{"id":"https://openalex.org/W4387847128","doi":"https://doi.org/10.1145/3583780.3615176","title":"FairGraph: Automated Graph Debiasing with Gradient Matching","display_name":"FairGraph: Automated Graph Debiasing with Gradient Matching","publication_year":2023,"publication_date":"2023-10-21","ids":{"openalex":"https://openalex.org/W4387847128","doi":"https://doi.org/10.1145/3583780.3615176"},"language":"en","primary_location":{"id":"doi:10.1145/3583780.3615176","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3583780.3615176","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3583780.3615176","source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 32nd ACM International Conference on Information and Knowledge Management","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://dl.acm.org/doi/pdf/10.1145/3583780.3615176","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5024225991","display_name":"Yezi Liu","orcid":"https://orcid.org/0000-0003-0454-5238"},"institutions":[{"id":"https://openalex.org/I204250578","display_name":"University of California, Irvine","ror":"https://ror.org/04gyf1771","country_code":"US","type":"education","lineage":["https://openalex.org/I204250578"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Yezi Liu","raw_affiliation_strings":["University of California, Irvine, Irvine, CA, USA"],"affiliations":[{"raw_affiliation_string":"University of California, Irvine, Irvine, CA, USA","institution_ids":["https://openalex.org/I204250578"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5024225991"],"corresponding_institution_ids":["https://openalex.org/I204250578"],"apc_list":null,"apc_paid":null,"fwci":1.5711,"has_fulltext":true,"cited_by_count":9,"citation_normalized_percentile":{"value":0.86638647,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"4135","last_page":"4139"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11273","display_name":"Advanced Graph Neural Networks","score":0.9973000288009644,"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":0.9973000288009644,"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":0.9932000041007996,"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/T12026","display_name":"Explainable Artificial Intelligence (XAI)","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/debiasing","display_name":"Debiasing","score":0.8914240598678589},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7528045773506165},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.5243373513221741},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.5171534419059753},{"id":"https://openalex.org/keywords/retraining","display_name":"Retraining","score":0.5144432187080383},{"id":"https://openalex.org/keywords/ranging","display_name":"Ranging","score":0.45113712549209595},{"id":"https://openalex.org/keywords/matching","display_name":"Matching (statistics)","score":0.42581087350845337},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.38040733337402344},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.35257264971733093},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.12908530235290527}],"concepts":[{"id":"https://openalex.org/C2779458634","wikidata":"https://www.wikidata.org/wiki/Q24963715","display_name":"Debiasing","level":2,"score":0.8914240598678589},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7528045773506165},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.5243373513221741},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.5171534419059753},{"id":"https://openalex.org/C2778712577","wikidata":"https://www.wikidata.org/wiki/Q3505966","display_name":"Retraining","level":2,"score":0.5144432187080383},{"id":"https://openalex.org/C115051666","wikidata":"https://www.wikidata.org/wiki/Q6522493","display_name":"Ranging","level":2,"score":0.45113712549209595},{"id":"https://openalex.org/C165064840","wikidata":"https://www.wikidata.org/wiki/Q1321061","display_name":"Matching (statistics)","level":2,"score":0.42581087350845337},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.38040733337402344},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.35257264971733093},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.12908530235290527},{"id":"https://openalex.org/C155202549","wikidata":"https://www.wikidata.org/wiki/Q178803","display_name":"International trade","level":1,"score":0.0},{"id":"https://openalex.org/C188147891","wikidata":"https://www.wikidata.org/wiki/Q147638","display_name":"Cognitive science","level":1,"score":0.0},{"id":"https://openalex.org/C15744967","wikidata":"https://www.wikidata.org/wiki/Q9418","display_name":"Psychology","level":0,"score":0.0},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.0},{"id":"https://openalex.org/C144133560","wikidata":"https://www.wikidata.org/wiki/Q4830453","display_name":"Business","level":0,"score":0.0},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3583780.3615176","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3583780.3615176","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3583780.3615176","source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 32nd ACM International Conference on Information and Knowledge Management","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.1145/3583780.3615176","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3583780.3615176","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3583780.3615176","source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 32nd ACM International Conference on Information and Knowledge Management","raw_type":"proceedings-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":false},"content_urls":{"pdf":"https://content.openalex.org/works/W4387847128.pdf"},"referenced_works_count":14,"referenced_works":["https://openalex.org/W2100960835","https://openalex.org/W2807021761","https://openalex.org/W2901375380","https://openalex.org/W2907492528","https://openalex.org/W2966133050","https://openalex.org/W3080365325","https://openalex.org/W3100848837","https://openalex.org/W3117178429","https://openalex.org/W3152893301","https://openalex.org/W3171764584","https://openalex.org/W3177385106","https://openalex.org/W3181414820","https://openalex.org/W4206323856","https://openalex.org/W4282004119"],"related_works":["https://openalex.org/W4362554880","https://openalex.org/W4281684980","https://openalex.org/W4386875279","https://openalex.org/W2171721708","https://openalex.org/W4390963114","https://openalex.org/W3214527415","https://openalex.org/W4287887864","https://openalex.org/W1495104519","https://openalex.org/W4225584739","https://openalex.org/W2199432031"],"abstract_inverted_index":{"As":[0],"a":[1,48,81,112,158],"prevalence":[2],"data":[3],"structure":[4,60],"in":[5],"the":[6,22,55,58,73,86,107,139,145,150,174],"real":[7],"world,":[8],"graphs":[9,28,185],"have":[10],"found":[11],"extensive":[12,165],"applications":[13],"ranging":[14],"from":[15,47,106],"modeling":[16,49],"social":[17],"networks":[18,38],"to":[19,31,84,102,125,181],"molecules.":[20],"However,":[21],"existence":[23],"of":[24,57,88,149,176],"diverse":[25],"biases":[26,105],"within":[27],"gives":[29],"rise":[30],"unfair":[32],"representations":[33],"learned":[34],"by":[35,110,143],"graph":[36,92,109,140,152],"neural":[37],"(GNNs).":[39],"Addressing":[40],"this":[41,77,120,132],"issue":[42],"has":[43],"typically":[44],"been":[45],"approached":[46],"perspective,":[50],"which":[51,137],"not":[52],"only":[53],"compromises":[54],"integrity":[56],"model":[59,70],"but":[61],"also":[62],"entails":[63],"additional":[64],"effort":[65],"and":[66,161,178],"cost":[67],"for":[68,94],"retraining":[69],"parameters":[71],"when":[72],"architecture":[74],"changes.":[75],"In":[76],"study,":[78],"we":[79,123,134,172],"adopt":[80],"data-centric":[82],"standpoint":[83],"tackle":[85],"problem":[87,142],"fairness,":[89],"focusing":[90],"on":[91,119,168],"debiasing":[93,141],"Graph":[95],"Neural":[96],"Networks.":[97],"Our":[98],"specific":[99],"objective":[100],"is":[101],"eliminate":[103],"various":[104],"input":[108,151],"generating":[111],"fair":[113,121,184],"synthetic":[114],"graph.":[115],"By":[116],"training":[117,147],"GNNs":[118],"graph,":[122],"aim":[124],"achieve":[126],"an":[127,154],"optimal":[128],"accuracy-fairness":[129],"trade-off.":[130],"To":[131],"end,":[133],"propose":[135],"FairGraph,":[136],"approaches":[138],"mimicking":[144],"GNN":[146,191],"trajectory":[148],"through":[153],"optimization":[155],"process":[156],"involving":[157],"gradient-matching":[159],"loss":[160],"fairness":[162],"constraints.":[163],"Through":[164],"experiments":[166],"conducted":[167],"three":[169],"benchmark":[170],"datasets,":[171],"demonstrate":[173],"effectiveness":[175],"FairGraph":[177],"its":[179],"ability":[180],"automatedly":[182],"generate":[183],"that":[186],"are":[187],"transferable":[188],"across":[189],"different":[190],"architectures.":[192]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":6},{"year":2024,"cited_by_count":2}],"updated_date":"2025-12-21T01:58:51.020947","created_date":"2025-10-10T00:00:00"}
