{"id":"https://openalex.org/W4410008090","doi":"https://doi.org/10.1145/3733605","title":"A Systematic Study and Analysis of Graph Neural Networks under Noise","display_name":"A Systematic Study and Analysis of Graph Neural Networks under Noise","publication_year":2025,"publication_date":"2025-05-01","ids":{"openalex":"https://openalex.org/W4410008090","doi":"https://doi.org/10.1145/3733605"},"language":"en","primary_location":{"id":"doi:10.1145/3733605","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3733605","pdf_url":null,"source":{"id":"https://openalex.org/S41523882","display_name":"ACM Transactions on Knowledge Discovery from Data","issn_l":"1556-4681","issn":["1556-4681","1556-472X"],"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 Knowledge Discovery from Data","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/A5082246936","display_name":"Yufei Jin","orcid":"https://orcid.org/0009-0006-6570-123X"},"institutions":[{"id":"https://openalex.org/I63772739","display_name":"Florida Atlantic University","ror":"https://ror.org/05p8w6387","country_code":"US","type":"education","lineage":["https://openalex.org/I63772739"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Yufei Jin","raw_affiliation_strings":["Department of Electrical Engineering and Computer Science, Florida Atlantic University, Boca Raton, Florida, USA","Dept. of Electrical Engineering &amp; Computer Science, Florida Atlantic University, USA"],"affiliations":[{"raw_affiliation_string":"Department of Electrical Engineering and Computer Science, Florida Atlantic University, Boca Raton, Florida, USA","institution_ids":["https://openalex.org/I63772739"]},{"raw_affiliation_string":"Dept. of Electrical Engineering &amp; Computer Science, Florida Atlantic University, USA","institution_ids":["https://openalex.org/I63772739"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5084641325","display_name":"Xingquan Zhu","orcid":"https://orcid.org/0000-0003-4129-9611"},"institutions":[{"id":"https://openalex.org/I63772739","display_name":"Florida Atlantic University","ror":"https://ror.org/05p8w6387","country_code":"US","type":"education","lineage":["https://openalex.org/I63772739"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Xingquan Zhu","raw_affiliation_strings":["Department of Electrical Engineering and Computer Science, Florida Atlantic University, Boca Raton, Florida, USA","Dept. of Electrical Engineering &amp; Computer Science, Florida Atlantic University, USA"],"affiliations":[{"raw_affiliation_string":"Department of Electrical Engineering and Computer Science, Florida Atlantic University, Boca Raton, Florida, USA","institution_ids":["https://openalex.org/I63772739"]},{"raw_affiliation_string":"Dept. of Electrical Engineering &amp; Computer Science, Florida Atlantic University, USA","institution_ids":["https://openalex.org/I63772739"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5082246936"],"corresponding_institution_ids":["https://openalex.org/I63772739"],"apc_list":null,"apc_paid":null,"fwci":6.8844,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":{"value":0.96226518,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":91,"max":99},"biblio":{"volume":"19","issue":"6","first_page":"1","last_page":"20"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12535","display_name":"Machine Learning and Data Classification","score":0.9998000264167786,"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":0.9998000264167786,"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/T12072","display_name":"Machine Learning and Algorithms","score":0.9916999936103821,"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/T10057","display_name":"Face and Expression Recognition","score":0.9861000180244446,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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":0.521600604057312},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.5036479830741882},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.49710896611213684},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.48277774453163147},{"id":"https://openalex.org/keywords/power-graph-analysis","display_name":"Power graph analysis","score":0.45101219415664673},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4242270290851593},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.41312891244888306},{"id":"https://openalex.org/keywords/data-science","display_name":"Data science","score":0.3978840410709381},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.35305172204971313},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.33570805191993713},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.3032403588294983}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.521600604057312},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.5036479830741882},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.49710896611213684},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.48277774453163147},{"id":"https://openalex.org/C106937863","wikidata":"https://www.wikidata.org/wiki/Q7236518","display_name":"Power graph analysis","level":3,"score":0.45101219415664673},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4242270290851593},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.41312891244888306},{"id":"https://openalex.org/C2522767166","wikidata":"https://www.wikidata.org/wiki/Q2374463","display_name":"Data science","level":1,"score":0.3978840410709381},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.35305172204971313},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.33570805191993713},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.3032403588294983},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3733605","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3733605","pdf_url":null,"source":{"id":"https://openalex.org/S41523882","display_name":"ACM Transactions on Knowledge Discovery from Data","issn_l":"1556-4681","issn":["1556-4681","1556-472X"],"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 Knowledge Discovery from Data","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G4697979232","display_name":null,"funder_award_id":"IIS-2302786, IIS-2236579, IOS-2430224","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"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":18,"referenced_works":["https://openalex.org/W2034841618","https://openalex.org/W2107559689","https://openalex.org/W2803831897","https://openalex.org/W2994598354","https://openalex.org/W3098276446","https://openalex.org/W3104667978","https://openalex.org/W3127927415","https://openalex.org/W3166215705","https://openalex.org/W4212890525","https://openalex.org/W4221139060","https://openalex.org/W4312382935","https://openalex.org/W4321480019","https://openalex.org/W4367663121","https://openalex.org/W4382239927","https://openalex.org/W4392796768","https://openalex.org/W4393159493","https://openalex.org/W4393972734","https://openalex.org/W4396769202"],"related_works":["https://openalex.org/W2391251536","https://openalex.org/W2362198218","https://openalex.org/W2019521278","https://openalex.org/W4368755543","https://openalex.org/W3088104186","https://openalex.org/W1543023114","https://openalex.org/W4245709619","https://openalex.org/W85088162","https://openalex.org/W2294734161","https://openalex.org/W3081531507"],"abstract_inverted_index":{"Graph":[0,65,69,73],"Neural":[1],"Networks":[2,67,71,75],"(GNNs)":[3],"have":[4],"shown":[5],"superb":[6],"performance":[7,27,44],"in":[8,101,168],"handling":[9],"networked":[10],"data,":[11],"mainly":[12,31],"attributed":[13],"to":[14,103,130,136,139,145,164],"their":[15],"message":[16],"passing":[17],"and":[18,56,77,90,115,141,162],"convolution":[19],"process":[20],"across":[21],"neighbors.":[22],"For":[23],"most":[24,112,121],"literature,":[25],"the":[26,111,120,132],"of":[28,59,62,83,106,156],"GNNs":[29,97,157,167],"is":[30,110,119],"reported":[32],"based":[33],"on":[34],"noise-free":[35],"data":[36],"environments.":[37],"No":[38],"study":[39,55,94,151],"has":[40],"systematically":[41],"evaluated":[42],"GNNs\u2019":[43],"under":[45,80,158],"noise.":[46,92,107],"In":[47],"this":[48],"article,":[49],"we":[50],"carry":[51,126],"out":[52,127],"an":[53],"empirical":[54],"theoretical":[57,128],"analysis":[58,129],"four":[60],"types":[61,82,105],"GNNs,":[63],"including":[64,85],"Convolutional":[66],"(GCNs),":[68],"Attention":[70],"(GATs),":[72],"Contrastive":[74],"(GCL),":[76],"graph":[78],"UniFilter":[79],"three":[81],"noise,":[84,87,89,140],"attribute":[86],"structure":[88],"label":[91],"Our":[93,150],"shows":[95],"that":[96],"behave":[98],"tremendously":[99],"differently":[100],"response":[102],"different":[104],"Overall,":[108],"GAT":[109,135],"noise":[113,122,148,159],"vulnerable":[114],"sensitive,":[116],"whereas":[117],"GCL":[118],"resilient.":[123],"We":[124],"further":[125],"explain":[131],"reason":[133],"causing":[134],"be":[137],"sensitive":[138],"propose":[142],"a":[143],"solution":[144],"enhance":[146],"its":[147],"resilience.":[149],"brings":[152],"in-depth":[153],"firsthand":[154],"knowledge":[155],"for":[160],"researchers":[161],"practitioners":[163],"better":[165],"utilize":[166],"real-world":[169],"applications.":[170]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2025,"cited_by_count":1}],"updated_date":"2026-04-14T08:04:32.555800","created_date":"2025-10-10T00:00:00"}
