{"id":"https://openalex.org/W4388406470","doi":"https://doi.org/10.1109/kse59128.2023.10299469","title":"Reduce Over-smoothing in Label Non-Uniformity Learning Based on Graph Convolution Network","display_name":"Reduce Over-smoothing in Label Non-Uniformity Learning Based on Graph Convolution Network","publication_year":2023,"publication_date":"2023-10-18","ids":{"openalex":"https://openalex.org/W4388406470","doi":"https://doi.org/10.1109/kse59128.2023.10299469"},"language":"en","primary_location":{"id":"doi:10.1109/kse59128.2023.10299469","is_oa":false,"landing_page_url":"https://doi.org/10.1109/kse59128.2023.10299469","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 15th International Conference on Knowledge and Systems Engineering (KSE)","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/A5112135123","display_name":"Thu Uyen","orcid":null},"institutions":[{"id":"https://openalex.org/I67868205","display_name":"VNU University of Science","ror":"https://ror.org/05w54hk79","country_code":"VN","type":"education","lineage":["https://openalex.org/I177233841","https://openalex.org/I67868205"]}],"countries":["VN"],"is_corresponding":true,"raw_author_name":"Thu Uyen Do","raw_affiliation_strings":["Institute for Artificial Intelligence, VNU University of Engineering and Technology,Hanoi,Vietnam","Institute for Artificial Intelligence, VNU University of Engineering and Technology, Hanoi, Vietnam"],"affiliations":[{"raw_affiliation_string":"Institute for Artificial Intelligence, VNU University of Engineering and Technology,Hanoi,Vietnam","institution_ids":["https://openalex.org/I67868205"]},{"raw_affiliation_string":"Institute for Artificial Intelligence, VNU University of Engineering and Technology, Hanoi, Vietnam","institution_ids":["https://openalex.org/I67868205"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5053935756","display_name":"Viet Cuong Ta","orcid":"https://orcid.org/0000-0001-8058-5915"},"institutions":[{"id":"https://openalex.org/I67868205","display_name":"VNU University of Science","ror":"https://ror.org/05w54hk79","country_code":"VN","type":"education","lineage":["https://openalex.org/I177233841","https://openalex.org/I67868205"]}],"countries":["VN"],"is_corresponding":false,"raw_author_name":"Viet Cuong Ta","raw_affiliation_strings":["VNU University of Engineering and Technology,HMI Lab,Hanoi,Vietnam","HMI Lab, VNU University of Engineering and Technology, Hanoi, Vietnam"],"affiliations":[{"raw_affiliation_string":"VNU University of Engineering and Technology,HMI Lab,Hanoi,Vietnam","institution_ids":["https://openalex.org/I67868205"]},{"raw_affiliation_string":"HMI Lab, VNU University of Engineering and Technology, Hanoi, Vietnam","institution_ids":["https://openalex.org/I67868205"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5112135123"],"corresponding_institution_ids":["https://openalex.org/I67868205"],"apc_list":null,"apc_paid":null,"fwci":0.1961,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.57131964,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":91,"max":95},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"6"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11273","display_name":"Advanced Graph Neural Networks","score":0.9995999932289124,"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":0.9995999932289124,"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/T12535","display_name":"Machine Learning and Data Classification","score":0.9797000288963318,"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/T13731","display_name":"Advanced Computing and Algorithms","score":0.979200005531311,"subfield":{"id":"https://openalex.org/subfields/3322","display_name":"Urban Studies"},"field":{"id":"https://openalex.org/fields/33","display_name":"Social Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/smoothing","display_name":"Smoothing","score":0.8131601810455322},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7245582342147827},{"id":"https://openalex.org/keywords/residual","display_name":"Residual","score":0.6559991836547852},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.6038134098052979},{"id":"https://openalex.org/keywords/margin","display_name":"Margin (machine learning)","score":0.5727666616439819},{"id":"https://openalex.org/keywords/convolution","display_name":"Convolution (computer science)","score":0.5629206895828247},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.48003140091896057},{"id":"https://openalex.org/keywords/property","display_name":"Property (philosophy)","score":0.4502611756324768},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4379969835281372},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.3266732096672058},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.2506597638130188},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.1465584933757782}],"concepts":[{"id":"https://openalex.org/C3770464","wikidata":"https://www.wikidata.org/wiki/Q775963","display_name":"Smoothing","level":2,"score":0.8131601810455322},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7245582342147827},{"id":"https://openalex.org/C155512373","wikidata":"https://www.wikidata.org/wiki/Q287450","display_name":"Residual","level":2,"score":0.6559991836547852},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.6038134098052979},{"id":"https://openalex.org/C774472","wikidata":"https://www.wikidata.org/wiki/Q6760393","display_name":"Margin (machine learning)","level":2,"score":0.5727666616439819},{"id":"https://openalex.org/C45347329","wikidata":"https://www.wikidata.org/wiki/Q5166604","display_name":"Convolution (computer science)","level":3,"score":0.5629206895828247},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.48003140091896057},{"id":"https://openalex.org/C189950617","wikidata":"https://www.wikidata.org/wiki/Q937228","display_name":"Property (philosophy)","level":2,"score":0.4502611756324768},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4379969835281372},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3266732096672058},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.2506597638130188},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.1465584933757782},{"id":"https://openalex.org/C111472728","wikidata":"https://www.wikidata.org/wiki/Q9471","display_name":"Epistemology","level":1,"score":0.0},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/kse59128.2023.10299469","is_oa":false,"landing_page_url":"https://doi.org/10.1109/kse59128.2023.10299469","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 15th International Conference on Knowledge and Systems Engineering (KSE)","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":45,"referenced_works":["https://openalex.org/W1522301498","https://openalex.org/W2153959628","https://openalex.org/W2158787690","https://openalex.org/W2194775991","https://openalex.org/W2606780347","https://openalex.org/W2735272571","https://openalex.org/W2900470550","https://openalex.org/W2907492528","https://openalex.org/W2916106175","https://openalex.org/W2963312446","https://openalex.org/W2963757395","https://openalex.org/W2964015378","https://openalex.org/W2964051675","https://openalex.org/W2964321699","https://openalex.org/W2981275410","https://openalex.org/W2996268457","https://openalex.org/W2998496395","https://openalex.org/W3034492151","https://openalex.org/W3082217026","https://openalex.org/W3092125438","https://openalex.org/W3152893301","https://openalex.org/W3208506679","https://openalex.org/W4281555548","https://openalex.org/W4302011509","https://openalex.org/W4309676791","https://openalex.org/W4321649052","https://openalex.org/W4353007232","https://openalex.org/W4367693460","https://openalex.org/W6631190155","https://openalex.org/W6699364125","https://openalex.org/W6720006811","https://openalex.org/W6726873649","https://openalex.org/W6736685754","https://openalex.org/W6748856961","https://openalex.org/W6756561102","https://openalex.org/W6760001035","https://openalex.org/W6769037471","https://openalex.org/W6771932116","https://openalex.org/W6779961489","https://openalex.org/W6782455772","https://openalex.org/W6838236921","https://openalex.org/W6845434523","https://openalex.org/W6850331305","https://openalex.org/W6851318867","https://openalex.org/W6852327890"],"related_works":["https://openalex.org/W1978572805","https://openalex.org/W2383807498","https://openalex.org/W1997992934","https://openalex.org/W1987225439","https://openalex.org/W4238188170","https://openalex.org/W2125114371","https://openalex.org/W2019977573","https://openalex.org/W2925692864","https://openalex.org/W2357412443","https://openalex.org/W3025407325"],"abstract_inverted_index":{"In":[0],"the":[1,9,17,27,40,46,50,58,60,64,72,83,94,101,109,115,119,130,139,142],"semi-supervised":[2],"training":[3,30,77],"of":[4,49,96,118,141],"Graph":[5],"Convolution":[6],"Network":[7],"(GCN),":[8],"output":[10],"signals":[11],"can":[12,107,137],"be":[13],"used":[14],"to":[15,20,33,44,57,70,99],"reveal":[16],"graph":[18],"structure":[19],"some":[21],"extent.":[22],"By":[23],"exploiting":[24],"this":[25,54,90],"property,":[26],"label":[28,79],"non-uniformity":[29,80],"procedure":[31],"attempts":[32],"extract":[34],"high":[35],"confidence":[36],"nodes":[37],"and":[38,112,129,145],"drop":[39],"low":[41],"important":[42],"edges":[43],"increase":[45],"learning":[47,65,116],"capacity":[48,117],"base":[51,102],"model.":[52,120],"However,":[53],"approach":[55,124],"leads":[56],"increasing":[59],"over-smoothing":[61,73,110],"issue":[62],"in":[63,75],"process.":[66],"Our":[67],"works":[68],"begin":[69],"examine":[71],"degrees":[74],"multi-step":[76],"with":[78,148],"weights":[81],"through":[82],"mean":[84],"average":[85],"distance":[86],"measure.":[87],"Based":[88],"on":[89,125],"observation,":[91],"we":[92],"propose":[93],"usage":[95],"residual":[97],"connections":[98],"improve":[100,114,138],"GCN.":[103],"The":[104],"resulting":[105],"model":[106,136],"reduce":[108],"effect,":[111],"thus":[113],"We":[121],"evaluate":[122],"our":[123,134],"6":[126],"public":[127],"datasets":[128],"results":[131,140],"show":[132],"that":[133],"proposed":[135],"GCN":[143],"architecture":[144],"other":[146],"baselines":[147],"a":[149],"wide":[150],"margin.":[151]},"counts_by_year":[{"year":2025,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}