{"id":"https://openalex.org/W4414359898","doi":"https://doi.org/10.24963/ijcai.2025/623","title":"Leveraging Peer-Informed Label Consistency for Robust Graph Neural Networks with Noisy Labels","display_name":"Leveraging Peer-Informed Label Consistency for Robust Graph Neural Networks with Noisy Labels","publication_year":2025,"publication_date":"2025-09-01","ids":{"openalex":"https://openalex.org/W4414359898","doi":"https://doi.org/10.24963/ijcai.2025/623"},"language":"en","primary_location":{"id":"doi:10.24963/ijcai.2025/623","is_oa":false,"landing_page_url":"https://doi.org/10.24963/ijcai.2025/623","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence","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/A5063845462","display_name":"Kailai Li","orcid":"https://orcid.org/0000-0002-2368-3217"},"institutions":[{"id":"https://openalex.org/I183067930","display_name":"Shanghai Jiao Tong University","ror":"https://ror.org/0220qvk04","country_code":"CN","type":"education","lineage":["https://openalex.org/I183067930"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Kailai Li","raw_affiliation_strings":["Shanghai Jiao Tong University","Shanghai Jiao Tong University (Wuxi) Blockchain Advanced Research Center"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Shanghai Jiao Tong University","institution_ids":["https://openalex.org/I183067930"]},{"raw_affiliation_string":"Shanghai Jiao Tong University (Wuxi) Blockchain Advanced Research Center","institution_ids":["https://openalex.org/I183067930"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101709693","display_name":"Jiawei Sun","orcid":"https://orcid.org/0009-0003-1048-5443"},"institutions":[{"id":"https://openalex.org/I183067930","display_name":"Shanghai Jiao Tong University","ror":"https://ror.org/0220qvk04","country_code":"CN","type":"education","lineage":["https://openalex.org/I183067930"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jiawei Sun","raw_affiliation_strings":["Shanghai Jiao Tong University","Shanghai Jiao Tong University (Wuxi) Blockchain Advanced Research Center"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Shanghai Jiao Tong University","institution_ids":["https://openalex.org/I183067930"]},{"raw_affiliation_string":"Shanghai Jiao Tong University (Wuxi) Blockchain Advanced Research Center","institution_ids":["https://openalex.org/I183067930"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5062969662","display_name":"Jiong Lou","orcid":"https://orcid.org/0000-0001-9245-2626"},"institutions":[{"id":"https://openalex.org/I183067930","display_name":"Shanghai Jiao Tong University","ror":"https://ror.org/0220qvk04","country_code":"CN","type":"education","lineage":["https://openalex.org/I183067930"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jiong Lou","raw_affiliation_strings":["Shanghai Jiao Tong University","Shanghai Jiao Tong University (Wuxi) Blockchain Advanced Research Center","Shanghai Key Laboratory of Trusted Data Circulation and Governance and Web3"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Shanghai Jiao Tong University","institution_ids":["https://openalex.org/I183067930"]},{"raw_affiliation_string":"Shanghai Jiao Tong University (Wuxi) Blockchain Advanced Research Center","institution_ids":["https://openalex.org/I183067930"]},{"raw_affiliation_string":"Shanghai Key Laboratory of Trusted Data Circulation and Governance and Web3","institution_ids":[]}]},{"author_position":"middle","author":{"id":null,"display_name":"Zhanbo Feng","orcid":null},"institutions":[{"id":"https://openalex.org/I183067930","display_name":"Shanghai Jiao Tong University","ror":"https://ror.org/0220qvk04","country_code":"CN","type":"education","lineage":["https://openalex.org/I183067930"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhanbo Feng","raw_affiliation_strings":["Shanghai Jiao Tong University","Shanghai Jiao Tong University (Wuxi) Blockchain Advanced Research Center"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Shanghai Jiao Tong University","institution_ids":["https://openalex.org/I183067930"]},{"raw_affiliation_string":"Shanghai Jiao Tong University (Wuxi) Blockchain Advanced Research Center","institution_ids":["https://openalex.org/I183067930"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100938373","display_name":"Hefeng Zhou","orcid":null},"institutions":[{"id":"https://openalex.org/I183067930","display_name":"Shanghai Jiao Tong University","ror":"https://ror.org/0220qvk04","country_code":"CN","type":"education","lineage":["https://openalex.org/I183067930"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Hefeng Zhou","raw_affiliation_strings":["Shanghai Jiao Tong University","Shanghai Jiao Tong University (Wuxi) Blockchain Advanced Research Center"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Shanghai Jiao Tong University","institution_ids":["https://openalex.org/I183067930"]},{"raw_affiliation_string":"Shanghai Jiao Tong University (Wuxi) Blockchain Advanced Research Center","institution_ids":["https://openalex.org/I183067930"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101783501","display_name":"Chentao Wu","orcid":"https://orcid.org/0000-0002-6882-3754"},"institutions":[{"id":"https://openalex.org/I183067930","display_name":"Shanghai Jiao Tong University","ror":"https://ror.org/0220qvk04","country_code":"CN","type":"education","lineage":["https://openalex.org/I183067930"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Chentao Wu","raw_affiliation_strings":["Shanghai Jiao Tong University","Shanghai Key Laboratory of Trusted Data Circulation and Governance and Web3","Yancheng Blockchain Research Institute"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Shanghai Jiao Tong University","institution_ids":["https://openalex.org/I183067930"]},{"raw_affiliation_string":"Shanghai Key Laboratory of Trusted Data Circulation and Governance and Web3","institution_ids":[]},{"raw_affiliation_string":"Yancheng Blockchain Research Institute","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101490654","display_name":"Guangtao Xue","orcid":"https://orcid.org/0000-0002-1617-3593"},"institutions":[{"id":"https://openalex.org/I183067930","display_name":"Shanghai Jiao Tong University","ror":"https://ror.org/0220qvk04","country_code":"CN","type":"education","lineage":["https://openalex.org/I183067930"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Guangtao Xue","raw_affiliation_strings":["Shanghai Jiao Tong University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Shanghai Jiao Tong University","institution_ids":["https://openalex.org/I183067930"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5002311337","display_name":"Wei Zhao","orcid":"https://orcid.org/0000-0002-6268-2559"},"institutions":[{"id":"https://openalex.org/I180726961","display_name":"Shenzhen University","ror":"https://ror.org/01vy4gh70","country_code":"CN","type":"education","lineage":["https://openalex.org/I180726961"]},{"id":"https://openalex.org/I4210152380","display_name":"Shenzhen Technology University","ror":"https://ror.org/04qzpec27","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210152380"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wei Zhao","raw_affiliation_strings":["Shenzhen University of Advanced Technology"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Shenzhen University of Advanced Technology","institution_ids":["https://openalex.org/I4210152380","https://openalex.org/I180726961"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100428255","display_name":"Jie Li","orcid":"https://orcid.org/0000-0002-5131-4769"},"institutions":[{"id":"https://openalex.org/I183067930","display_name":"Shanghai Jiao Tong University","ror":"https://ror.org/0220qvk04","country_code":"CN","type":"education","lineage":["https://openalex.org/I183067930"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jie Li","raw_affiliation_strings":["Shanghai Jiao Tong University","Shanghai Key Laboratory of Trusted Data Circulation and Governance and Web3","Yancheng Blockchain Research Institute"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Shanghai Jiao Tong University","institution_ids":["https://openalex.org/I183067930"]},{"raw_affiliation_string":"Shanghai Key Laboratory of Trusted Data Circulation and Governance and Web3","institution_ids":[]},{"raw_affiliation_string":"Yancheng Blockchain Research Institute","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":9,"corresponding_author_ids":["https://openalex.org/A5063845462"],"corresponding_institution_ids":["https://openalex.org/I183067930"],"apc_list":null,"apc_paid":null,"fwci":1.9057,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.89359695,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":95,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"5598","last_page":"5606"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12535","display_name":"Machine Learning and Data Classification","score":0.9757999777793884,"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":0.9757999777793884,"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/T10320","display_name":"Neural Networks and Applications","score":0.9302999973297119,"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/consistency","display_name":"Consistency (knowledge bases)","score":0.6075000166893005},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5544000267982483},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.5508000254631042},{"id":"https://openalex.org/keywords/filter","display_name":"Filter (signal processing)","score":0.4821999967098236},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.46059998869895935},{"id":"https://openalex.org/keywords/exploit","display_name":"Exploit","score":0.4593000113964081},{"id":"https://openalex.org/keywords/noise-measurement","display_name":"Noise measurement","score":0.4449000060558319},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.43860000371932983},{"id":"https://openalex.org/keywords/gaussian","display_name":"Gaussian","score":0.4185999929904938}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6934000253677368},{"id":"https://openalex.org/C2776436953","wikidata":"https://www.wikidata.org/wiki/Q5163215","display_name":"Consistency (knowledge bases)","level":2,"score":0.6075000166893005},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.583299994468689},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5544000267982483},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.5508000254631042},{"id":"https://openalex.org/C106131492","wikidata":"https://www.wikidata.org/wiki/Q3072260","display_name":"Filter (signal processing)","level":2,"score":0.4821999967098236},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.46059998869895935},{"id":"https://openalex.org/C165696696","wikidata":"https://www.wikidata.org/wiki/Q11287","display_name":"Exploit","level":2,"score":0.4593000113964081},{"id":"https://openalex.org/C29265498","wikidata":"https://www.wikidata.org/wiki/Q7047719","display_name":"Noise measurement","level":3,"score":0.4449000060558319},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.43860000371932983},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.4185999929904938},{"id":"https://openalex.org/C2781170535","wikidata":"https://www.wikidata.org/wiki/Q30587856","display_name":"Noisy data","level":2,"score":0.4146000146865845},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4106000065803528},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4036000072956085},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.4018000066280365},{"id":"https://openalex.org/C2984842247","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep neural networks","level":3,"score":0.3483999967575073},{"id":"https://openalex.org/C163294075","wikidata":"https://www.wikidata.org/wiki/Q581861","display_name":"Noise reduction","level":2,"score":0.34540000557899475},{"id":"https://openalex.org/C4199805","wikidata":"https://www.wikidata.org/wiki/Q2725903","display_name":"Gaussian noise","level":2,"score":0.32330000400543213},{"id":"https://openalex.org/C149441793","wikidata":"https://www.wikidata.org/wiki/Q200726","display_name":"Probability distribution","level":2,"score":0.32249999046325684},{"id":"https://openalex.org/C2780009758","wikidata":"https://www.wikidata.org/wiki/Q6804172","display_name":"Measure (data warehouse)","level":2,"score":0.32100000977516174},{"id":"https://openalex.org/C2776517306","wikidata":"https://www.wikidata.org/wiki/Q29017317","display_name":"Similarity measure","level":2,"score":0.3192000091075897},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.3086000084877014},{"id":"https://openalex.org/C103278499","wikidata":"https://www.wikidata.org/wiki/Q254465","display_name":"Similarity (geometry)","level":3,"score":0.30790001153945923},{"id":"https://openalex.org/C2779530757","wikidata":"https://www.wikidata.org/wiki/Q1207505","display_name":"Quality (philosophy)","level":2,"score":0.2782000005245209},{"id":"https://openalex.org/C88230418","wikidata":"https://www.wikidata.org/wiki/Q131476","display_name":"Graph theory","level":2,"score":0.2667999863624573},{"id":"https://openalex.org/C37279795","wikidata":"https://www.wikidata.org/wiki/Q2492305","display_name":"Consistency model","level":3,"score":0.25870001316070557},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.2533999979496002},{"id":"https://openalex.org/C159694833","wikidata":"https://www.wikidata.org/wiki/Q2321565","display_name":"Iterative method","level":2,"score":0.25040000677108765}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.24963/ijcai.2025/623","is_oa":false,"landing_page_url":"https://doi.org/10.24963/ijcai.2025/623","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence","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":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Graph":[0],"Neural":[1],"Networks":[2],"(GNNs)":[3],"excel":[4],"in":[5,27,111],"many":[6],"applications":[7],"but":[8],"struggle":[9],"when":[10],"trained":[11],"with":[12,80],"noisy":[13],"labels,":[14],"especially":[15],"as":[16],"noise":[17,130],"can":[18],"propagate":[19],"through":[20],"the":[21,34,100],"graph":[22,38,72],"structure.":[23],"Despite":[24],"recent":[25],"progress":[26],"developing":[28],"robust":[29],"GNNs,":[30],"few":[31],"methods":[32],"exploit":[33],"intrinsic":[35],"properties":[36],"of":[37],"data":[39],"to":[40,99,103],"filter":[41],"out":[42],"noise.":[43],"In":[44],"this":[45],"paper,":[46],"we":[47,85],"introduce":[48],"ProCon,":[49],"a":[50,83],"novel":[51],"framework":[52],"that":[53,121],"identifies":[54],"mislabeled":[55],"nodes":[56,75],"by":[57,68],"measuring":[58],"label":[59,129],"consistency":[60,79,101],"among":[61],"semantically":[62],"similar":[63],"peers,":[64,82],"which":[65,107],"are":[66],"determined":[67],"feature":[69],"similarity":[70],"and":[71,131],"adjacency.":[73],"Mislabeled":[74],"typically":[76],"exhibit":[77],"lower":[78],"these":[81],"signal":[84],"measure":[86],"using":[87],"pseudo-labels":[88],"derived":[89],"from":[90],"representational":[91],"prototypes.":[92],"A":[93],"Gaussian":[94],"Mixture":[95],"Model":[96],"is":[97],"fitted":[98],"distribution":[102],"identify":[104],"clean":[105],"samples,":[106],"refine":[108],"prototype":[109],"quality":[110],"an":[112],"iterative":[113],"feedback":[114],"loop.":[115],"Experiments":[116],"on":[117],"multiple":[118],"datasets":[119],"demonstrate":[120],"ProCon":[122],"significantly":[123],"outperforms":[124],"state-of-the-art":[125],"methods,":[126],"effectively":[127],"mitigating":[128],"enhancing":[132],"GNN":[133],"robustness.":[134]},"counts_by_year":[{"year":2026,"cited_by_count":1}],"updated_date":"2026-05-21T06:26:12.895304","created_date":"2025-10-10T00:00:00"}