{"id":"https://openalex.org/W4399257625","doi":"https://doi.org/10.1145/3653781.3653826","title":"Graph Autoencoder Subspace Clustering","display_name":"Graph Autoencoder Subspace Clustering","publication_year":2024,"publication_date":"2024-01-19","ids":{"openalex":"https://openalex.org/W4399257625","doi":"https://doi.org/10.1145/3653781.3653826"},"language":"en","primary_location":{"id":"doi:10.1145/3653781.3653826","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3653781.3653826","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the International Conference on Computer Vision and Deep Learning","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/A5110719637","display_name":"MingBing Yao","orcid":"https://orcid.org/0009-0007-3006-9286"},"institutions":[{"id":"https://openalex.org/I68986083","display_name":"Northwest Normal University","ror":"https://ror.org/00gx3j908","country_code":"CN","type":"education","lineage":["https://openalex.org/I68986083"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Meng Yao","raw_affiliation_strings":["School of Computer Science and Engineering, Northwest Normal University, China"],"raw_orcid":"https://orcid.org/0009-0007-3006-9286","affiliations":[{"raw_affiliation_string":"School of Computer Science and Engineering, Northwest Normal University, China","institution_ids":["https://openalex.org/I68986083"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5110719637"],"corresponding_institution_ids":["https://openalex.org/I68986083"],"apc_list":null,"apc_paid":null,"fwci":0.2381,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.4770874,"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":"5"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10057","display_name":"Face and Expression Recognition","score":0.9987000226974487,"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"}},"topics":[{"id":"https://openalex.org/T10057","display_name":"Face and Expression Recognition","score":0.9987000226974487,"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/T11273","display_name":"Advanced Graph Neural Networks","score":0.9972000122070312,"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/T10637","display_name":"Advanced Clustering Algorithms Research","score":0.9945999979972839,"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/autoencoder","display_name":"Autoencoder","score":0.9137482047080994},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7578994035720825},{"id":"https://openalex.org/keywords/cluster-analysis","display_name":"Cluster analysis","score":0.7257911562919617},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.6296936273574829},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6250123977661133},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5829246640205383},{"id":"https://openalex.org/keywords/subspace-topology","display_name":"Subspace topology","score":0.54827880859375},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.5272487998008728},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.5245823264122009},{"id":"https://openalex.org/keywords/encoder","display_name":"Encoder","score":0.46533361077308655},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.43845269083976746},{"id":"https://openalex.org/keywords/feature-learning","display_name":"Feature learning","score":0.42576146125793457},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.4089623689651489},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.22664788365364075}],"concepts":[{"id":"https://openalex.org/C101738243","wikidata":"https://www.wikidata.org/wiki/Q786435","display_name":"Autoencoder","level":3,"score":0.9137482047080994},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7578994035720825},{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.7257911562919617},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.6296936273574829},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6250123977661133},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5829246640205383},{"id":"https://openalex.org/C32834561","wikidata":"https://www.wikidata.org/wiki/Q660730","display_name":"Subspace topology","level":2,"score":0.54827880859375},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.5272487998008728},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.5245823264122009},{"id":"https://openalex.org/C118505674","wikidata":"https://www.wikidata.org/wiki/Q42586063","display_name":"Encoder","level":2,"score":0.46533361077308655},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.43845269083976746},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.42576146125793457},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4089623689651489},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.22664788365364075},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3653781.3653826","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3653781.3653826","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the International Conference on Computer Vision and Deep Learning","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Industry, innovation and infrastructure","id":"https://metadata.un.org/sdg/9","score":0.47999998927116394}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":8,"referenced_works":["https://openalex.org/W79405465","https://openalex.org/W2017441234","https://openalex.org/W2165874743","https://openalex.org/W2168029744","https://openalex.org/W2963764968","https://openalex.org/W4254300870","https://openalex.org/W6601013545","https://openalex.org/W6684578312"],"related_works":["https://openalex.org/W2983142544","https://openalex.org/W2891059443","https://openalex.org/W4281663961","https://openalex.org/W3208888551","https://openalex.org/W4313561566","https://openalex.org/W3208386644","https://openalex.org/W4389832810","https://openalex.org/W4220682630","https://openalex.org/W3181622257","https://openalex.org/W3163146846"],"abstract_inverted_index":{"Abstract:":[0],"Most":[1],"subspace":[2,87],"clustering":[3,143,151],"networks":[4,51,119],"in":[5,54],"current":[6],"literature":[7],"adopt":[8],"autoencoders":[9],"as":[10,36],"the":[11,18,41,46,72,80,86,114,129],"overall":[12,42],"architecture,":[13],"using":[14,133],"convolutional":[15,29,50,118],"layers":[16,30],"within":[17],"autoencoder":[19],"network":[20],"to":[21,39,120],"capture":[22],"features":[23,66,83],"of":[24,82,104,128],"data":[25,76],"points.":[26],"However,":[27],"shallow":[28],"have":[31],"limited":[32],"feature":[33],"extraction":[34],"ability,":[35],"they":[37],"fail":[38],"consider":[40],"image":[43],"region.":[44],"On":[45],"other":[47],"hand,":[48],"deep":[49],"introduce":[52],"difficulties":[53],"parameter":[55],"tuning":[56],"and":[57],"require":[58],"significant":[59],"computational":[60],"resources":[61],"for":[62],"training.":[63],"Additionally,":[64],"these":[65,90],"do":[67],"not":[68],"take":[69],"into":[70],"account":[71],"structural":[73],"relationships":[74],"among":[75],"points,":[77],"thereby":[78],"impacting":[79],"quality":[81],"extracted":[84,132],"by":[85,123],"network.To":[88],"address":[89],"issues,":[91],"we":[92,107],"propose":[93],"a":[94,134],"novel":[95],"Graph":[96],"Autoencoder":[97],"Subspace":[98],"Clustering":[99],"(GASC)":[100],"model.":[101],"Specifically,":[102],"instead":[103],"traditional":[105],"autoencoders,":[106,112],"replace":[108],"them":[109],"with":[110],"graph":[111,117,130],"where":[113],"encoder":[115],"adopts":[116],"decode":[121],"information":[122],"reconstructing":[124],"edges.":[125],"The":[126],"nodes":[127],"are":[131],"pre-trained":[135],"CNN":[136],"network.":[137],"Experimental":[138],"results":[139],"on":[140],"publicly":[141],"available":[142],"datasets":[144],"demonstrate":[145],"that":[146],"GASC":[147],"outperforms":[148],"most":[149],"existing":[150],"models.":[152]},"counts_by_year":[{"year":2025,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}