{"id":"https://openalex.org/W4401857188","doi":"https://doi.org/10.1145/3637528.3671826","title":"Rethinking Fair Graph Neural Networks from Re-balancing","display_name":"Rethinking Fair Graph Neural Networks from Re-balancing","publication_year":2024,"publication_date":"2024-08-24","ids":{"openalex":"https://openalex.org/W4401857188","doi":"https://doi.org/10.1145/3637528.3671826"},"language":"en","primary_location":{"id":"doi:10.1145/3637528.3671826","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3637528.3671826","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","raw_type":"proceedings-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/A5084081468","display_name":"Zhixun Li","orcid":"https://orcid.org/0000-0001-6750-9002"},"institutions":[{"id":"https://openalex.org/I177725633","display_name":"Chinese University of Hong Kong","ror":"https://ror.org/00t33hh48","country_code":"HK","type":"education","lineage":["https://openalex.org/I177725633"]}],"countries":["HK"],"is_corresponding":false,"raw_author_name":"Zhixun Li","raw_affiliation_strings":["The Chinese University of Hong Kong, Hong Kong, Hong Kong"],"raw_orcid":"https://orcid.org/0000-0001-6750-9002","affiliations":[{"raw_affiliation_string":"The Chinese University of Hong Kong, Hong Kong, Hong Kong","institution_ids":["https://openalex.org/I177725633"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5047581320","display_name":"Yushun Dong","orcid":"https://orcid.org/0000-0001-7504-6159"},"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":"Yushun Dong","raw_affiliation_strings":["University of Virginia, Charlottesville, USA"],"raw_orcid":"https://orcid.org/0000-0001-7504-6159","affiliations":[{"raw_affiliation_string":"University of Virginia, Charlottesville, USA","institution_ids":["https://openalex.org/I51556381"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100409503","display_name":"Qiang Liu","orcid":"https://orcid.org/0000-0002-9233-3827"},"institutions":[{"id":"https://openalex.org/I19820366","display_name":"Chinese Academy of Sciences","ror":"https://ror.org/034t30j35","country_code":"CN","type":"government","lineage":["https://openalex.org/I19820366"]},{"id":"https://openalex.org/I4210094879","display_name":"Shandong Institute of Automation","ror":"https://ror.org/00qdtba35","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4210094879","https://openalex.org/I4210142748"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Qiang Liu","raw_affiliation_strings":["Institute of Automation, Chinese Academy of Sciences, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0002-9233-3827","affiliations":[{"raw_affiliation_string":"Institute of Automation, Chinese Academy of Sciences, Beijing, China","institution_ids":["https://openalex.org/I4210094879","https://openalex.org/I19820366"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5075642293","display_name":"Jeffrey Xu Yu","orcid":"https://orcid.org/0000-0002-9738-827X"},"institutions":[{"id":"https://openalex.org/I177725633","display_name":"Chinese University of Hong Kong","ror":"https://ror.org/00t33hh48","country_code":"HK","type":"education","lineage":["https://openalex.org/I177725633"]}],"countries":["HK"],"is_corresponding":false,"raw_author_name":"Jeffrey Xu Yu","raw_affiliation_strings":["The Chinese University of Hong Kong, Hong Kong, Hong Kong"],"raw_orcid":"https://orcid.org/0000-0002-9738-827X","affiliations":[{"raw_affiliation_string":"The Chinese University of Hong Kong, Hong Kong, Hong Kong","institution_ids":["https://openalex.org/I177725633"]}]}],"institutions":[],"countries_distinct_count":3,"institutions_distinct_count":4,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":3.6656,"has_fulltext":false,"cited_by_count":12,"citation_normalized_percentile":{"value":0.93833314,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":98,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"1736","last_page":"1745"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11273","display_name":"Advanced Graph Neural Networks","score":0.9994999766349792,"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":0.9994999766349792,"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/T10764","display_name":"Privacy-Preserving Technologies in Data","score":0.9945999979972839,"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/T12026","display_name":"Explainable Artificial Intelligence (XAI)","score":0.9918000102043152,"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":0.6890398263931274},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.46383461356163025},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.4223196506500244},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.3882408142089844},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.295635461807251}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6890398263931274},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.46383461356163025},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.4223196506500244},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.3882408142089844},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.295635461807251}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3637528.3671826","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3637528.3671826","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":30,"referenced_works":["https://openalex.org/W1975982181","https://openalex.org/W2144487656","https://openalex.org/W2187089797","https://openalex.org/W2800788706","https://openalex.org/W2885305518","https://openalex.org/W2915520221","https://openalex.org/W2964060106","https://openalex.org/W2964068143","https://openalex.org/W2966133050","https://openalex.org/W3048692209","https://openalex.org/W3103409210","https://openalex.org/W3110901318","https://openalex.org/W3117178429","https://openalex.org/W3134509497","https://openalex.org/W3158511434","https://openalex.org/W3171764584","https://openalex.org/W3172133997","https://openalex.org/W3185350140","https://openalex.org/W3192448376","https://openalex.org/W3207949751","https://openalex.org/W4212774754","https://openalex.org/W4213199213","https://openalex.org/W4290878493","https://openalex.org/W4290948450","https://openalex.org/W4318822267","https://openalex.org/W4320476363","https://openalex.org/W4362714312","https://openalex.org/W4365393360","https://openalex.org/W4384111736","https://openalex.org/W4386072263"],"related_works":["https://openalex.org/W4391375266","https://openalex.org/W2748952813","https://openalex.org/W2390279801","https://openalex.org/W2358668433","https://openalex.org/W4396701345","https://openalex.org/W2376932109","https://openalex.org/W2001405890","https://openalex.org/W4396696052","https://openalex.org/W2382290278","https://openalex.org/W4395014643"],"abstract_inverted_index":{"Driven":[0],"by":[1,141,175,186],"the":[2,48,89,109,137,168,180,187,207],"powerful":[3],"representation":[4],"ability":[5],"of":[6,42,50,99,139,147,183,209],"Graph":[7],"Neural":[8,131],"Networks":[9],"(GNNs),":[10],"plentiful":[11],"GNN":[12,84],"models":[13],"have":[14,117],"been":[15],"widely":[16],"deployed":[17],"in":[18,32,102],"many":[19],"real-world":[20],"applications.":[21],"Nevertheless,":[22],"due":[23],"to":[24,46,108,170,213],"distribution":[25],"disparities":[26],"between":[27],"different":[28,92],"demographic":[29,93],"groups,":[30],"fairness":[31,49,243],"high-stake":[33],"decision-making":[34],"systems":[35],"is":[36,95,246],"receiving":[37],"increasing":[38],"attention.":[39],"Although":[40],"lots":[41],"recent":[43],"works":[44],"devoted":[45],"improving":[47],"GNNs":[51,140],"and":[52,153,164,166,190,242],"achieved":[53],"considerable":[54],"success,":[55],"they":[56,220],"all":[57],"require":[58],"significant":[59,97],"architectural":[60],"changes":[61],"or":[62,80],"additional":[63],"loss":[64],"functions":[65],"requiring":[66],"more":[67],"hyper-parameter":[68],"tuning.":[69],"Surprisingly,":[70],"we":[71,126,158,177,205],"find":[72],"that":[73,88,192,232],"simple":[74,114],"re-balancing":[75,115],"methods":[76,116],"can":[77,178,195,221,235],"easily":[78],"match":[79],"surpass":[81],"existing":[82],"fair":[83],"methods.":[85],"We":[86],"claim":[87],"imbalance":[90],"across":[91,162],"groups":[94],"a":[96],"source":[98],"unfairness,":[100],"resulting":[101],"imbalanced":[103],"contributions":[104],"from":[105,201],"each":[106,210,224],"group":[107,142,211],"parameters":[110],"updating.":[111],"However,":[112],"these":[113,217],"their":[118],"own":[119],"shortcomings":[120],"during":[121],"training.":[122],"In":[123],"this":[124],"paper,":[125],"propose":[127],"FairGB,":[128],"<u>Fair</u>":[129],"<u>G</u>raph":[130],"Network":[132],"via":[133],"re-<u>B</u>alancing,":[134],"which":[135],"mitigates":[136],"unfairness":[138],"balancing.":[143],"Technically,":[144],"FairGB":[145],"consists":[146],"two":[148,218],"modules:":[149],"counterfactual":[150,160],"node":[151],"mixup":[152],"contribution":[154,208],"alignment":[155],"loss.":[156],"Firstly,":[157],"select":[159],"pairs":[161],"inter-domain":[163],"inter-class,":[165],"interpolate":[167],"ego-networks":[169],"generate":[171],"new":[172],"samples.":[173],"Guided":[174],"analysis,":[176],"reveal":[179],"debiasing":[181],"mechanism":[182],"our":[184,193,233],"model":[185],"causal":[188],"view":[189],"prove":[191],"strategy":[194],"make":[196],"sensitive":[197],"attributes":[198],"statistically":[199],"independent":[200],"target":[202],"labels.":[203],"Secondly,":[204],"reweigh":[206],"according":[212],"gradients.":[214],"By":[215],"combining":[216],"modules,":[219],"mutually":[222],"promote":[223],"other.":[225],"Experimental":[226],"results":[227,238],"on":[228],"benchmark":[229],"datasets":[230],"show":[231],"method":[234],"achieve":[236],"state-of-the-art":[237],"concerning":[239],"both":[240],"utility":[241],"metrics.":[244],"Code":[245],"available":[247],"at":[248],"https://github.com/ZhixunLEE/FairGB.":[249]},"counts_by_year":[{"year":2026,"cited_by_count":3},{"year":2025,"cited_by_count":9}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}