean function","level":2,"score":0.2791999876499176},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.2773999869823456},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.27149999141693115},{"id":"https://openalex.org/C78548338","wikidata":"https://www.wikidata.org/wiki/Q2493","display_name":"Data compression","level":2,"score":0.2542000114917755},{"id":"https://openalex.org/C88230418","wikidata":"https://www.wikidata.org/wiki/Q131476","display_name":"Graph theory","level":2,"score":0.2540999948978424},{"id":"https://openalex.org/C116834253","wikidata":"https://www.wikidata.org/wiki/Q2039217","display_name":"Identification (biology)","level":2,"score":0.2526000142097473},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.25119999051094055},{"id":"https://openalex.org/C45340560","wikidata":"https://www.wikidata.org/wiki/Q215382","display_name":"Disjoint sets","level":2,"score":0.25060001015663147}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tkde.2026.3676748","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tkde.2026.3676748","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/G8582128816","display_name":null,"funder_award_id":"62472317","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":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":53,"referenced_works":["https://openalex.org/W2006904655","https://openalex.org/W2085988980","https://openalex.org/W2100960835","https://openalex.org/W2130354913","https://openalex.org/W2170057991","https://openalex.org/W2397674542","https://openalex.org/W2796093898","https://openalex.org/W2892880750","https://openalex.org/W2992026644","https://openalex.org/W3035520037","https://openalex.org/W3035524453","https://openalex.org/W3035635319","https://openalex.org/W3080365325","https://openalex.org/W3081304929","https://openalex.org/W3095746859","https://openalex.org/W3117178429","https://openalex.org/W3117562715","https://openalex.org/W3165159705","https://openalex.org/W3171764584","https://openalex.org/W3181414820","https://openalex.org/W3188916965","https://openalex.org/W3198760092","https://openalex.org/W3210658351","https://openalex.org/W4206323856","https://openalex.org/W4207040932","https://openalex.org/W4213199213","https://openalex.org/W4214689175","https://openalex.org/W4224983022","https://openalex.org/W4226110810","https://openalex.org/W4281861579","https://openalex.org/W4285606137","https://openalex.org/W4289433255","https://openalex.org/W4290878031","https://openalex.org/W4306317457","https://openalex.org/W4362714312","https://openalex.org/W4379767400","https://openalex.org/W4381613464","https://openalex.org/W4382237408","https://openalex.org/W4382318032","https://openalex.org/W4384111736","https://openalex.org/W4384887328","https://openalex.org/W4392131519","https://openalex.org/W4393147769","https://openalex.org/W4393159796","https://openalex.org/W4394994643","https://openalex.org/W4396736359","https://openalex.org/W4396844344","https://openalex.org/W4401857188","https://openalex.org/W4407953124","https://openalex.org/W4409149976","https://openalex.org/W4409364135","https://openalex.org/W4409364207","https://openalex.org/W4414360696"],"related_works":[],"abstract_inverted_index":{"Self-supervised":[0],"learning":[1,10,24],"(SSL)":[2],"provides":[3],"a":[4,59,72,98,181,188,195],"promising":[5],"paradigm":[6],"for":[7],"hypergraph":[8,18,73,133,165,197],"representation":[9],"without":[11],"reliance":[12],"on":[13,87,267],"costly":[14],"labels.":[15],"However,":[16],"existing":[17],"SSL":[19,74],"methods":[20],"predominantly":[21],"employ":[22],"contrastive":[23,101],"with":[25,77,187],"instance":[26],"level":[27,218,234],"discrimination,":[28],"encountering":[29],"two":[30,83,202],"significant":[31],"challenges:":[32],"(1)":[33],"Unreliable":[34],"negative":[35,40,63,114],"sampling,":[36],"where":[37],"arbitrarily":[38],"selected":[39],"samples":[41],"introduce":[42],"bias":[43,116],"by":[44,143,176,247],"misclassifying":[45],"similar":[46],"and":[47,50,94,117,126,148,207,252,275],"dissimilar":[48],"pairs;":[49],"(2)":[51],"High":[52],"computational":[53],"cost,":[54],"as":[55,92],"effective":[56],"training":[57,173],"requires":[58],"large":[60],"number":[61],"of":[62,108,161,184,239,262,277,289],"samples.":[64],"To":[65,150,193],"address":[66,151],"these":[67,111,130],"limitations,":[68],"we":[69,154,178,258],"propose":[70,179],"SE-HSSL,":[71],"frame":[75],"work":[76],"three":[78],"sampling-efficient":[79],"self-supervised":[80],"objectives.":[81],"Specifically,":[82,210],"sampling-free":[84],"objectives":[85],"based":[86],"canonical":[88],"correlation":[89],"analysis":[90],"serve":[91],"node-":[93],"group-level":[95],"signals,":[96],"while":[97,226],"hierarchical":[99],"membership-level":[100],"objective":[102],"exploits":[103],"the":[104,172,199,216,232,237,249,260,272],"cascading":[105],"overlap":[106],"structure":[107,233],"hypergraphs.":[109],"Overall,":[110],"designs":[112],"mitigate":[113,259],"sampling":[115,119],"enhance":[118],"efficiency,":[120],"leading":[121],"to":[122,137,219,235],"better":[123],"downstream":[124],"performance":[125],"faster":[127],"training.":[128],"Beyond":[129],"challenges,":[131],"deep":[132],"models":[134],"are":[135],"prone":[136],"biased":[138,254],"predictions":[139],"against":[140],"groups":[141,163,242],"defined":[142],"sensitive":[144,169,224,241,263],"attributes":[145],"(e.g.,":[146],"gender":[147],"race).":[149],"fairness":[152],"concerns,":[153],"first":[155],"theoretically":[156],"show":[157],"that":[158],"imbalanced":[159],"contributions":[160,238],"demographic":[162],"during":[164,243],"message":[166,244],"passing":[167],"amplify":[168],"biases":[170],"in":[171,256,287],"data.":[174],"Motivated":[175],"this,":[177],"FairHSSL,":[180],"fairness-aware":[182],"variant":[183],"SE-HSSL":[185],"equipped":[186],"two-level":[189],"debiasing":[190,206],"augmentation":[191,200],"strategy.":[192],"generate":[194],"fair":[196,250],"view,":[198],"integrates":[201],"complementary":[203],"components:":[204],"feature-level":[205],"structure-level":[208],"perturbation.":[209],"orthogonal":[211],"projection":[212],"is":[213,229],"applied":[214],"at":[215,231],"feature":[217],"decouple":[220],"node":[221],"features":[222],"from":[223],"attributes,":[225],"rebalance-based":[227],"perturbation":[228],"introduced":[230],"equalize":[236],"different":[240],"passing.":[245],"Finally,":[246],"aligning":[248],"augmented":[251],"original":[253],"views":[255],"SSL,":[257],"influence":[261],"information.":[264],"Extensive":[265],"experiments":[266],"10":[268],"real-world":[269],"hypergraphs":[270],"demonstrate":[271],"superior":[273],"effectiveness":[274],"efficiency":[276],"SE-HSSL.":[278],"Moreover,":[279],"FairHSSL":[280],"consistently":[281],"outperforms":[282],"state":[283],"of-the-art":[284],"(SOTA)":[285],"baselines":[286],"terms":[288],"utility-fairness":[290],"trade-off":[291],"across":[292],"all":[293],"datasets.":[294]},"counts_by_year":[],"updated_date":"2026-05-07T06:04:25.777469","created_date":"2026-03-24T00:00:00"}