{"id":"https://openalex.org/W4412876935","doi":"https://doi.org/10.1145/3711896.3737392","title":"Fairness-Aware Graph Learning: A Benchmark","display_name":"Fairness-Aware Graph Learning: A Benchmark","publication_year":2025,"publication_date":"2025-08-03","ids":{"openalex":"https://openalex.org/W4412876935","doi":"https://doi.org/10.1145/3711896.3737392"},"language":"en","primary_location":{"id":"doi:10.1145/3711896.3737392","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3711896.3737392","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3711896.3737392","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining V.2","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://dl.acm.org/doi/pdf/10.1145/3711896.3737392","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5047581320","display_name":"Yushun Dong","orcid":"https://orcid.org/0000-0001-7504-6159"},"institutions":[{"id":"https://openalex.org/I103163165","display_name":"Florida State University","ror":"https://ror.org/05g3dte14","country_code":"US","type":"education","lineage":["https://openalex.org/I103163165"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Yushun Dong","raw_affiliation_strings":["Florida State University, Tallahassee, Florida, USA"],"raw_orcid":"https://orcid.org/0000-0001-7504-6159","affiliations":[{"raw_affiliation_string":"Florida State University, Tallahassee, Florida, USA","institution_ids":["https://openalex.org/I103163165"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100326218","display_name":"Song Wang","orcid":"https://orcid.org/0000-0003-1273-7694"},"institutions":[{"id":"https://openalex.org/I51556381","display_name":"University of Virginia","ror":"https://ror.org/0153tk833","country_code":"US","type":"education","lineage":["https://openalex.org/I51556381"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Song Wang","raw_affiliation_strings":["The University of Virginia, Charlottesville, VA, USA"],"raw_orcid":"https://orcid.org/0000-0003-1273-7694","affiliations":[{"raw_affiliation_string":"The University of Virginia, Charlottesville, VA, USA","institution_ids":["https://openalex.org/I51556381"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5094050777","display_name":"Zhenyu Lei","orcid":"https://orcid.org/0000-0002-5606-3268"},"institutions":[{"id":"https://openalex.org/I51556381","display_name":"University of Virginia","ror":"https://ror.org/0153tk833","country_code":"US","type":"education","lineage":["https://openalex.org/I51556381"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Zhenyu Lei","raw_affiliation_strings":["The University of Virginia, Charlottesville, VA, USA"],"raw_orcid":"https://orcid.org/0000-0002-5606-3268","affiliations":[{"raw_affiliation_string":"The University of Virginia, Charlottesville, VA, USA","institution_ids":["https://openalex.org/I51556381"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5097767807","display_name":"Zaiyi Zheng","orcid":"https://orcid.org/0009-0003-0685-0057"},"institutions":[{"id":"https://openalex.org/I51556381","display_name":"University of Virginia","ror":"https://ror.org/0153tk833","country_code":"US","type":"education","lineage":["https://openalex.org/I51556381"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Zaiyi Zheng","raw_affiliation_strings":["The University of Virginia, Charlottesville, VA, USA"],"raw_orcid":"https://orcid.org/0009-0003-0685-0057","affiliations":[{"raw_affiliation_string":"The University of Virginia, Charlottesville, VA, USA","institution_ids":["https://openalex.org/I51556381"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5032200312","display_name":"Jing Ma","orcid":"https://orcid.org/0000-0003-4237-6607"},"institutions":[{"id":"https://openalex.org/I58956616","display_name":"Case Western Reserve University","ror":"https://ror.org/051fd9666","country_code":"US","type":"education","lineage":["https://openalex.org/I58956616"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Jing Ma","raw_affiliation_strings":["Case Western Reserve University, Cleveland, USA"],"raw_orcid":"https://orcid.org/0000-0003-4237-6607","affiliations":[{"raw_affiliation_string":"Case Western Reserve University, Cleveland, USA","institution_ids":["https://openalex.org/I58956616"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100418485","display_name":"Chen Chen","orcid":"https://orcid.org/0000-0002-7099-7905"},"institutions":[{"id":"https://openalex.org/I51556381","display_name":"University of Virginia","ror":"https://ror.org/0153tk833","country_code":"US","type":"education","lineage":["https://openalex.org/I51556381"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Chen Chen","raw_affiliation_strings":["The University of Virginia, Charlottesville, VA, USA"],"raw_orcid":"https://orcid.org/0000-0002-7099-7905","affiliations":[{"raw_affiliation_string":"The University of Virginia, Charlottesville, VA, USA","institution_ids":["https://openalex.org/I51556381"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5029588473","display_name":"Jundong Li","orcid":"https://orcid.org/0000-0002-1878-817X"},"institutions":[{"id":"https://openalex.org/I51556381","display_name":"University of Virginia","ror":"https://ror.org/0153tk833","country_code":"US","type":"education","lineage":["https://openalex.org/I51556381"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Jundong Li","raw_affiliation_strings":["The University of Virginia, Charlottesville, VA, USA"],"raw_orcid":"https://orcid.org/0000-0002-1878-817X","affiliations":[{"raw_affiliation_string":"The University of Virginia, Charlottesville, VA, USA","institution_ids":["https://openalex.org/I51556381"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"5402","last_page":"5412"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11273","display_name":"Advanced Graph Neural Networks","score":0.9984999895095825,"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":0.9984999895095825,"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/T10883","display_name":"Ethics and Social Impacts of AI","score":0.991100013256073,"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/T10764","display_name":"Privacy-Preserving Technologies in Data","score":0.9656000137329102,"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":0.7624839544296265},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.647020161151886},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.535210371017456},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.41093912720680237},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.37215375900268555},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.35685500502586365}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7624839544296265},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.647020161151886},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.535210371017456},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.41093912720680237},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.37215375900268555},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.35685500502586365},{"id":"https://openalex.org/C13280743","wikidata":"https://www.wikidata.org/wiki/Q131089","display_name":"Geodesy","level":1,"score":0.0},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3711896.3737392","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3711896.3737392","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3711896.3737392","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining V.2","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.1145/3711896.3737392","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3711896.3737392","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3711896.3737392","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining V.2","raw_type":"proceedings-article"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G3230151192","display_name":"SCC-IRG Track 1: Community-Responsive Electrified and Adaptive Transit Ecosystem (CREATE): Planning, Operations, and Management","funder_award_id":"2411248","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G4932945845","display_name":"CAREER: Toward A Knowledge-Guided Framework for Personalized Decision Making","funder_award_id":"2144209","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G7344714104","display_name":"Collaborative Research: SAI-R: Dynamical Coupling of Physical and Social Infrastructures: Evaluating the Impacts of Social Capital on Access to Safe Well Water","funder_award_id":"2228534","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G7378744750","display_name":"III: Small: Collaborative Research: Demystifying Deep Learning on Graphs: From Basic Operations to Applications","funder_award_id":"2006844","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G8441734984","display_name":null,"funder_award_id":"IIS-2006844,IIS-2144209,IIS-2223769,CNS-2154962,BCS-2228534,CMMI-2411248","funder_id":"https://openalex.org/F4320323817","funder_display_name":"Universitas Brawijaya"}],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"},{"id":"https://openalex.org/F4320323817","display_name":"Universitas Brawijaya","ror":"https://ror.org/01wk3d929"},{"id":"https://openalex.org/F4320337345","display_name":"Office of Naval Research","ror":"https://ror.org/00rk2pe57"},{"id":"https://openalex.org/F4320337391","display_name":"Division of Civil, Mechanical and Manufacturing Innovation","ror":"https://ror.org/028yd4c30"}],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4412876935.pdf","grobid_xml":"https://content.openalex.org/works/W4412876935.grobid-xml"},"referenced_works_count":53,"referenced_works":["https://openalex.org/W569478347","https://openalex.org/W1975982181","https://openalex.org/W2022166150","https://openalex.org/W2100960835","https://openalex.org/W2110953678","https://openalex.org/W2142517301","https://openalex.org/W2735272571","https://openalex.org/W2786016794","https://openalex.org/W2907492528","https://openalex.org/W2935051602","https://openalex.org/W2950393809","https://openalex.org/W2951823788","https://openalex.org/W2954709318","https://openalex.org/W2963214893","https://openalex.org/W2966133050","https://openalex.org/W2990138404","https://openalex.org/W3012895300","https://openalex.org/W3012996519","https://openalex.org/W3022201018","https://openalex.org/W3034616536","https://openalex.org/W3080365325","https://openalex.org/W3106390645","https://openalex.org/W3117178429","https://openalex.org/W3122083688","https://openalex.org/W3123909522","https://openalex.org/W3125675327","https://openalex.org/W3153432523","https://openalex.org/W3158511434","https://openalex.org/W3171764584","https://openalex.org/W3192448376","https://openalex.org/W3194259208","https://openalex.org/W3210378911","https://openalex.org/W3215602957","https://openalex.org/W4205718326","https://openalex.org/W4207036031","https://openalex.org/W4226237846","https://openalex.org/W4281861579","https://openalex.org/W4290875022","https://openalex.org/W4290878493","https://openalex.org/W4303426745","https://openalex.org/W4306405505","https://openalex.org/W4362714312","https://openalex.org/W4380091476","https://openalex.org/W4382239632","https://openalex.org/W4386158663","https://openalex.org/W4387848665","https://openalex.org/W4388553381","https://openalex.org/W4392131519","https://openalex.org/W4393146624","https://openalex.org/W4396220739","https://openalex.org/W4401856727","https://openalex.org/W6602989878","https://openalex.org/W6604424379"],"related_works":["https://openalex.org/W2961085424","https://openalex.org/W4306674287","https://openalex.org/W4387369504","https://openalex.org/W4394896187","https://openalex.org/W3170094116","https://openalex.org/W4386462264","https://openalex.org/W3107602296","https://openalex.org/W4364306694","https://openalex.org/W4312192474","https://openalex.org/W4283697347"],"abstract_inverted_index":{"Fairness-aware":[0],"graph":[1,22,48,108,131],"learning":[2,23,49,109,132],"has":[3],"gained":[4],"increasing":[5],"attention":[6],"in":[7,111,138],"recent":[8],"years.":[9],"Nevertheless,":[10],"there":[11],"lacks":[12],"a":[13,54],"comprehensive":[14],"benchmark":[15,43],"to":[16,65,134],"evaluate":[17,66],"and":[18,58,83,95],"compare":[19],"different":[20,80],"fairness-aware":[21,47,107,130],"methods,":[24],"which":[25],"blocks":[26],"practitioners":[27],"from":[28,69],"choosing":[29],"appropriate":[30],"ones":[31],"for":[32,105],"broader":[33],"real-world":[34,63],"applications.":[35,112],"In":[36],"this":[37,119,139],"paper,":[38],"we":[39,52,101],"present":[40],"an":[41,123],"extensive":[42],"on":[44,61],"ten":[45],"representative":[46,129],"methods.":[50,99],"Specifically,":[51],"design":[53],"systematic":[55],"evaluation":[56],"protocol":[57],"conduct":[59],"experiments":[60],"seven":[62],"datasets":[64],"these":[67],"methods":[68,110,133],"multiple":[70],"perspectives,":[71],"including":[72],"group":[73],"fairness,":[74,76],"individual":[75],"the":[77,93,114],"balance":[78],"between":[79],"fairness":[81],"criteria,":[82],"computational":[84],"efficiency.":[85],"Our":[86],"in-depth":[87],"analysis":[88],"reveals":[89],"key":[90],"insights":[91],"into":[92],"strengths":[94],"limitations":[96],"of":[97,116],"existing":[98],"Additionally,":[100],"provide":[102],"practical":[103],"guidance":[104],"applying":[106],"To":[113],"best":[115],"our":[117],"knowledge,":[118],"work":[120],"serves":[121],"as":[122],"initial":[124],"step":[125],"towards":[126],"comprehensively":[127],"understanding":[128],"facilitate":[135],"future":[136],"advancements":[137],"area.":[140],"Open-source":[141],"code":[142],"can":[143],"be":[144],"found":[145],"at:":[146],"https://github.com/yushundong/Fairness-Aware-Graph-Learning-Benchmark.":[147]},"counts_by_year":[],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}
