{"id":"https://openalex.org/W4416251943","doi":"https://doi.org/10.1109/ijcnn64981.2025.11227407","title":"Cyclo-VGAE: Dual-Mechanism Approach to GNN Robustness Against Noisy Labels","display_name":"Cyclo-VGAE: Dual-Mechanism Approach to GNN Robustness Against Noisy Labels","publication_year":2025,"publication_date":"2025-06-30","ids":{"openalex":"https://openalex.org/W4416251943","doi":"https://doi.org/10.1109/ijcnn64981.2025.11227407"},"language":null,"primary_location":{"id":"doi:10.1109/ijcnn64981.2025.11227407","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn64981.2025.11227407","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 International Joint Conference on Neural Networks (IJCNN)","raw_type":"proceedings-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/A5065730563","display_name":"Carlos Minutti-Martinez","orcid":"https://orcid.org/0000-0002-4002-0773"},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Carlos Minutti-Martinez","raw_affiliation_strings":["Center for Research and Innovation in Information and Communication Technologies,INFOTEC,Aguascalientes,Mexico"],"affiliations":[{"raw_affiliation_string":"Center for Research and Innovation in Information and Communication Technologies,INFOTEC,Aguascalientes,Mexico","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5037203797","display_name":"Boris Escalante\u2010Ram\u00edrez","orcid":"https://orcid.org/0000-0003-4936-8714"},"institutions":[{"id":"https://openalex.org/I4210137755","display_name":"National College","ror":"https://ror.org/03wj61f53","country_code":"MX","type":"education","lineage":["https://openalex.org/I4210137755"]}],"countries":["MX"],"is_corresponding":false,"raw_author_name":"Boris Escalante-Ram\u00edrez","raw_affiliation_strings":["National University of Mexico,Division of Electrical Engineering,Mexico City,Mexico"],"affiliations":[{"raw_affiliation_string":"National University of Mexico,Division of Electrical Engineering,Mexico City,Mexico","institution_ids":["https://openalex.org/I4210137755"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5057830181","display_name":"Jimena Olveres","orcid":"https://orcid.org/0000-0002-1514-4520"},"institutions":[{"id":"https://openalex.org/I4210137755","display_name":"National College","ror":"https://ror.org/03wj61f53","country_code":"MX","type":"education","lineage":["https://openalex.org/I4210137755"]}],"countries":["MX"],"is_corresponding":false,"raw_author_name":"Jimena Olveres","raw_affiliation_strings":["National University of Mexico,Division of Electrical Engineering,Mexico City,Mexico"],"affiliations":[{"raw_affiliation_string":"National University of Mexico,Division of Electrical Engineering,Mexico City,Mexico","institution_ids":["https://openalex.org/I4210137755"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5065730563"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.19500237,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"8"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12535","display_name":"Machine Learning and Data Classification","score":0.892300009727478,"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/T12535","display_name":"Machine Learning and Data Classification","score":0.892300009727478,"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":0.024800000712275505,"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/T11550","display_name":"Text and Document Classification Technologies","score":0.007799999788403511,"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/hyperparameter","display_name":"Hyperparameter","score":0.7907999753952026},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.5393999814987183},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.49790000915527344},{"id":"https://openalex.org/keywords/noisy-data","display_name":"Noisy data","score":0.4810999929904938},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.46790000796318054},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.42480000853538513},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3666999936103821}],"concepts":[{"id":"https://openalex.org/C8642999","wikidata":"https://www.wikidata.org/wiki/Q4171168","display_name":"Hyperparameter","level":2,"score":0.7907999753952026},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7354999780654907},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.6503000259399414},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6238999962806702},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.5393999814987183},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.49790000915527344},{"id":"https://openalex.org/C2781170535","wikidata":"https://www.wikidata.org/wiki/Q30587856","display_name":"Noisy data","level":2,"score":0.4810999929904938},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.46790000796318054},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.42480000853538513},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.37049999833106995},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3666999936103821},{"id":"https://openalex.org/C112972136","wikidata":"https://www.wikidata.org/wiki/Q7595718","display_name":"Stability (learning theory)","level":2,"score":0.36070001125335693},{"id":"https://openalex.org/C29265498","wikidata":"https://www.wikidata.org/wiki/Q7047719","display_name":"Noise measurement","level":3,"score":0.3359000086784363},{"id":"https://openalex.org/C139807058","wikidata":"https://www.wikidata.org/wiki/Q352374","display_name":"Adaptation (eye)","level":2,"score":0.29510000348091125},{"id":"https://openalex.org/C168167062","wikidata":"https://www.wikidata.org/wiki/Q1117970","display_name":"Component (thermodynamics)","level":2,"score":0.2802000045776367},{"id":"https://openalex.org/C115051666","wikidata":"https://www.wikidata.org/wiki/Q6522493","display_name":"Ranging","level":2,"score":0.25450000166893005},{"id":"https://openalex.org/C114289077","wikidata":"https://www.wikidata.org/wiki/Q3284399","display_name":"Statistical model","level":2,"score":0.2535000145435333},{"id":"https://openalex.org/C2780909371","wikidata":"https://www.wikidata.org/wiki/Q4801092","display_name":"Artificial noise","level":4,"score":0.25279998779296875}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ijcnn64981.2025.11227407","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn64981.2025.11227407","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 International Joint Conference on Neural Networks (IJCNN)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":10,"referenced_works":["https://openalex.org/W2535690855","https://openalex.org/W3036586801","https://openalex.org/W3166215705","https://openalex.org/W4226256913","https://openalex.org/W4308410741","https://openalex.org/W4321480019","https://openalex.org/W4367663121","https://openalex.org/W4381327774","https://openalex.org/W4403577372","https://openalex.org/W4404566019"],"related_works":[],"abstract_inverted_index":{"Graph":[0,98,103],"Neural":[1,91],"Networks":[2,92],"(GNNs)":[3],"have":[4],"become":[5],"indispensable":[6],"in":[7,33,44,79,133,192],"learning":[8],"from":[9,15],"graph-structured":[10],"data,":[11],"with":[12,96,106,152],"applications":[13],"ranging":[14],"social":[16],"network":[17],"analysis":[18],"to":[19,113,129,199],"bioinformatics.":[20],"However,":[21],"their":[22],"performance":[23,132],"is":[24],"significantly":[25],"degraded":[26],"by":[27],"noisy":[28,134,190],"labels,":[29],"which":[30,76,83],"are":[31],"common":[32],"real-world":[34],"datasets.":[35],"This":[36,181],"paper":[37],"addresses":[38],"the":[39,56,60,85,118,146,166,187],"challenge":[40],"of":[41,52,55,155,189],"label":[42,115,156],"noise":[43,68],"GNNs":[45,193],"through":[46],"two":[47],"approaches:":[48],"(1)":[49],"an":[50],"adaptation":[51],"ExpC+bot,":[53,168],"one":[54],"top-performing":[57],"models":[58],"on":[59,90,145],"clean":[61],"data":[62],"that":[63,161],"we":[64,77,121],"optimize":[65],"extensively":[66],"for":[67],"robustness,":[69],"and":[70,82,126,173,194],"(2)":[71],"our":[72,162],"novel":[73,197],"Cyclo-VGAE":[74,100,164],"method,":[75],"introduce":[78],"this":[80,200],"work":[81,182],"won":[84],"2025":[86],"International":[87],"Joint":[88],"Conference":[89],"(IJCNN)":[93],"Competition:":[94],"\u2019Learning":[95],"Noisy":[97],"Labels\u2019.":[99],"employs":[101],"Variational":[102],"Auto-Encoders":[104],"(VGAE)":[105],"a":[107,196],"statistical":[108],"ensemble":[109],"approach":[110],"specifically":[111],"designed":[112],"mitigate":[114],"noise.":[116,157],"For":[117],"ExpC+bot":[119],"adaptation,":[120],"conduct":[122],"comprehensive":[123],"hyperparameter":[124,179],"optimization":[125],"component":[127],"modifications":[128],"enhance":[130],"its":[131,138],"settings,":[135],"while":[136],"preserving":[137],"original":[139],"architecture.":[140],"We":[141],"compare":[142],"both":[143],"approaches":[144],"Protein-Protein":[147],"Association":[148],"Network":[149],"dataset":[150],"(ogbg-ppa)":[151],"varying":[153],"levels":[154],"Our":[158],"results":[159],"demonstrate":[160],"proposed":[163],"outperforms":[165],"adapted":[167],"achieving":[169],"higher":[170],"F1":[171],"scores":[172],"demonstrating":[174],"greater":[175],"stability":[176],"across":[177],"different":[178],"configurations.":[180],"provides":[183],"valuable":[184],"insight":[185],"into":[186],"handling":[188],"labels":[191],"offers":[195],"solution":[198],"challenging":[201],"problem.":[202]},"counts_by_year":[],"updated_date":"2026-03-07T16:01:11.037858","created_date":"2025-11-14T00:00:00"}