乐胖代购免代理版

{"id":"https://openalex.org/W4226030949","doi":"https://doi.org/10.1109/tii.2022.3156658","title":"Denoising Aggregation of Graph Neural Networks by Using Principal Component Analysis","display_name":"Denoising Aggregation of Graph Neural Networks by Using Principal Component Analysis","publication_year":2022,"publication_date":"2022-03-07","ids":{"openalex":"https://openalex.org/W4226030949","doi":"https://doi.org/10.1109/tii.2022.3156658"},"language":"en","primary_location":{"is_oa":false,"landing_page_url":"https://doi.org/10.1109/tii.2022.3156658","pdf_url":null,"source":{"id":"https://openalex.org/S184777250","display_name":"IEEE Transactions on Industrial Informatics","issn_l":"1551-3203","issn":["1551-3203","1941-0050"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false},"type":"article","type_crossref":"journal-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/A5009180198","display_name":"Wei Dong","orcid":"https://orcid.org/0000-0003-0263-3584"},"institutions":[{"id":"https://openalex.org/I17145004","display_name":"Northwestern Polytechnical University","ror":"https://ror.org/01y0j0j86","country_code":"CN","type":"education","lineage":["https://openalex.org/I17145004"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wei Dong","raw_affiliation_strings":["School of Computer Science and Engineering, Northwestern Polytechnical University, Xi’an, China"],"affiliations":[{"raw_affiliation_string":"School of Computer Science and Engineering, Northwestern Polytechnical University, Xi’an, China","institution_ids":["https://openalex.org/I17145004"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5016267473","display_name":"Marcin Wo\u017aniak","orcid":"https://orcid.org/0000-0002-9073-5347"},"institutions":[{"id":"https://openalex.org/I119004910","display_name":"Silesian University of Technology","ror":"https://ror.org/02dyjk442","country_code":"PL","type":"education","lineage":["https://openalex.org/I119004910"]}],"countries":["PL"],"is_corresponding":false,"raw_author_name":"Marcin Wo\u017aniak","raw_affiliation_strings":["Faculty of Applied Mathematics, Silesian University of Technology, Gliwice, Poland"],"affiliations":[{"raw_affiliation_string":"Faculty of Applied Mathematics, Silesian University of Technology, Gliwice, Poland","institution_ids":["https://openalex.org/I119004910"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5017194230","display_name":"Junsheng Wu","orcid":"https://orcid.org/0000-0003-3974-5838"},"institutions":[{"id":"https://openalex.org/I17145004","display_name":"Northwestern Polytechnical University","ror":"https://ror.org/01y0j0j86","country_code":"CN","type":"education","lineage":["https://openalex.org/I17145004"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Junsheng Wu","raw_affiliation_strings":["School of Software, Northwestern Polytechnical University, Xi’an, China"],"affiliations":[{"raw_affiliation_string":"School of Software, Northwestern Polytechnical University, Xi’an, China","institution_ids":["https://openalex.org/I17145004"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5102983096","display_name":"Weigang Li","orcid":"https://orcid.org/0000-0001-8290-2275"},"institutions":[{"id":"https://openalex.org/I17145004","display_name":"Northwestern Polytechnical University","ror":"https://ror.org/01y0j0j86","country_code":"CN","type":"education","lineage":["https://openalex.org/I17145004"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Weigang Li","raw_affiliation_strings":["School of Software, Northwestern Polytechnical University, Xi’an, China"],"affiliations":[{"raw_affiliation_string":"School of Software, Northwestern Polytechnical University, Xi’an, China","institution_ids":["https://openalex.org/I17145004"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5017773073","display_name":"Zongwen Bai","orcid":"https://orcid.org/0000-0001-8795-4373"},"institutions":[{"id":"https://openalex.org/I4210089311","display_name":"Yanan University Affiliated Hospital","ror":"https://ror.org/006992e45","country_code":"CN","type":"healthcare","lineage":["https://openalex.org/I4210089311"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zongwen Bai","raw_affiliation_strings":["Shaanxi Key Laboratory of Intelligent Processing of Big Energy Data, School of Physics and Electronic Information, Yanan University, Yan’an, China"],"affiliations":[{"raw_affiliation_string":"Shaanxi Key Laboratory of Intelligent Processing of Big Energy Data, School of Physics and Electronic Information, Yanan University, Yan’an, China","institution_ids":["https://openalex.org/I4210089311"]}]}],"institution_assertions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":9.402,"has_fulltext":false,"cited_by_count":58,"citation_normalized_percentile":{"value":0.99997,"is_in_top_1_percent":true,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":98,"max":99},"biblio":{"volume":"19","issue":"3","first_page":"2385","last_page":"2394"},"is_retracted":false,"is_paratext":false,"primary_topic":{"id":"https://openalex.org/T11273","display_name":"Advanced Graph Neural Networks","score":0.9999,"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":0.9999,"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":0.994,"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/T12676","display_name":"Machine Learning and ELM","score":0.9901,"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/dropout","display_name":"Dropout (neural networks)","score":0.4297778}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7002344},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5400551},{"id":"https://openalex.org/C163294075","wikidata":"https://www.wikidata.org/wiki/Q581861","display_name":"Noise reduction","level":2,"score":0.5094345},{"id":"https://openalex.org/C27438332","wikidata":"https://www.wikidata.org/wiki/Q2873","display_name":"Principal component analysis","level":2,"score":0.49113485},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.46592543},{"id":"https://openalex.org/C70518039","wikidata":"https://www.wikidata.org/wiki/Q16000077","display_name":"Dimensionality reduction","level":2,"score":0.45011532},{"id":"https://openalex.org/C2776145597","wikidata":"https://www.wikidata.org/wiki/Q25339462","display_name":"Dropout (neural networks)","level":2,"score":0.4297778},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.42828596},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.42398316},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.4212242},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4158377},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.18181583},{"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":[{"is_oa":false,"landing_page_url":"https://doi.org/10.1109/tii.2022.3156658","pdf_url":null,"source":{"id":"https://openalex.org/S184777250","display_name":"IEEE Transactions on Industrial Informatics","issn_l":"1551-3203","issn":["1551-3203","1941-0050"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/11","display_name":"Sustainable cities and communities","score":0.69}],"grants":[{"funder":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China","award_id":"61761042"}],"datasets":[],"versions":[],"referenced_works_count":12,"referenced_works":["https://openalex.org/W1495164570","https://openalex.org/W1904365287","https://openalex.org/W2042524010","https://openalex.org/W2753105308","https://openalex.org/W2900470550","https://openalex.org/W2907492528","https://openalex.org/W2945848398","https://openalex.org/W2981132250","https://openalex.org/W3015616869","https://openalex.org/W3034604293","https://openalex.org/W3043790937","https://openalex.org/W3101553402"],"related_works":["https://openalex.org/W4389669152","https://openalex.org/W4387893611","https://openalex.org/W4317486777","https://openalex.org/W2579148721","https://openalex.org/W2347335694","https://openalex.org/W2091056927","https://openalex.org/W2067407580","https://openalex.org/W2038514069","https://openalex.org/W2009181529","https://openalex.org/W1967233468"],"abstract_inverted_index":{"To":[0,62],"avoid":[1],"the":[2,16,24,40,47,55,85,96,111,122,139,149,153,164,167],"overfitting":[3],"phenomenon":[4],"that":[5],"appeared":[6],"in":[7,66,144,157],"performing":[8],"graph":[9],"neural":[10],"networks":[11],"(GNNs)":[12],"on":[13,106,175],"test":[14],"examples,":[15],"feature":[17,112],"encoding":[18],"scheme":[19],"of":[20,59,142,152,166,172],"GNNs":[21],"usually":[22],"introduces":[23],"dropout":[25,41],"procedure.":[26],"However,":[27],"after":[28],"learning":[29],"latent":[30],"node":[31,124],"representations":[32],"under":[33],"this":[34,64,67],"scheme,":[35],"Gaussian":[36,97],"noise":[37],"produced":[38],"by":[39],"operation":[42],"is":[43,101],"inevitably":[44],"transmitted":[45],"into":[46],"next":[48],"neighborhood":[49],"aggregation":[50,57,75],"step,":[51],"which":[52,77],"necessarily":[53],"hampers":[54],"unbiased":[56],"ability":[58],"GNN":[60],"models.":[61],"address":[63],"issue,":[65],"article,":[68],"we":[69],"present":[70],"a":[71,170],"novel":[72],"aggregator,":[73],"denoising":[74,130,160],"(DNAG),":[76],"utilizes":[78],"principal":[79],"component":[80],"analysis":[81],"(PCA)":[82],"to":[83,109,120,126,137],"preserve":[84],"aggregated":[86],"real":[87],"signals":[88],"from":[89,103],"neighboring":[90,123],"features":[91,125],"and":[92,162],"simultaneously":[93],"filter":[94],"out":[95],"noise.":[98],"The":[99],"idea":[100],"different":[102],"using":[104],"PCA":[105,116,143],"traditional":[107],"applications":[108],"reduce":[110],"dimension.":[113],"We":[114,132],"regard":[115],"as":[117],"an":[118],"aggregator":[119],"compress":[121],"have":[127],"better":[128],"expressive":[129],"power.":[131],"propose":[133],"new":[134],"training":[135],"architectures":[136],"simplify":[138],"intensive":[140],"computation":[141],"DNAG.":[145],"Numerical":[146],"experiments":[147],"show":[148],"apparent":[150],"superiority":[151],"proposed":[154],"DNAG":[155],"models":[156],"gaining":[158],"more":[159],"capability":[161],"achieving":[163],"state":[165],"art":[168],"for":[169],"set":[171],"predictive":[173],"tasks":[174],"several":[176],"graph-structured":[177],"datasets.":[178]},"cited_by_api_url":"https://api.openalex.org/works?filter=cites:W4226030949","counts_by_year":[{"year":2024,"cited_by_count":12},{"year":2023,"cited_by_count":22},{"year":2022,"cited_by_count":24}],"updated_date":"2024-12-09T14:12:36.639593","created_date":"2022-05-05"}