{"id":"https://openalex.org/W4378194932","doi":"https://doi.org/10.1109/tce.2023.3279836","title":"Deep Self-Supervised Graph Attention Convolution Autoencoder for Networks Clustering","display_name":"Deep Self-Supervised Graph Attention Convolution Autoencoder for Networks Clustering","publication_year":2023,"publication_date":"2023-05-25","ids":{"openalex":"https://openalex.org/W4378194932","doi":"https://doi.org/10.1109/tce.2023.3279836"},"language":"en","primary_location":{"id":"doi:10.1109/tce.2023.3279836","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tce.2023.3279836","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/A5114911427","display_name":"Chao Chen","orcid":"https://orcid.org/0000-0003-4649-4061"},"institutions":[{"id":"https://openalex.org/I115592961","display_name":"Jiangsu University","ror":"https://ror.org/03jc41j30","country_code":"CN","type":"education","lineage":["https://openalex.org/I115592961"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Chao Chen","raw_affiliation_strings":["School of Computer Science and Communication Engineering, Jiangsu University, Zhenjiang, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Computer Science and Communication Engineering, Jiangsu University, Zhenjiang, China","institution_ids":["https://openalex.org/I115592961"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5056659804","display_name":"Hu Lu","orcid":"https://orcid.org/0000-0003-0350-4055"},"institutions":[{"id":"https://openalex.org/I115592961","display_name":"Jiangsu University","ror":"https://ror.org/03jc41j30","country_code":"CN","type":"education","lineage":["https://openalex.org/I115592961"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Hu Lu","raw_affiliation_strings":["School of Computer Science and Communication Engineering, Jiangsu University, Zhenjiang, China"],"raw_orcid":"https://orcid.org/0000-0003-0350-4055","affiliations":[{"raw_affiliation_string":"School of Computer Science and Communication Engineering, Jiangsu University, Zhenjiang, China","institution_ids":["https://openalex.org/I115592961"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5063697092","display_name":"Haotian Hong","orcid":"https://orcid.org/0009-0004-0452-1230"},"institutions":[{"id":"https://openalex.org/I115592961","display_name":"Jiangsu University","ror":"https://ror.org/03jc41j30","country_code":"CN","type":"education","lineage":["https://openalex.org/I115592961"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Haotian Hong","raw_affiliation_strings":["School of Computer Science and Communication Engineering, Jiangsu University, Zhenjiang, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Computer Science and Communication Engineering, Jiangsu University, Zhenjiang, China","institution_ids":["https://openalex.org/I115592961"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100771674","display_name":"Hai Wang","orcid":"https://orcid.org/0000-0002-9136-8091"},"institutions":[{"id":"https://openalex.org/I115592961","display_name":"Jiangsu University","ror":"https://ror.org/03jc41j30","country_code":"CN","type":"education","lineage":["https://openalex.org/I115592961"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Hai Wang","raw_affiliation_strings":["School of Automotive and Traffic Engineering, Jiangsu University, Zhenjiang, China"],"raw_orcid":"https://orcid.org/0000-0002-9136-8091","affiliations":[{"raw_affiliation_string":"School of Automotive and Traffic Engineering, Jiangsu University, Zhenjiang, China","institution_ids":["https://openalex.org/I115592961"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5003203521","display_name":"Shaohua Wan","orcid":"https://orcid.org/0000-0001-7013-9081"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Shaohua Wan","raw_affiliation_strings":["Shenzhen Institute for Advanced Study, University of Electronic Science and Technology of China, Shenzhen, China"],"raw_orcid":"https://orcid.org/0000-0001-7013-9081","affiliations":[{"raw_affiliation_string":"Shenzhen Institute for Advanced Study, University of Electronic Science and Technology of China, Shenzhen, China","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":4.9174,"has_fulltext":false,"cited_by_count":31,"citation_normalized_percentile":{"value":0.95926433,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":94,"max":100},"biblio":{"volume":"69","issue":"4","first_page":"974","last_page":"983"},"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/T10203","display_name":"Recommender Systems and Techniques","score":0.9980000257492065,"subfield":{"id":"https://openalex.org/subfields/1710","display_name":"Information Systems"},"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/T10064","display_name":"Complex Network Analysis Techniques","score":0.9975000023841858,"subfield":{"id":"https://openalex.org/subfields/3109","display_name":"Statistical and Nonlinear Physics"},"field":{"id":"https://openalex.org/fields/31","display_name":"Physics and Astronomy"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/autoencoder","display_name":"Autoencoder","score":0.8144709467887878},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7584820985794067},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7017310261726379},{"id":"https://openalex.org/keywords/cluster-analysis","display_name":"Cluster analysis","score":0.6945890188217163},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5732008814811707},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5657922029495239},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.5205498933792114},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.46884286403656006},{"id":"https://openalex.org/keywords/unsupervised-learning","display_name":"Unsupervised learning","score":0.429002046585083},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3285753130912781},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.16835790872573853}],"concepts":[{"id":"https://openalex.org/C101738243","wikidata":"https://www.wikidata.org/wiki/Q786435","display_name":"Autoencoder","level":3,"score":0.8144709467887878},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7584820985794067},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7017310261726379},{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.6945890188217163},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5732008814811707},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5657922029495239},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.5205498933792114},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.46884286403656006},{"id":"https://openalex.org/C8038995","wikidata":"https://www.wikidata.org/wiki/Q1152135","display_name":"Unsupervised learning","level":2,"score":0.429002046585083},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3285753130912781},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.16835790872573853}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tce.2023.3279836","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tce.2023.3279836","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":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":44,"referenced_works":["https://openalex.org/W143174683","https://openalex.org/W1826417238","https://openalex.org/W2116341502","https://openalex.org/W2121947440","https://openalex.org/W2153959628","https://openalex.org/W2154851992","https://openalex.org/W2161494102","https://openalex.org/W2405933695","https://openalex.org/W2415243320","https://openalex.org/W2590822257","https://openalex.org/W2765741717","https://openalex.org/W2765811365","https://openalex.org/W2787740662","https://openalex.org/W2788919350","https://openalex.org/W2808409763","https://openalex.org/W2963521811","https://openalex.org/W2964015378","https://openalex.org/W2964732194","https://openalex.org/W2966502719","https://openalex.org/W2997574889","https://openalex.org/W2998269939","https://openalex.org/W3008932288","https://openalex.org/W3094034726","https://openalex.org/W3104097132","https://openalex.org/W3136526008","https://openalex.org/W3165608758","https://openalex.org/W3199612398","https://openalex.org/W3210535434","https://openalex.org/W4211197802","https://openalex.org/W4214814755","https://openalex.org/W4225674624","https://openalex.org/W4297733535","https://openalex.org/W4322614756","https://openalex.org/W4367663507","https://openalex.org/W6678914141","https://openalex.org/W6681096077","https://openalex.org/W6684578312","https://openalex.org/W6685380521","https://openalex.org/W6690230747","https://openalex.org/W6726873649","https://openalex.org/W6730084236","https://openalex.org/W6748402259","https://openalex.org/W6758990916","https://openalex.org/W6774485418"],"related_works":["https://openalex.org/W3013693939","https://openalex.org/W2669956259","https://openalex.org/W4249005693","https://openalex.org/W2997921738","https://openalex.org/W4220926404","https://openalex.org/W2806873178","https://openalex.org/W3123344745","https://openalex.org/W2770818364","https://openalex.org/W2965146396","https://openalex.org/W2891286602"],"abstract_inverted_index":{"In":[0,85],"recent":[1],"years,":[2],"graph-based":[3],"deep":[4,200],"learning":[5,29,168],"algorithms":[6,197],"have":[7,207],"attracted":[8],"widespread":[9],"attention":[10,96],"in":[11],"the":[12,20,37,41,72,86,102,128,137,141,144,153,157,163,171,176,193,210,215,229],"field":[13],"of":[14,19,40,92,123,140,156],"consumer":[15,224],"electronics.":[16],"Still,":[17],"most":[18],"current":[21],"graph":[22,95,201],"neural":[23,202],"networks":[24,68],"are":[25,119],"based":[26],"on":[27,36,152],"supervised":[28],"or":[30],"semi-supervised":[31],"learning,":[32],"which":[33],"often":[34],"relies":[35],"true":[38,154],"labels":[39,139],"given":[42],"samples":[43,93,115],"as":[44,121],"auxiliary":[45],"information.":[46],"To":[47],"solve":[48],"this":[49],"problem,":[50],"we":[51,89],"propose":[52],"a":[53,78,82,110],"Deep":[54],"Self-Supervised":[55],"Attention":[56],"Convolution":[57],"Autoencoder":[58],"Graph":[59],"Clustering":[60],"(DSAGC)":[61],"model":[62,74,135,148,178,212],"and":[63,81,98,116,161,198],"use":[64],"it":[65],"for":[66,126,159,223],"social":[67,184],"clustering.":[69],"We":[70,174,227],"divide":[71],"proposed":[73,103,177,211,218],"into":[75],"two":[76],"parts:":[77],"pretext":[79,87],"task":[80,125,130],"downstream":[83,124,129,145],"task.":[84,146],"task,":[88],"obtain":[90],"pseudo-labels":[91,118,164],"by":[94,166],"autoencoder":[97],"clustering,":[99],"then":[100],"adopt":[101],"reliable":[104],"sample":[105,142,158],"selection":[106],"mechanism":[107],"to":[108,131,169,179,187],"gain":[109],"high":[111],"confidential":[112],"sample.":[113],"These":[114],"corresponding":[117],"selected":[120],"input":[122],"helping":[127],"finish":[132],"training.":[133],"The":[134,217],"obtains":[136],"predictive":[138],"from":[143],"This":[147],"does":[149],"not":[150],"depend":[151],"label":[155],"training":[160],"uses":[162],"produced":[165],"unsupervised":[167,195],"improve":[170],"clustering":[172,196],"performance.":[173,190],"apply":[175],"three":[180],"commonly":[181],"used":[182,222],"public":[183],"network":[185,203],"datasets":[186],"test":[188],"its":[189],"Compared":[191],"with":[192],"presented":[194],"other":[199],"algorithms,":[204],"various":[205],"metrics":[206],"shown":[208],"that":[209],"consistently":[213],"outperforms":[214],"state-of-the-art.":[216],"method":[219],"can":[220],"be":[221],"behavior":[225],"analysis.":[226],"release":[228],"source":[230],"code":[231],"at":[232],"<uri":[233],"xmlns:mml=\"http://www.w3.org/1998/Math/MathML\"":[234],"xmlns:xlink=\"http://www.w3.org/1999/xlink\">https://github.com/hulu88/DSAGC</uri>":[235],".":[236]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":12},{"year":2024,"cited_by_count":15},{"year":2023,"cited_by_count":3}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
