{"id":"https://openalex.org/W3192448376","doi":"https://doi.org/10.1145/3485447.3512173","title":"EDITS: Modeling and Mitigating Data Bias for Graph Neural Networks","display_name":"EDITS: Modeling and Mitigating Data Bias for Graph Neural Networks","publication_year":2022,"publication_date":"2022-04-25","ids":{"openalex":"https://openalex.org/W3192448376","doi":"https://doi.org/10.1145/3485447.3512173","mag":"3192448376"},"language":"en","primary_location":{"id":"doi:10.1145/3485447.3512173","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3485447.3512173","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3485447.3512173","source":{"id":"https://openalex.org/S4363608783","display_name":"Proceedings of the ACM Web Conference 2022","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the ACM Web Conference 2022","raw_type":"proceedings-article"},"type":"article","indexed_in":["arxiv","crossref"],"open_access":{"is_oa":true,"oa_status":"bronze","oa_url":"https://dl.acm.org/doi/pdf/10.1145/3485447.3512173","any_repository_has_fulltext":true},"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/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":true,"raw_author_name":"Yushun Dong","raw_affiliation_strings":["University of Virginia, USA"],"affiliations":[{"raw_affiliation_string":"University of Virginia, USA","institution_ids":["https://openalex.org/I51556381"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5007489034","display_name":"Ninghao Liu","orcid":"https://orcid.org/0000-0002-9170-2424"},"institutions":[{"id":"https://openalex.org/I165733156","display_name":"University of Georgia","ror":"https://ror.org/00te3t702","country_code":"US","type":"education","lineage":["https://openalex.org/I165733156"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Ninghao Liu","raw_affiliation_strings":["University of Georgia, USA"],"affiliations":[{"raw_affiliation_string":"University of Georgia, USA","institution_ids":["https://openalex.org/I165733156"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5084773390","display_name":"Brian Jalaian","orcid":"https://orcid.org/0000-0003-3029-601X"},"institutions":[{"id":"https://openalex.org/I166416128","display_name":"DEVCOM Army Research Laboratory","ror":"https://ror.org/011hc8f90","country_code":"US","type":"government","lineage":["https://openalex.org/I1304082316","https://openalex.org/I1330347796","https://openalex.org/I166416128","https://openalex.org/I2802705668","https://openalex.org/I4210154437"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Brian Jalaian","raw_affiliation_strings":["U.S. Army Research Laboratory, USA"],"affiliations":[{"raw_affiliation_string":"U.S. Army Research Laboratory, USA","institution_ids":["https://openalex.org/I166416128"]}]},{"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":["University of Virginia, USA"],"affiliations":[{"raw_affiliation_string":"University of Virginia, USA","institution_ids":["https://openalex.org/I51556381"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5047581320"],"corresponding_institution_ids":["https://openalex.org/I51556381"],"apc_list":null,"apc_paid":null,"fwci":11.8539,"has_fulltext":true,"cited_by_count":117,"citation_normalized_percentile":{"value":0.99109604,"is_in_top_1_percent":true,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":93,"max":100},"biblio":{"volume":null,"issue":null,"first_page":"1259","last_page":"1269"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11273","display_name":"Advanced Graph Neural Networks","score":0.9976000189781189,"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":0.9976000189781189,"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":0.9972000122070312,"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":0.9918000102043152,"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":0.8524953126907349},{"id":"https://openalex.org/keywords/debiasing","display_name":"Debiasing","score":0.6922548413276672},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.4743857681751251},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.45423761010169983},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.40397965908050537},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3430229425430298},{"id":"https://openalex.org/keywords/data-science","display_name":"Data science","score":0.33771955966949463},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.14697611331939697}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8524953126907349},{"id":"https://openalex.org/C2779458634","wikidata":"https://www.wikidata.org/wiki/Q24963715","display_name":"Debiasing","level":2,"score":0.6922548413276672},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4743857681751251},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.45423761010169983},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.40397965908050537},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3430229425430298},{"id":"https://openalex.org/C2522767166","wikidata":"https://www.wikidata.org/wiki/Q2374463","display_name":"Data science","level":1,"score":0.33771955966949463},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.14697611331939697},{"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}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1145/3485447.3512173","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3485447.3512173","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3485447.3512173","source":{"id":"https://openalex.org/S4363608783","display_name":"Proceedings of the ACM Web Conference 2022","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the ACM Web Conference 2022","raw_type":"proceedings-article"},{"id":"pmh:oai:arXiv.org:2108.05233","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2108.05233","pdf_url":"https://arxiv.org/pdf/2108.05233","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"}],"best_oa_location":{"id":"doi:10.1145/3485447.3512173","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3485447.3512173","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3485447.3512173","source":{"id":"https://openalex.org/S4363608783","display_name":"Proceedings of the ACM Web Conference 2022","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the ACM Web Conference 2022","raw_type":"proceedings-article"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/10","score":0.5299999713897705,"display_name":"Reduced inequalities"},{"id":"https://metadata.un.org/sdg/16","score":0.4099999964237213,"display_name":"Peace, Justice and strong institutions"}],"awards":[{"id":"https://openalex.org/G7378744750","display_name":null,"funder_award_id":"2006844","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/F4320307791","display_name":"Cisco Systems","ror":"https://ror.org/03yt1ez60"}],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W3192448376.pdf","grobid_xml":"https://content.openalex.org/works/W3192448376.grobid-xml"},"referenced_works_count":88,"referenced_works":["https://openalex.org/W385466589","https://openalex.org/W615621364","https://openalex.org/W1522301498","https://openalex.org/W1578099820","https://openalex.org/W1585160083","https://openalex.org/W1956343362","https://openalex.org/W2020141429","https://openalex.org/W2046253692","https://openalex.org/W2100960835","https://openalex.org/W2116666691","https://openalex.org/W2116984840","https://openalex.org/W2150256170","https://openalex.org/W2155461593","https://openalex.org/W2162670686","https://openalex.org/W2181900708","https://openalex.org/W2519887557","https://openalex.org/W2530395818","https://openalex.org/W2554952599","https://openalex.org/W2607500032","https://openalex.org/W2624431344","https://openalex.org/W2725155646","https://openalex.org/W2741179140","https://openalex.org/W2751465153","https://openalex.org/W2753845591","https://openalex.org/W2766453196","https://openalex.org/W2768075475","https://openalex.org/W2888161220","https://openalex.org/W2890976914","https://openalex.org/W2894175828","https://openalex.org/W2899771611","https://openalex.org/W2920058944","https://openalex.org/W2945848398","https://openalex.org/W2945903605","https://openalex.org/W2946133803","https://openalex.org/W2949818472","https://openalex.org/W2951576127","https://openalex.org/W2951934011","https://openalex.org/W2953314936","https://openalex.org/W2953418083","https://openalex.org/W2954709318","https://openalex.org/W2962711740","https://openalex.org/W2962750142","https://openalex.org/W2962767366","https://openalex.org/W2962787423","https://openalex.org/W2963327716","https://openalex.org/W2963392941","https://openalex.org/W2963396480","https://openalex.org/W2963757395","https://openalex.org/W2963798744","https://openalex.org/W2964015378","https://openalex.org/W2964121744","https://openalex.org/W2965548693","https://openalex.org/W2966133050","https://openalex.org/W2976404235","https://openalex.org/W2987574774","https://openalex.org/W2997837569","https://openalex.org/W3001580599","https://openalex.org/W3012996519","https://openalex.org/W3014590323","https://openalex.org/W3035447285","https://openalex.org/W3037567775","https://openalex.org/W3068123808","https://openalex.org/W3080365325","https://openalex.org/W3081203761","https://openalex.org/W3082642400","https://openalex.org/W3097736165","https://openalex.org/W3102969158","https://openalex.org/W3103409210","https://openalex.org/W3107309862","https://openalex.org/W3117178429","https://openalex.org/W3120559998","https://openalex.org/W3125564681","https://openalex.org/W3128736136","https://openalex.org/W3132822009","https://openalex.org/W3133595540","https://openalex.org/W3133596390","https://openalex.org/W3153858161","https://openalex.org/W3177385106","https://openalex.org/W3181414820","https://openalex.org/W4243567347","https://openalex.org/W4285790115","https://openalex.org/W4287323365","https://openalex.org/W4288057716","https://openalex.org/W4294558607","https://openalex.org/W4297733535","https://openalex.org/W4297795193","https://openalex.org/W4322614756","https://openalex.org/W4394670483"],"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":{"Graph":[0],"Neural":[1],"Networks":[2],"(GNNs)":[3],"have":[4,69],"shown":[5],"superior":[6],"performance":[7,156],"in":[8,12,24,127,150,159,164],"analyzing":[9],"attributed":[10,103,129,151],"networks":[11,152],"various":[13],"web-based":[14],"applications":[15],"such":[16,28],"as":[17,29],"social":[18],"recommendation":[19],"and":[20,75,121,185,194],"web":[21],"search.":[22],"Nevertheless,":[23],"high-stake":[25],"decision-making":[26],"scenarios":[27],"online":[30],"fraud":[31],"detection,":[32],"there":[33],"is":[34,77,170],"an":[35,128],"increasing":[36],"societal":[37],"concern":[38],"that":[39,93],"GNNs":[40,108,111,158],"could":[41],"make":[42],"discriminatory":[43],"decisions":[44],"towards":[45],"certain":[46],"demographic":[47],"groups.":[48],"Despite":[49],"recent":[50],"explorations":[51],"on":[52,190],"fair":[53],"GNNs,":[54],"these":[55],"works":[56,92,163],"are":[57],"tailored":[58],"for":[59,72,84],"a":[60,143,165],"specific":[61,86,174],"GNN":[62,67,87,95],"model.":[63],"However,":[64],"myriads":[65],"of":[66,157,172,180,188],"variants":[68],"been":[70],"proposed":[71,182],"different":[73],"applications,":[74],"it":[76,169],"costly":[78],"to":[79,99,105,123,133,137,146],"fine-tune":[80],"existing":[81,91],"debiasing":[82],"algorithms":[83],"each":[85],"architecture.":[88],"Different":[89],"from":[90],"debias":[94,100],"models,":[96],"we":[97,117],"aim":[98],"the":[101,125,134,148,155,178,181,186],"input":[102],"network":[104],"achieve":[106],"fairer":[107],"through":[109],"feeding":[110],"with":[112],"less":[113],"biased":[114],"data.":[115],"Specifically,":[116],"propose":[118],"novel":[119],"definitions":[120],"metrics":[122,184],"measure":[124],"bias":[126,149,183,192],"network,":[130],"which":[131],"leads":[132],"optimization":[135],"objective":[136],"mitigate":[138,147],"bias.":[139],"We":[140],"then":[141],"develop":[142],"framework":[144],"EDITS":[145,162,189],"while":[153],"maintaining":[154],"downstream":[160],"tasks.":[161],"model-agnostic":[166],"manner,":[167],"i.e.,":[168],"independent":[171],"any":[173],"GNN.":[175],"Experiments":[176],"demonstrate":[177],"validity":[179],"superiority":[187],"both":[191],"mitigation":[193],"utility":[195],"maintenance.":[196],"Open-source":[197],"implementation:":[198],"https://github.com/yushundong/EDITS.":[199]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2025,"cited_by_count":34},{"year":2024,"cited_by_count":39},{"year":2023,"cited_by_count":31},{"year":2022,"cited_by_count":9},{"year":2021,"cited_by_count":2}],"updated_date":"2026-03-13T16:22:10.518609","created_date":"2025-10-10T00:00:00"}
