{"id":"https://openalex.org/W7138220985","doi":"https://doi.org/10.1609/aaai.v40i24.39108","title":"Generating In-Distribution Counterfactual Explanation for Graph Neural Networks","display_name":"Generating In-Distribution Counterfactual Explanation for Graph Neural Networks","publication_year":2026,"publication_date":"2026-03-14","ids":{"openalex":"https://openalex.org/W7138220985","doi":"https://doi.org/10.1609/aaai.v40i24.39108"},"language":"en","primary_location":{"id":"doi:10.1609/aaai.v40i24.39108","is_oa":true,"landing_page_url":"https://doi.org/10.1609/aaai.v40i24.39108","pdf_url":"https://ojs.aaai.org/index.php/AAAI/article/download/39108/43070","source":{"id":"https://openalex.org/S4210191458","display_name":"Proceedings of the AAAI Conference on Artificial Intelligence","issn_l":"2159-5399","issn":["2159-5399","2374-3468"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/P4310320058","host_organization_name":"Association for the Advancement of Artificial Intelligence","host_organization_lineage":["https://openalex.org/P4310320058"],"host_organization_lineage_names":["Association for the Advancement of Artificial Intelligence"],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the AAAI Conference on Artificial Intelligence","raw_type":"journal-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"diamond","oa_url":"https://ojs.aaai.org/index.php/AAAI/article/download/39108/43070","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5122630575","display_name":"Linmao Chen","orcid":null},"institutions":[{"id":"https://openalex.org/I187400657","display_name":"South China Normal University","ror":"https://ror.org/01kq0pv72","country_code":"CN","type":"education","lineage":["https://openalex.org/I187400657"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Linmao Chen","raw_affiliation_strings":["South China Normal University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"South China Normal University","institution_ids":["https://openalex.org/I187400657"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5026139698","display_name":"Chaobo He","orcid":"https://orcid.org/0000-0002-6651-1175"},"institutions":[{"id":"https://openalex.org/I187400657","display_name":"South China Normal University","ror":"https://ror.org/01kq0pv72","country_code":"CN","type":"education","lineage":["https://openalex.org/I187400657"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Chaobo He","raw_affiliation_strings":["South China Normal University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"South China Normal University","institution_ids":["https://openalex.org/I187400657"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5121761617","display_name":"Junwei Cheng","orcid":null},"institutions":[{"id":"https://openalex.org/I187400657","display_name":"South China Normal University","ror":"https://ror.org/01kq0pv72","country_code":"CN","type":"education","lineage":["https://openalex.org/I187400657"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Junwei Cheng","raw_affiliation_strings":["South China Normal University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"South China Normal University","institution_ids":["https://openalex.org/I187400657"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100646074","display_name":"Chunying Li","orcid":"https://orcid.org/0000-0001-5190-506X"},"institutions":[{"id":"https://openalex.org/I4210122543","display_name":"Guangdong Polytechnic Normal University","ror":"https://ror.org/02pcb5m77","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210122543"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Chunying Li","raw_affiliation_strings":["Guangdong Polytechnic Normal University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Guangdong Polytechnic Normal University","institution_ids":["https://openalex.org/I4210122543"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5129667082","display_name":"Quanlong Guan","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Quanlong Guan","raw_affiliation_strings":["Jinan University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Jinan University","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"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":"40","issue":"24","first_page":"20208","last_page":"20216"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11273","display_name":"Advanced Graph Neural Networks","score":0.5047000050544739,"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":0.5047000050544739,"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":0.40059998631477356,"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/T13702","display_name":"Machine Learning in Healthcare","score":0.023499999195337296,"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/counterfactual-thinking","display_name":"Counterfactual thinking","score":0.9569000005722046},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.5688999891281128},{"id":"https://openalex.org/keywords/generative-grammar","display_name":"Generative grammar","score":0.5573999881744385},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5098999738693237},{"id":"https://openalex.org/keywords/focus","display_name":"Focus (optics)","score":0.4429999887943268},{"id":"https://openalex.org/keywords/data-driven","display_name":"Data-driven","score":0.3725000023841858},{"id":"https://openalex.org/keywords/generative-model","display_name":"Generative model","score":0.3723999857902527}],"concepts":[{"id":"https://openalex.org/C108650721","wikidata":"https://www.wikidata.org/wiki/Q1783253","display_name":"Counterfactual thinking","level":2,"score":0.9569000005722046},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.626800000667572},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.5688999891281128},{"id":"https://openalex.org/C39890363","wikidata":"https://www.wikidata.org/wiki/Q36108","display_name":"Generative grammar","level":2,"score":0.5573999881744385},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5288000106811523},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5241000056266785},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5098999738693237},{"id":"https://openalex.org/C192209626","wikidata":"https://www.wikidata.org/wiki/Q190909","display_name":"Focus (optics)","level":2,"score":0.4429999887943268},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.4196999967098236},{"id":"https://openalex.org/C2780440489","wikidata":"https://www.wikidata.org/wiki/Q5227278","display_name":"Data-driven","level":2,"score":0.3725000023841858},{"id":"https://openalex.org/C167966045","wikidata":"https://www.wikidata.org/wiki/Q5532625","display_name":"Generative model","level":3,"score":0.3723999857902527},{"id":"https://openalex.org/C2984842247","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep neural networks","level":3,"score":0.36000001430511475},{"id":"https://openalex.org/C110121322","wikidata":"https://www.wikidata.org/wiki/Q865811","display_name":"Distribution (mathematics)","level":2,"score":0.32899999618530273},{"id":"https://openalex.org/C88230418","wikidata":"https://www.wikidata.org/wiki/Q131476","display_name":"Graph theory","level":2,"score":0.325300008058548},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3158000111579895},{"id":"https://openalex.org/C71889745","wikidata":"https://www.wikidata.org/wiki/Q1783264","display_name":"Counterfactual conditional","level":3,"score":0.3125999867916107},{"id":"https://openalex.org/C160920958","wikidata":"https://www.wikidata.org/wiki/Q7662746","display_name":"Synthetic data","level":2,"score":0.30250000953674316},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.28200000524520874},{"id":"https://openalex.org/C149441793","wikidata":"https://www.wikidata.org/wiki/Q200726","display_name":"Probability distribution","level":2,"score":0.27250000834465027},{"id":"https://openalex.org/C18653775","wikidata":"https://www.wikidata.org/wiki/Q1333358","display_name":"Joint probability distribution","level":2,"score":0.2556000053882599},{"id":"https://openalex.org/C147764199","wikidata":"https://www.wikidata.org/wiki/Q6865248","display_name":"Minification","level":2,"score":0.25529998540878296}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1609/aaai.v40i24.39108","is_oa":true,"landing_page_url":"https://doi.org/10.1609/aaai.v40i24.39108","pdf_url":"https://ojs.aaai.org/index.php/AAAI/article/download/39108/43070","source":{"id":"https://openalex.org/S4210191458","display_name":"Proceedings of the AAAI Conference on Artificial Intelligence","issn_l":"2159-5399","issn":["2159-5399","2374-3468"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/P4310320058","host_organization_name":"Association for the Advancement of Artificial Intelligence","host_organization_lineage":["https://openalex.org/P4310320058"],"host_organization_lineage_names":["Association for the Advancement of Artificial Intelligence"],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the AAAI Conference on Artificial Intelligence","raw_type":"journal-article"},{"id":"pmh:oai:ojs.aaai.org:article/39108","is_oa":false,"landing_page_url":"https://ojs.aaai.org/index.php/AAAI/article/view/39108","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"2159-5399","raw_type":"info:eu-repo/semantics/article"}],"best_oa_location":{"id":"doi:10.1609/aaai.v40i24.39108","is_oa":true,"landing_page_url":"https://doi.org/10.1609/aaai.v40i24.39108","pdf_url":"https://ojs.aaai.org/index.php/AAAI/article/download/39108/43070","source":{"id":"https://openalex.org/S4210191458","display_name":"Proceedings of the AAAI Conference on Artificial Intelligence","issn_l":"2159-5399","issn":["2159-5399","2374-3468"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/P4310320058","host_organization_name":"Association for the Advancement of Artificial Intelligence","host_organization_lineage":["https://openalex.org/P4310320058"],"host_organization_lineage_names":["Association for the Advancement of Artificial Intelligence"],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the AAAI Conference on Artificial Intelligence","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G2356398109","display_name":null,"funder_award_id":"21625102","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G3776946349","display_name":null,"funder_award_id":"62077045","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G8062238156","display_name":null,"funder_award_id":"2023B1515120064","funder_id":"https://openalex.org/F4320337111","funder_display_name":"Basic and Applied Basic Research Foundation of Guangdong Province"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320335787","display_name":"Fundamental Research Funds for the Central Universities","ror":null},{"id":"https://openalex.org/F4320337111","display_name":"Basic and Applied Basic Research Foundation of Guangdong Province","ror":null}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W7138220985.pdf","grobid_xml":"https://content.openalex.org/works/W7138220985.grobid-xml"},"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Graph":[0],"Neural":[1],"Networks":[2],"(GNNs)":[3],"have":[4],"received":[5],"increasing":[6],"attention":[7],"due":[8],"to":[9,12,27,37,46,78,130,139,155],"their":[10,17],"ability":[11],"handle":[13],"graph-structured":[14],"data,":[15],"yet":[16],"explainability":[18],"remains":[19],"a":[20,89],"significant":[21],"challenge.":[22],"An":[23],"effective":[24],"solution":[25],"is":[26,149],"provide":[28],"the":[29,41,48,68,73,79,98,109,132,136,157,180],"GNN":[30],"models":[31],"with":[32,72],"counterfactual":[33,110,166],"explanations,":[34],"which":[35],"aim":[36],"answer":[38],"\u201cHow":[39],"should":[40],"input":[42,137],"instance":[43],"be":[44],"perturbed":[45],"change":[47],"model's":[49],"prediction?\".":[50],"However,":[51],"existing":[52,186],"works":[53],"mainly":[54],"focus":[55],"on":[56,173],"generating":[57,95],"explanations":[58,69,96,163],"that":[59,161],"can":[60],"effectively":[61],"alter":[62],"model":[63,107],"predictions,":[64],"while":[65],"neglecting":[66],"whether":[67],"remain":[70],"aligned":[71],"original":[74,99],"data":[75],"distribution,":[76],"leading":[77],"distribution":[80,120,134,148],"shift":[81],"problem.":[82],"To":[83],"address":[84],"this":[85],"problem,":[86],"we":[87,102,127],"propose":[88],"novel":[90],"method":[91],"called":[92],"ICExplainer":[93,184],"for":[94,118],"within":[97],"distribution.":[100],"Specifically,":[101],"introduce":[103],"graph":[104,119],"diffusion-based":[105],"generative":[106],"into":[108],"reasoning,":[111],"treating":[112],"it":[113,129],"as":[114,152],"an":[115],"optimization":[116],"objective":[117],"learning.":[121],"Taking":[122],"insights":[123],"from":[124],"variational":[125],"inference,":[126],"use":[128],"estimate":[131],"true":[133],"of":[135,183],"graphs":[138],"retain":[140],"essential":[141],"structural":[142],"and":[143,167,176],"semantic":[144],"information.":[145],"The":[146],"inferred":[147],"then":[150],"utilized":[151],"prior":[153],"knowledge":[154],"guide":[156],"reverse":[158],"process,":[159],"ensuring":[160],"generated":[162],"are":[164],"both":[165,174],"distributionally":[168],"coherent.":[169],"Extensive":[170],"experiments":[171],"conducted":[172],"synthetic":[175],"real-world":[177],"datasets":[178],"demonstrate":[179],"superior":[181],"performance":[182],"over":[185],"methods.":[187]},"counts_by_year":[],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2026-03-18T00:00:00"}