{"id":"https://openalex.org/W4390044640","doi":"https://doi.org/10.1109/tce.2023.3345390","title":"Feature Selection Graph Neural Network for Optimized Node Categorization","display_name":"Feature Selection Graph Neural Network for Optimized Node Categorization","publication_year":2023,"publication_date":"2023-12-21","ids":{"openalex":"https://openalex.org/W4390044640","doi":"https://doi.org/10.1109/tce.2023.3345390"},"language":"en","primary_location":{"id":"doi:10.1109/tce.2023.3345390","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tce.2023.3345390","pdf_url":null,"source":{"id":"https://openalex.org/S126824455","display_name":"IEEE Transactions on Consumer Electronics","issn_l":"0098-3063","issn":["0098-3063","1558-4127"],"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":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Consumer Electronics","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/A5015700005","display_name":"K. R. Sri Preethaa","orcid":null},"institutions":[{"id":"https://openalex.org/I31419693","display_name":"Kyungpook National University","ror":"https://ror.org/040c17130","country_code":"KR","type":"education","lineage":["https://openalex.org/I31419693"]}],"countries":["KR"],"is_corresponding":true,"raw_author_name":"K. R. Sri Preethaa","raw_affiliation_strings":["Department of Robot and Smart System Engineering, Kyungpook National University, Daegu, South Korea"],"affiliations":[{"raw_affiliation_string":"Department of Robot and Smart System Engineering, Kyungpook National University, Daegu, South Korea","institution_ids":["https://openalex.org/I31419693"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5049246663","display_name":"Gitanjali Wadhwa","orcid":null},"institutions":[{"id":"https://openalex.org/I4210133257","display_name":"KPR Institute of Engineering and Technology","ror":"https://ror.org/02q9f3a53","country_code":"IN","type":"education","lineage":["https://openalex.org/I4210133257"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Gitanjali Wadhwa","raw_affiliation_strings":["Department of Computer Science and Engineering, KPR Institute of Engineering and Technology, Coimbatore, India"],"affiliations":[{"raw_affiliation_string":"Department of Computer Science and Engineering, KPR Institute of Engineering and Technology, Coimbatore, India","institution_ids":["https://openalex.org/I4210133257"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5051146250","display_name":"Yuvaraj Natarajan","orcid":"https://orcid.org/0000-0002-0422-8374"},"institutions":[{"id":"https://openalex.org/I4210133257","display_name":"KPR Institute of Engineering and Technology","ror":"https://ror.org/02q9f3a53","country_code":"IN","type":"education","lineage":["https://openalex.org/I4210133257"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Yuvaraj Natarajan","raw_affiliation_strings":["Department of Computer Science and Engineering, KPR Institute of Engineering and Technology, Coimbatore, India"],"affiliations":[{"raw_affiliation_string":"Department of Computer Science and Engineering, KPR Institute of Engineering and Technology, Coimbatore, India","institution_ids":["https://openalex.org/I4210133257"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5057499929","display_name":"Anand Paul","orcid":"https://orcid.org/0000-0002-1952-9743"},"institutions":[{"id":"https://openalex.org/I31419693","display_name":"Kyungpook National University","ror":"https://ror.org/040c17130","country_code":"KR","type":"education","lineage":["https://openalex.org/I31419693"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Anand Paul","raw_affiliation_strings":["Department of Computer Science and Engineering, Kyungpook National University, Daegu, South Korea"],"affiliations":[{"raw_affiliation_string":"Department of Computer Science and Engineering, Kyungpook National University, Daegu, South Korea","institution_ids":["https://openalex.org/I31419693"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5015700005"],"corresponding_institution_ids":["https://openalex.org/I31419693"],"apc_list":null,"apc_paid":null,"fwci":0.5219,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":{"value":0.73273751,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":96,"max":97},"biblio":{"volume":"70","issue":"1","first_page":"2872","last_page":"2883"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11273","display_name":"Advanced Graph Neural Networks","score":0.9986000061035156,"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":0.9986000061035156,"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/T12292","display_name":"Graph Theory and Algorithms","score":0.9696000218391418,"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"}},{"id":"https://openalex.org/T10320","display_name":"Neural Networks and Applications","score":0.9587000012397766,"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/computer-science","display_name":"Computer science","score":0.7613009810447693},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5867540836334229},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5580918192863464},{"id":"https://openalex.org/keywords/normalization","display_name":"Normalization (sociology)","score":0.5158883333206177},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.5060941576957703},{"id":"https://openalex.org/keywords/feature-selection","display_name":"Feature selection","score":0.49094465374946594},{"id":"https://openalex.org/keywords/categorization","display_name":"Categorization","score":0.47407594323158264},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4737827777862549},{"id":"https://openalex.org/keywords/softmax-function","display_name":"Softmax function","score":0.44983208179473877},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.4158455729484558},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.34145769476890564},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.22041833400726318}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7613009810447693},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5867540836334229},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5580918192863464},{"id":"https://openalex.org/C136886441","wikidata":"https://www.wikidata.org/wiki/Q926129","display_name":"Normalization (sociology)","level":2,"score":0.5158883333206177},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.5060941576957703},{"id":"https://openalex.org/C148483581","wikidata":"https://www.wikidata.org/wiki/Q446488","display_name":"Feature selection","level":2,"score":0.49094465374946594},{"id":"https://openalex.org/C94124525","wikidata":"https://www.wikidata.org/wiki/Q912550","display_name":"Categorization","level":2,"score":0.47407594323158264},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4737827777862549},{"id":"https://openalex.org/C188441871","wikidata":"https://www.wikidata.org/wiki/Q7554146","display_name":"Softmax function","level":3,"score":0.44983208179473877},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4158455729484558},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.34145769476890564},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.22041833400726318},{"id":"https://openalex.org/C19165224","wikidata":"https://www.wikidata.org/wiki/Q23404","display_name":"Anthropology","level":1,"score":0.0},{"id":"https://openalex.org/C144024400","wikidata":"https://www.wikidata.org/wiki/Q21201","display_name":"Sociology","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tce.2023.3345390","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tce.2023.3345390","pdf_url":null,"source":{"id":"https://openalex.org/S126824455","display_name":"IEEE Transactions on Consumer Electronics","issn_l":"0098-3063","issn":["0098-3063","1558-4127"],"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":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Consumer Electronics","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G7558388333","display_name":null,"funder_award_id":"2020R1A2C1012196","funder_id":"https://openalex.org/F4320322120","funder_display_name":"National Research Foundation of Korea"}],"funders":[{"id":"https://openalex.org/F4320322120","display_name":"National Research Foundation of Korea","ror":"https://ror.org/013aysd81"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":48,"referenced_works":["https://openalex.org/W2317515691","https://openalex.org/W2334209830","https://openalex.org/W2408246687","https://openalex.org/W2560070550","https://openalex.org/W2590822257","https://openalex.org/W2593914038","https://openalex.org/W2600702321","https://openalex.org/W2624407581","https://openalex.org/W2788919350","https://openalex.org/W2807021761","https://openalex.org/W2883387735","https://openalex.org/W2907492528","https://openalex.org/W2919821184","https://openalex.org/W2944152053","https://openalex.org/W2962946486","https://openalex.org/W2963695795","https://openalex.org/W2964015378","https://openalex.org/W2979750067","https://openalex.org/W2988589399","https://openalex.org/W3022935508","https://openalex.org/W3039503982","https://openalex.org/W3047630232","https://openalex.org/W3100848837","https://openalex.org/W3118913631","https://openalex.org/W3146711186","https://openalex.org/W3161944108","https://openalex.org/W3210856765","https://openalex.org/W3215122682","https://openalex.org/W4200412796","https://openalex.org/W4206236541","https://openalex.org/W4206425576","https://openalex.org/W4206609219","https://openalex.org/W4214920758","https://openalex.org/W4220726338","https://openalex.org/W4221150249","https://openalex.org/W4280569535","https://openalex.org/W4287757758","https://openalex.org/W4297733535","https://openalex.org/W6726873649","https://openalex.org/W6738964360","https://openalex.org/W6745537798","https://openalex.org/W6748799445","https://openalex.org/W6753331806","https://openalex.org/W6761659004","https://openalex.org/W6767121309","https://openalex.org/W6779961489","https://openalex.org/W6783485119","https://openalex.org/W6810247653"],"related_works":["https://openalex.org/W3107204728","https://openalex.org/W4287591324","https://openalex.org/W4226420367","https://openalex.org/W2980176872","https://openalex.org/W2962876041","https://openalex.org/W3090555870","https://openalex.org/W3108503355","https://openalex.org/W2249953602","https://openalex.org/W2801655600","https://openalex.org/W2912971006"],"abstract_inverted_index":{"Graph":[0,207],"Neural":[1,208],"Networks":[2],"(GNNs)":[3],"is":[4,97,182,216],"one":[5],"of":[6,19,61,64,70,88,155,161,195,243],"the":[7,56,62,68,106,119,146,166,226,233],"most":[8],"essential":[9],"tools":[10],"for":[11,105,172,225],"learning":[12,158],"from":[13,74,198],"graph-structured":[14,26],"data.":[15,27],"A":[16],"wide":[17,153],"range":[18,154],"tasks":[20,108],"has":[21,30],"demonstrated":[22],"their":[23],"usefulness":[24],"in":[25,55,92],"Engineering":[28],"configuration":[29],"progressed":[31],"fundamentally,":[32],"further":[33],"developing":[34],"execution":[35],"on":[36,116,151,165],"different":[37],"forecast":[38,107],"errands.":[39],"Utilizing":[40],"learnable":[41],"weight":[42],"matrices,":[43],"these":[44,162,220],"neural":[45,65,173],"networks":[46,175],"typically":[47],"incorporate":[48],"feature":[49,53],"transformation":[50],"and":[51,67,90,132,192,213],"node":[52,71,227],"aggregation":[54,136],"same":[57],"layer.":[58],"The":[59,112,141,204],"articulateness":[60],"layers":[63,186],"network":[66],"significance":[69],"information":[72],"gathered":[73,197],"various":[75,201],"hops":[76],"are":[77,103,139,176],"complicated":[78],"to":[79,99,245],"analyze.":[80],"Because":[81],"diverse":[82],"graph":[83,174],"datasets":[84,156,224],"exhibit":[85,231],"changeable":[86],"degrees":[87],"heterophily":[89],"homophily":[91],"class":[93],"label":[94],"dissemination,":[95],"it":[96],"necessary":[98],"recognize":[100],"which":[101],"features":[102,131,138],"crucial":[104],"without":[109],"preceding":[110],"knowledge.":[111],"proposed":[113,234],"work":[114],"focus":[115],"demonstrating":[117],"that":[118,133,145,232],"GNN":[120,185,238],"model\u2019s":[121],"efficiency":[122],"can":[123,148],"be":[124,149],"harmed":[125],"by":[126,157,218],"frequently":[127],"employing":[128],"less":[129],"informative":[130],"not":[134],"all":[135],"process":[137],"beneficial.":[140],"experimental":[142],"outcomes":[143],"validated":[144],"performance":[147],"enhanced":[150],"a":[152,190,193,211],"specific":[159],"subsets":[160],"features.":[163],"Based":[164],"observations,":[167],"several":[168],"significant":[169,241],"design":[170],"concepts":[171],"being":[177],"introduce.":[178],"More":[179],"specifically,":[180],"L2-Normalization":[181],"employed":[183],"over":[184],"using":[187],"SoftMax":[188],"as":[189],"regularize":[191],"\u201csoft-selector\u201d":[194],"characteristics":[196],"neighbors":[199],"at":[200],"hop":[202],"distances.":[203],"Feature":[205],"Selection":[206],"Network":[209],"(FSGNN),":[210],"straightforward":[212],"shallow":[214],"model,":[215],"framed":[217],"combining":[219],"approaches.":[221],"Nine":[222],"standard":[223],"classification":[228],"task":[229],"empirically":[230],"model":[235],"outperforms":[236],"current":[237],"models,":[239],"with":[240],"improvements":[242],"up":[244],"50.80%.":[246]},"counts_by_year":[{"year":2025,"cited_by_count":3}],"updated_date":"2026-03-27T05:58:40.876381","created_date":"2025-10-10T00:00:00"}