{"id":"https://openalex.org/W4386096518","doi":"https://doi.org/10.1145/3616542","title":"Faithful and Consistent Graph Neural Network Explanations with Rationale Alignment","display_name":"Faithful and Consistent Graph Neural Network Explanations with Rationale Alignment","publication_year":2023,"publication_date":"2023-08-23","ids":{"openalex":"https://openalex.org/W4386096518","doi":"https://doi.org/10.1145/3616542"},"language":"en","primary_location":{"id":"doi:10.1145/3616542","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3616542","pdf_url":null,"source":{"id":"https://openalex.org/S2492086750","display_name":"ACM Transactions on Intelligent Systems and Technology","issn_l":"2157-6904","issn":["2157-6904","2157-6912"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319798","host_organization_name":"Association for Computing Machinery","host_organization_lineage":["https://openalex.org/P4310319798"],"host_organization_lineage_names":["Association for Computing Machinery"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ACM Transactions on Intelligent Systems and Technology","raw_type":"journal-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/A5053042660","display_name":"Tianxiang Zhao","orcid":"https://orcid.org/0000-0003-4504-7809"},"institutions":[{"id":"https://openalex.org/I130769515","display_name":"Pennsylvania State University","ror":"https://ror.org/04p491231","country_code":"US","type":"education","lineage":["https://openalex.org/I130769515"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Tianxiang Zhao","raw_affiliation_strings":["The Pennsylvania State University, USA"],"raw_orcid":"https://orcid.org/0000-0003-4504-7809","affiliations":[{"raw_affiliation_string":"The Pennsylvania State University, USA","institution_ids":["https://openalex.org/I130769515"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5102761357","display_name":"Dongsheng Luo","orcid":"https://orcid.org/0000-0003-4192-0826"},"institutions":[{"id":"https://openalex.org/I19700959","display_name":"Florida International University","ror":"https://ror.org/02gz6gg07","country_code":"US","type":"education","lineage":["https://openalex.org/I19700959"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Dongsheng Luo","raw_affiliation_strings":["Florida International University, USA"],"raw_orcid":"https://orcid.org/0000-0003-4192-0826","affiliations":[{"raw_affiliation_string":"Florida International University, USA","institution_ids":["https://openalex.org/I19700959"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5060725887","display_name":"X. D. Zhang","orcid":"https://orcid.org/0000-0003-0940-6595"},"institutions":[{"id":"https://openalex.org/I130769515","display_name":"Pennsylvania State University","ror":"https://ror.org/04p491231","country_code":"US","type":"education","lineage":["https://openalex.org/I130769515"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Xiang Zhang","raw_affiliation_strings":["The Pennsylvania State University, USA"],"raw_orcid":"https://orcid.org/0000-0003-0940-6595","affiliations":[{"raw_affiliation_string":"The Pennsylvania State University, USA","institution_ids":["https://openalex.org/I130769515"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5011048500","display_name":"Suhang Wang","orcid":"https://orcid.org/0000-0003-3448-4878"},"institutions":[{"id":"https://openalex.org/I130769515","display_name":"Pennsylvania State University","ror":"https://ror.org/04p491231","country_code":"US","type":"education","lineage":["https://openalex.org/I130769515"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Suhang Wang","raw_affiliation_strings":["The Pennsylvania State University, USA"],"raw_orcid":"https://orcid.org/0000-0003-3448-4878","affiliations":[{"raw_affiliation_string":"The Pennsylvania State University, USA","institution_ids":["https://openalex.org/I130769515"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.9123,"has_fulltext":false,"cited_by_count":7,"citation_normalized_percentile":{"value":0.79099399,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":98},"biblio":{"volume":"14","issue":"5","first_page":"1","last_page":"23"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11273","display_name":"Advanced Graph Neural Networks","score":0.9991999864578247,"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":0.9991999864578247,"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/T11303","display_name":"Bayesian Modeling and Causal Inference","score":0.9979000091552734,"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":0.9975000023841858,"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/spurious-relationship","display_name":"Spurious relationship","score":0.8538756370544434},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7920678853988647},{"id":"https://openalex.org/keywords/consistency","display_name":"Consistency (knowledge bases)","score":0.5160478949546814},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.48683398962020874},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.46208828687667847},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.461883008480072},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.4547847509384155},{"id":"https://openalex.org/keywords/causality","display_name":"Causality (physics)","score":0.441888689994812},{"id":"https://openalex.org/keywords/perspective","display_name":"Perspective (graphical)","score":0.4392521381378174},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.42427968978881836},{"id":"https://openalex.org/keywords/gaussian","display_name":"Gaussian","score":0.4124220013618469}],"concepts":[{"id":"https://openalex.org/C97256817","wikidata":"https://www.wikidata.org/wiki/Q1462316","display_name":"Spurious relationship","level":2,"score":0.8538756370544434},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7920678853988647},{"id":"https://openalex.org/C2776436953","wikidata":"https://www.wikidata.org/wiki/Q5163215","display_name":"Consistency (knowledge bases)","level":2,"score":0.5160478949546814},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.48683398962020874},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.46208828687667847},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.461883008480072},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.4547847509384155},{"id":"https://openalex.org/C64357122","wikidata":"https://www.wikidata.org/wiki/Q1149766","display_name":"Causality (physics)","level":2,"score":0.441888689994812},{"id":"https://openalex.org/C12713177","wikidata":"https://www.wikidata.org/wiki/Q1900281","display_name":"Perspective (graphical)","level":2,"score":0.4392521381378174},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.42427968978881836},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.4124220013618469},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3616542","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3616542","pdf_url":null,"source":{"id":"https://openalex.org/S2492086750","display_name":"ACM Transactions on Intelligent Systems and Technology","issn_l":"2157-6904","issn":["2157-6904","2157-6912"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319798","host_organization_name":"Association for Computing Machinery","host_organization_lineage":["https://openalex.org/P4310319798"],"host_organization_lineage_names":["Association for Computing Machinery"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ACM Transactions on Intelligent Systems and Technology","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G125128811","display_name":null,"funder_award_id":"W911NF-2110198","funder_id":"https://openalex.org/F4320338281","funder_display_name":"Army Research Office"},{"id":"https://openalex.org/G7366345995","display_name":"III: Small: Collaborative Research: Effective Labeled Data Generation via Generative Adversarial Learning","funder_award_id":"1909702","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G7405368409","display_name":"CAREER: Novel Approaches for Mining Large and Complex Networks","funder_award_id":"1707548","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/F4320338281","display_name":"Army Research Office","ror":"https://ror.org/05epdh915"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":91,"referenced_works":["https://openalex.org/W1538021842","https://openalex.org/W1662382123","https://openalex.org/W2165190832","https://openalex.org/W2519887557","https://openalex.org/W2615497679","https://openalex.org/W2624431344","https://openalex.org/W2812869832","https://openalex.org/W2914721378","https://openalex.org/W2937967841","https://openalex.org/W2945848398","https://openalex.org/W2947873642","https://openalex.org/W2962711740","https://openalex.org/W2963757395","https://openalex.org/W2964015378","https://openalex.org/W2972317931","https://openalex.org/W2976758207","https://openalex.org/W2979481854","https://openalex.org/W2981806891","https://openalex.org/W3000120900","https://openalex.org/W3005552578","https://openalex.org/W3009381467","https://openalex.org/W3027758526","https://openalex.org/W3033039844","https://openalex.org/W3033100793","https://openalex.org/W3034371431","https://openalex.org/W3034693603","https://openalex.org/W3044311607","https://openalex.org/W3093206758","https://openalex.org/W3093551363","https://openalex.org/W3095602948","https://openalex.org/W3096655658","https://openalex.org/W3100324210","https://openalex.org/W3103717137","https://openalex.org/W3105259638","https://openalex.org/W3108823960","https://openalex.org/W3111440465","https://openalex.org/W3116637551","https://openalex.org/W3118062200","https://openalex.org/W3119169886","https://openalex.org/W3134509497","https://openalex.org/W3163899735","https://openalex.org/W3169325548","https://openalex.org/W3169350676","https://openalex.org/W3169827396","https://openalex.org/W3178057123","https://openalex.org/W3190214286","https://openalex.org/W3207379368","https://openalex.org/W3207981989","https://openalex.org/W3212161586","https://openalex.org/W3215430231","https://openalex.org/W3217045679","https://openalex.org/W4206776774","https://openalex.org/W4212890525","https://openalex.org/W4213245422","https://openalex.org/W4214900325","https://openalex.org/W4221146190","https://openalex.org/W4221149460","https://openalex.org/W4224298369","https://openalex.org/W4224307896","https://openalex.org/W4224983022","https://openalex.org/W4281488627","https://openalex.org/W4281713394","https://openalex.org/W4287642280","https://openalex.org/W4287812705","https://openalex.org/W4289699827","https://openalex.org/W4294558607","https://openalex.org/W4297733535","https://openalex.org/W4306317334","https://openalex.org/W4309258819","https://openalex.org/W4310980124","https://openalex.org/W4311833010","https://openalex.org/W4318812135","https://openalex.org/W4321460022","https://openalex.org/W4323073030","https://openalex.org/W4380303532","https://openalex.org/W4380715556","https://openalex.org/W4382317733","https://openalex.org/W6748856961","https://openalex.org/W6754929296","https://openalex.org/W6755573351","https://openalex.org/W6758918355","https://openalex.org/W6772452955","https://openalex.org/W6774263817","https://openalex.org/W6776700526","https://openalex.org/W6779940601","https://openalex.org/W6784203733","https://openalex.org/W6784694379","https://openalex.org/W6786048916","https://openalex.org/W6790734178","https://openalex.org/W6797926237","https://openalex.org/W6846243207"],"related_works":["https://openalex.org/W3113091479","https://openalex.org/W2162899405","https://openalex.org/W941090075","https://openalex.org/W2044987316","https://openalex.org/W3134374554","https://openalex.org/W2237480245","https://openalex.org/W2075065631","https://openalex.org/W2519167559","https://openalex.org/W4311248832","https://openalex.org/W4386113923"],"abstract_inverted_index":{"Uncovering":[0],"rationales":[1,157],"behind":[2],"predictions":[3,118],"of":[4,45,88,119,135,194,222,228,235],"graph":[5],"neural":[6],"networks":[7],"(GNNs)":[8],"has":[9],"received":[10],"increasing":[11],"attention":[12],"over":[13],"recent":[14],"years.":[15],"Instance-level":[16],"GNN":[17,33],"explanation":[18,167,184,229],"aims":[19],"to":[20,94,100,178,243],"discover":[21],"critical":[22],"input":[23],"elements,":[24],"such":[25],"as":[26,79],"nodes":[27],"or":[28,76],"edges,":[29],"that":[30,150],"the":[31,52,74,80,86,117,122,146,220,233,244],"target":[32],"relies":[34],"upon":[35],"for":[36,128,188],"making":[37],"predictions.":[38,59],"Though":[39],"various":[40],"algorithms":[41],"are":[42,131,158,197],"proposed,":[43],"most":[44],"them":[46,99],"formalize":[47],"this":[48,67,112,189,223],"task":[49],"by":[50],"searching":[51],"minimal":[53],"subgraph,":[54],"which":[55,174],"can":[56,71],"preserve":[57],"original":[58,81,147],"However,":[60],"an":[61,170],"inductive":[62],"bias":[63],"is":[64,175,216],"deep-rooted":[65],"in":[66,73,160,226],"framework:":[68],"Several":[69],"subgraphs":[70],"result":[72],"same":[75],"similar":[77],"outputs":[78],"graphs.":[82],"Consequently,":[83],"they":[84],"have":[85],"danger":[87],"providing":[89],"spurious":[90,129],"explanations":[91,130],"and":[92,141,154,210,231],"failing":[93],"provide":[95],"consistent":[96],"explanations.":[97],"Applying":[98],"explain":[101],"weakly":[102],"performed":[103],"GNNs":[104,120],"would":[105],"further":[106],"amplify":[107],"these":[108,236],"issues.":[109],"To":[110],"address":[111],"problem,":[113],"we":[114,163],"theoretically":[115,176],"examine":[116],"from":[121,145],"causality":[123],"perspective.":[124],"Two":[125],"typical":[126],"reasons":[127],"identified:":[132],"confounding":[133,152],"effect":[134],"latent":[136],"variables":[137],"like":[138],"distribution":[139],"shift":[140],"causal":[142,156],"factors":[143],"distinct":[144],"input.":[148],"Observing":[149],"both":[151,218],"effects":[153],"diverse":[155],"encoded":[159],"internal":[161],"representations,":[162],"propose":[164],"a":[165,181,192],"new":[166,224],"framework":[168,225],"with":[169],"auxiliary":[171],"alignment":[172,190,202],"loss,":[173,191],"proven":[177],"be":[179],"optimizing":[180],"more":[182],"faithful":[183],"objective":[185],"intrinsically.":[186],"Concretely":[187],"set":[193],"different":[195],"perspectives":[196],"explored:":[198],"anchor-based":[199],"alignment,":[200,209],"distributional":[201],"based":[203],"on":[204,219,232],"Gaussian":[205],"mixture":[206],"models,":[207],"mutual-information-based":[208],"so":[211],"on.":[212],"A":[213],"comprehensive":[214],"study":[215],"conducted":[217],"effectiveness":[221],"terms":[227],"faithfulness/consistency":[230],"advantages":[234],"variants.":[237],"For":[238],"our":[239],"codes,":[240],"please":[241],"refer":[242],"following":[245],"URL":[246],"link:":[247],"https://github.com/TianxiangZhao/GraphNNExplanation":[248]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":4},{"year":2024,"cited_by_count":2}],"updated_date":"2026-08-06T08:24:18.245995","created_date":"2025-10-10T00:00:00"}
