{"id":"https://openalex.org/W3004946360","doi":"https://doi.org/10.1145/3366423.3380214","title":"Structural Deep Clustering Network","display_name":"Structural Deep Clustering Network","publication_year":2020,"publication_date":"2020-04-20","ids":{"openalex":"https://openalex.org/W3004946360","doi":"https://doi.org/10.1145/3366423.3380214","mag":"3004946360"},"language":"en","primary_location":{"id":"doi:10.1145/3366423.3380214","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3366423.3380214","pdf_url":null,"source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of The Web Conference 2020","raw_type":"proceedings-article"},"type":"article","indexed_in":["arxiv","crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.1145/3366423.3380214","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"Deyu Bo","orcid":null},"institutions":[{"id":"https://openalex.org/I139759216","display_name":"Beijing University of Posts and Telecommunications","ror":"https://ror.org/04w9fbh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I139759216"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Deyu Bo","raw_affiliation_strings":["Beijing University of Posts and Telecommunications"],"affiliations":[{"raw_affiliation_string":"Beijing University of Posts and Telecommunications","institution_ids":["https://openalex.org/I139759216"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Xiao Wang","orcid":null},"institutions":[{"id":"https://openalex.org/I139759216","display_name":"Beijing University of Posts and Telecommunications","ror":"https://ror.org/04w9fbh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I139759216"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiao Wang","raw_affiliation_strings":["Beijing University of Posts and Telecommunications"],"affiliations":[{"raw_affiliation_string":"Beijing University of Posts and Telecommunications","institution_ids":["https://openalex.org/I139759216"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Chuan Shi","orcid":null},"institutions":[{"id":"https://openalex.org/I139759216","display_name":"Beijing University of Posts and Telecommunications","ror":"https://ror.org/04w9fbh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I139759216"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Chuan Shi","raw_affiliation_strings":["Beijing University of Posts and Telecommunications"],"affiliations":[{"raw_affiliation_string":"Beijing University of Posts and Telecommunications","institution_ids":["https://openalex.org/I139759216"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Meiqi Zhu","orcid":null},"institutions":[{"id":"https://openalex.org/I139759216","display_name":"Beijing University of Posts and Telecommunications","ror":"https://ror.org/04w9fbh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I139759216"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Meiqi Zhu","raw_affiliation_strings":["Beijing University of Posts and Telecommunications"],"affiliations":[{"raw_affiliation_string":"Beijing University of Posts and Telecommunications","institution_ids":["https://openalex.org/I139759216"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Emiao Lu","orcid":null},"institutions":[{"id":"https://openalex.org/I2250653659","display_name":"Tencent (China)","ror":"https://ror.org/00hhjss72","country_code":"CN","type":"company","lineage":["https://openalex.org/I2250653659"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Emiao Lu","raw_affiliation_strings":["Tencent Ltd"],"affiliations":[{"raw_affiliation_string":"Tencent Ltd","institution_ids":["https://openalex.org/I2250653659"]}]},{"author_position":"last","author":{"id":null,"display_name":"Peng Cui","orcid":null},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Peng Cui","raw_affiliation_strings":["Tsinghua University"],"affiliations":[{"raw_affiliation_string":"Tsinghua University","institution_ids":["https://openalex.org/I99065089"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":6,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I139759216"],"apc_list":null,"apc_paid":null,"fwci":30.8581,"has_fulltext":false,"cited_by_count":475,"citation_normalized_percentile":{"value":0.99754385,"is_in_top_1_percent":true,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":97,"max":100},"biblio":{"volume":null,"issue":null,"first_page":"1400","last_page":"1410"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11273","display_name":"Advanced Graph Neural Networks","score":0.9998999834060669,"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":0.9998999834060669,"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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.9954000115394592,"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/T11550","display_name":"Text and Document Classification Technologies","score":0.9954000115394592,"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":0.7998999953269958},{"id":"https://openalex.org/keywords/cluster-analysis","display_name":"Cluster analysis","score":0.7879999876022339},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.6722999811172485},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.5012000203132629},{"id":"https://openalex.org/keywords/feature-learning","display_name":"Feature learning","score":0.4964999854564667},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.42719998955726624},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.4268999993801117},{"id":"https://openalex.org/keywords/external-data-representation","display_name":"External Data Representation","score":0.4244999885559082}],"concepts":[{"id":"https://openalex.org/C101738243","wikidata":"https://www.wikidata.org/wiki/Q786435","display_name":"Autoencoder","level":3,"score":0.7998999953269958},{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.7879999876022339},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7253000140190125},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7081999778747559},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.6722999811172485},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.5012000203132629},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.4964999854564667},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.42719998955726624},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.4268999993801117},{"id":"https://openalex.org/C116409475","wikidata":"https://www.wikidata.org/wiki/Q1385056","display_name":"External Data Representation","level":2,"score":0.4244999885559082},{"id":"https://openalex.org/C2776036281","wikidata":"https://www.wikidata.org/wiki/Q48769818","display_name":"Constraint (computer-aided design)","level":2,"score":0.3882000148296356},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3531999886035919},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.35269999504089355},{"id":"https://openalex.org/C2776135515","wikidata":"https://www.wikidata.org/wiki/Q17143721","display_name":"Regularization (linguistics)","level":2,"score":0.34630000591278076},{"id":"https://openalex.org/C2984842247","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep neural networks","level":3,"score":0.3294000029563904},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.31859999895095825},{"id":"https://openalex.org/C184509293","wikidata":"https://www.wikidata.org/wiki/Q5136711","display_name":"Clustering high-dimensional data","level":3,"score":0.30809998512268066},{"id":"https://openalex.org/C94641424","wikidata":"https://www.wikidata.org/wiki/Q5172845","display_name":"Correlation clustering","level":3,"score":0.30489999055862427},{"id":"https://openalex.org/C22047676","wikidata":"https://www.wikidata.org/wiki/Q898680","display_name":"Clustering coefficient","level":3,"score":0.30480000376701355},{"id":"https://openalex.org/C97385483","wikidata":"https://www.wikidata.org/wiki/Q16954980","display_name":"Deep belief network","level":3,"score":0.3018999993801117},{"id":"https://openalex.org/C125411270","wikidata":"https://www.wikidata.org/wiki/Q18653","display_name":"Encoding (memory)","level":2,"score":0.2955000102519989},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.29280000925064087},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.2750999927520752},{"id":"https://openalex.org/C105611402","wikidata":"https://www.wikidata.org/wiki/Q2976589","display_name":"Spectral clustering","level":3,"score":0.27309998869895935}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1145/3366423.3380214","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3366423.3380214","pdf_url":null,"source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of The Web Conference 2020","raw_type":"proceedings-article"},{"id":"pmh:oai:arXiv.org:2002.01633","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2002.01633","pdf_url":"https://arxiv.org/pdf/2002.01633","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"}],"best_oa_location":{"id":"doi:10.1145/3366423.3380214","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3366423.3380214","pdf_url":null,"source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of The Web Conference 2020","raw_type":"proceedings-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":12,"referenced_works":["https://openalex.org/W1987971958","https://openalex.org/W2025768430","https://openalex.org/W2057907879","https://openalex.org/W2129793592","https://openalex.org/W2150102617","https://openalex.org/W2730106296","https://openalex.org/W2757686304","https://openalex.org/W2783863635","https://openalex.org/W2800017313","https://openalex.org/W2883725317","https://openalex.org/W2911286998","https://openalex.org/W2964732194"],"related_works":[],"abstract_inverted_index":{"Clustering":[0,109],"is":[1,56,66],"a":[2,57,106,123,139,199],"fundamental":[3],"task":[4],"in":[5,86,99,211],"data":[6,74],"analysis.":[7],"Recently,":[8],"deep":[9,16,28,44,63,118,148],"clustering,":[10],"which":[11,82],"derives":[12],"inspiration":[13],"primarily":[14],"from":[15,72,167],"learning":[17,50],"approaches,":[18],"achieves":[19],"state-of-the-art":[20,228],"performance":[21],"and":[22,138,151,204],"has":[23],"attracted":[24],"considerable":[25],"attention.":[26],"Current":[27],"clustering":[29,34,55,64],"methods":[30,65],"usually":[31],"boost":[32],"the":[33,39,69,73,78,91,101,114,128,134,153,156,162,175,185,190,195,208,227],"results":[35],"by":[36,90,131,179],"means":[37],"of":[38,43,62,80,94,155,165],"powerful":[40],"representation":[41,53,87,197],"ability":[42],"learning,":[45],"e.g.,":[46],"autoencoder,":[47],"suggesting":[48],"that":[49,218],"an":[51],"effective":[52],"for":[54],"crucial":[58],"requirement.":[59],"The":[60],"strength":[61],"to":[67,112,126,133,143,169],"extract":[68],"useful":[70],"representations":[71,129,177],"itself,":[75],"rather":[76],"than":[77],"structure":[79],"data,":[81,166],"receives":[83],"scarce":[84],"attention":[85],"learning.":[88],"Motivated":[89],"great":[92],"success":[93],"Graph":[95],"Convolutional":[96],"Network":[97,110],"(GCN)":[98],"encoding":[100],"graph":[102,201],"structure,":[103],"we":[104,121,182,216],"propose":[105,220],"Structural":[107],"Deep":[108],"(SDCN)":[111],"integrate":[113],"structural":[115],"information":[116],"into":[117],"clustering.":[119],"Specifically,":[120],"design":[122],"delivery":[124,186,191],"operator":[125],"transfer":[127],"learned":[130,178],"autoencoder":[132,205],"corresponding":[135],"GCN":[136,193],"layer,":[137],"dual":[140],"self-supervised":[141],"mechanism":[142],"unify":[144],"these":[145],"two":[146],"different":[147],"neural":[149],"architectures":[150],"guide":[152],"update":[154],"whole":[157],"model.":[158],"In":[159],"this":[160],"way,":[161],"multiple":[163,176],"structures":[164],"low-order":[168],"high-order,":[170],"are":[171],"naturally":[172],"combined":[173],"with":[174,189],"autoencoder.":[180],"Furthermore,":[181],"theoretically":[183],"analyze":[184],"operator,":[187,192],"i.e.,":[188],"improves":[194],"autoencoder-specific":[196],"as":[198],"high-order":[200],"regularization":[202],"constraint":[203],"helps":[206],"alleviate":[207],"over-smoothing":[209],"problem":[210],"GCN.":[212],"Through":[213],"comprehensive":[214],"experiments,":[215],"demonstrate":[217],"our":[219],"model":[221],"can":[222],"consistently":[223],"perform":[224],"better":[225],"over":[226],"techniques.":[229]},"counts_by_year":[{"year":2026,"cited_by_count":14},{"year":2025,"cited_by_count":122},{"year":2024,"cited_by_count":112},{"year":2023,"cited_by_count":107},{"year":2022,"cited_by_count":78},{"year":2021,"cited_by_count":37},{"year":2020,"cited_by_count":5}],"updated_date":"2026-04-10T15:06:20.359241","created_date":"2020-02-14T00:00:00"}
