{"id":"https://openalex.org/W4407246876","doi":"https://doi.org/10.1109/tkde.2025.3539989","title":"GAFExplainer: Global View Explanation of Graph Neural Networks Through Attribute Augmentation and Fusion Embedding","display_name":"GAFExplainer: Global View Explanation of Graph Neural Networks Through Attribute Augmentation and Fusion Embedding","publication_year":2025,"publication_date":"2025-02-07","ids":{"openalex":"https://openalex.org/W4407246876","doi":"https://doi.org/10.1109/tkde.2025.3539989"},"language":"en","primary_location":{"id":"doi:10.1109/tkde.2025.3539989","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tkde.2025.3539989","pdf_url":null,"source":{"id":"https://openalex.org/S30698027","display_name":"IEEE Transactions on Knowledge and Data Engineering","issn_l":"1041-4347","issn":["1041-4347","1558-2191","2326-3865"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320439","host_organization_name":"IEEE Computer Society","host_organization_lineage":["https://openalex.org/P4310320439","https://openalex.org/P4310319808"],"host_organization_lineage_names":["IEEE Computer Society","Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Knowledge and Data Engineering","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/A5005307932","display_name":"Wenya Hu","orcid":"https://orcid.org/0000-0002-5823-1819"},"institutions":[{"id":"https://openalex.org/I141962983","display_name":"Shanghai University of Engineering Science","ror":"https://ror.org/0557b9y08","country_code":"CN","type":"education","lineage":["https://openalex.org/I141962983"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wenya Hu","raw_affiliation_strings":["School of Computer Engineering and Science, Shanghai University, Shanghai, China"],"raw_orcid":"https://orcid.org/0000-0002-5823-1819","affiliations":[{"raw_affiliation_string":"School of Computer Engineering and Science, Shanghai University, Shanghai, China","institution_ids":["https://openalex.org/I141962983"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5007475662","display_name":"Jia Wu","orcid":"https://orcid.org/0000-0002-1371-5801"},"institutions":[{"id":"https://openalex.org/I99043593","display_name":"Macquarie University","ror":"https://ror.org/01sf06y89","country_code":"AU","type":"education","lineage":["https://openalex.org/I99043593"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Jia Wu","raw_affiliation_strings":["School of Computing, Macquarie University, Sydney, NSW, Australia"],"raw_orcid":"https://orcid.org/0000-0002-1371-5801","affiliations":[{"raw_affiliation_string":"School of Computing, Macquarie University, Sydney, NSW, Australia","institution_ids":["https://openalex.org/I99043593"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5084189341","display_name":"Quan Qian","orcid":"https://orcid.org/0000-0002-3020-005X"},"institutions":[{"id":"https://openalex.org/I141962983","display_name":"Shanghai University of Engineering Science","ror":"https://ror.org/0557b9y08","country_code":"CN","type":"education","lineage":["https://openalex.org/I141962983"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Quan Qian","raw_affiliation_strings":["School of Computer Engineering and Science, Shanghai University, Shanghai, China"],"raw_orcid":"https://orcid.org/0000-0002-3020-005X","affiliations":[{"raw_affiliation_string":"School of Computer Engineering and Science, Shanghai University, Shanghai, China","institution_ids":["https://openalex.org/I141962983"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":8.5976,"has_fulltext":false,"cited_by_count":7,"citation_normalized_percentile":{"value":0.97101676,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":95,"max":99},"biblio":{"volume":"37","issue":"5","first_page":"2569","last_page":"2583"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12026","display_name":"Explainable Artificial Intelligence (XAI)","score":0.9929999709129333,"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/T12026","display_name":"Explainable Artificial Intelligence (XAI)","score":0.9929999709129333,"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/T11273","display_name":"Advanced Graph Neural Networks","score":0.9772999882698059,"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/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.9768000245094299,"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":0.7603093385696411},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5153316855430603},{"id":"https://openalex.org/keywords/embedding","display_name":"Embedding","score":0.5058841109275818},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.48376011848449707},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.4597698450088501},{"id":"https://openalex.org/keywords/sensor-fusion","display_name":"Sensor fusion","score":0.4203590750694275},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.41646912693977356},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.34201568365097046},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3312942683696747}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7603093385696411},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5153316855430603},{"id":"https://openalex.org/C41608201","wikidata":"https://www.wikidata.org/wiki/Q980509","display_name":"Embedding","level":2,"score":0.5058841109275818},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.48376011848449707},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.4597698450088501},{"id":"https://openalex.org/C33954974","wikidata":"https://www.wikidata.org/wiki/Q486494","display_name":"Sensor fusion","level":2,"score":0.4203590750694275},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.41646912693977356},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.34201568365097046},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3312942683696747}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tkde.2025.3539989","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tkde.2025.3539989","pdf_url":null,"source":{"id":"https://openalex.org/S30698027","display_name":"IEEE Transactions on Knowledge and Data Engineering","issn_l":"1041-4347","issn":["1041-4347","1558-2191","2326-3865"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320439","host_organization_name":"IEEE Computer Society","host_organization_lineage":["https://openalex.org/P4310320439","https://openalex.org/P4310319808"],"host_organization_lineage_names":["IEEE Computer Society","Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Knowledge and Data Engineering","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G1282416069","display_name":"Deep Pattern Mining for Brain Graph Analysis: A Data Mining Perspective","funder_award_id":"LP210301259","funder_id":"https://openalex.org/F4320334704","funder_display_name":"Australian Research Council"},{"id":"https://openalex.org/G3877369778","display_name":"New Graph Mining Technologies to Enable Timely Exploration of Social Events","funder_award_id":"DP230100899","funder_id":"https://openalex.org/F4320334704","funder_display_name":"Australian Research Council"}],"funders":[{"id":"https://openalex.org/F4320320591","display_name":"Macquarie University","ror":"https://ror.org/01sf06y89"},{"id":"https://openalex.org/F4320334704","display_name":"Australian Research Council","ror":"https://ror.org/05mmh0f86"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":65,"referenced_works":["https://openalex.org/W2130354913","https://openalex.org/W2153624566","https://openalex.org/W2766447205","https://openalex.org/W2788919350","https://openalex.org/W2796096336","https://openalex.org/W2911286998","https://openalex.org/W2946327826","https://openalex.org/W2962858109","https://openalex.org/W2964051675","https://openalex.org/W2964811671","https://openalex.org/W2966567766","https://openalex.org/W2979481854","https://openalex.org/W2983721890","https://openalex.org/W2998008360","https://openalex.org/W2998496395","https://openalex.org/W3000120900","https://openalex.org/W3004366655","https://openalex.org/W3004507689","https://openalex.org/W3068123808","https://openalex.org/W3092083846","https://openalex.org/W3108823960","https://openalex.org/W3116637551","https://openalex.org/W3155886566","https://openalex.org/W3160381762","https://openalex.org/W3184370740","https://openalex.org/W3197032092","https://openalex.org/W4252973173","https://openalex.org/W4306317642","https://openalex.org/W4312887577","https://openalex.org/W4321485252","https://openalex.org/W4327813569","https://openalex.org/W4384643953","https://openalex.org/W4390647500","https://openalex.org/W4391582491","https://openalex.org/W4393160837","https://openalex.org/W4396757490","https://openalex.org/W6620673361","https://openalex.org/W6640963894","https://openalex.org/W6685350579","https://openalex.org/W6720006811","https://openalex.org/W6726873649","https://openalex.org/W6729448088","https://openalex.org/W6730091202","https://openalex.org/W6736685754","https://openalex.org/W6738549535","https://openalex.org/W6738964360","https://openalex.org/W6748856961","https://openalex.org/W6752110883","https://openalex.org/W6754929296","https://openalex.org/W6755863455","https://openalex.org/W6760001035","https://openalex.org/W6763140224","https://openalex.org/W6767098714","https://openalex.org/W6767288045","https://openalex.org/W6771621015","https://openalex.org/W6777775360","https://openalex.org/W6778095638","https://openalex.org/W6779273517","https://openalex.org/W6784106106","https://openalex.org/W6784653527","https://openalex.org/W6786048916","https://openalex.org/W6790580958","https://openalex.org/W6794534400","https://openalex.org/W6857478114","https://openalex.org/W6868776270"],"related_works":["https://openalex.org/W2081900870","https://openalex.org/W2037549926","https://openalex.org/W2345479200","https://openalex.org/W2183306018","https://openalex.org/W2849310602","https://openalex.org/W3006008237","https://openalex.org/W2419146053","https://openalex.org/W4388890789","https://openalex.org/W2088247287","https://openalex.org/W2932872266"],"abstract_inverted_index":{"The":[0,75],"excellent":[1],"performance":[2,183],"of":[3,34,91,113,134,185],"graph":[4,23],"neural":[5],"networks":[6],"(GNNs),":[7],"which":[8,35],"learn":[9],"node":[10,42,67,78,92],"representations":[11,93],"by":[12,159],"aggregating":[13],"their":[14,19,53],"neighborhood":[15],"information,":[16],"led":[17],"to":[18,38],"use":[20],"in":[21,168],"various":[22],"tasks.":[24],"However,":[25],"GNNs":[26],"are":[27,36,44,122,163],"black":[28],"box":[29],"models,":[30],"the":[31,86,89,106,129,144,153,156,160,172,193],"prediction":[32],"results":[33,136],"difficult":[37],"understand":[39],"directly.":[40],"Although":[41],"attributes":[43,68],"vital":[45],"for":[46,55,81,124],"making":[47],"predictions,":[48],"previous":[49],"studies":[50],"have":[51],"ignored":[52],"importance":[54],"explanation.":[56],"This":[57],"study":[58],"presents":[59],"GAFExplainer,":[60],"a":[61,109],"novel":[62],"GNN":[63,114],"explainer":[64],"that":[65,143,179],"emphasizes":[66],"via":[69],"attribute":[70,79],"augmentation":[71],"and":[72,118,139,149,175],"fusion":[73],"embedding.":[74],"former":[76],"enhances":[77],"encoding":[80],"more":[82,164],"expressive":[83],"masks,":[84],"while":[85,197],"latter":[87],"preserves":[88],"discrimination":[90],"across":[94],"different":[95],"layers.":[96],"Together,":[97],"these":[98],"modules":[99],"significantly":[100],"improve":[101],"explanation":[102,112],"performance.":[103],"By":[104],"training":[105],"explanatory":[107],"network,":[108],"global":[110],"view":[111],"models":[115],"is":[116],"obtained,":[117],"reasonably":[119],"explainable":[120],"subgraphs":[121],"available":[123],"new":[125],"graphs,":[126],"thus":[127],"rendering":[128],"model":[130,146,162],"well-generalizable.":[131],"Multiple":[132],"sets":[133],"experimental":[135],"on":[137],"real":[138],"synthetic":[140],"datasets":[141],"demonstrate":[142],"proposed":[145,161],"provides":[147],"valid":[148],"accurate":[150],"explanations.":[151],"In":[152],"visual":[154],"analysis,":[155],"explanations":[157],"obtained":[158],"comprehensible":[165],"than":[166],"those":[167],"existing":[169],"work.":[170],"Further,":[171],"fidelity":[173,195],"evaluation":[174],"efficiency":[176],"comparison":[177],"reveal":[178],"with":[180,188],"an":[181],"average":[182],"improvement":[184],"8.9$\\%":[186],"$compared":[187],"representative":[189],"baselines,":[190],"GAFExplainer":[191],"achieves":[192],"best":[194],"metrics":[196],"maintaining":[198],"computational":[199],"efficiency.":[200]},"counts_by_year":[{"year":2026,"cited_by_count":5},{"year":2025,"cited_by_count":2}],"updated_date":"2026-07-29T09:40:50.615796","created_date":"2025-10-10T00:00:00"}
