{"id":"https://openalex.org/W2964457099","doi":"https://doi.org/10.1109/tbdata.2019.2931532","title":"Fast Compressive Spectral Clustering for Large-Scale Sparse Graph","display_name":"Fast Compressive Spectral Clustering for Large-Scale Sparse Graph","publication_year":2019,"publication_date":"2019-07-29","ids":{"openalex":"https://openalex.org/W2964457099","doi":"https://doi.org/10.1109/tbdata.2019.2931532","mag":"2964457099"},"language":"en","primary_location":{"id":"doi:10.1109/tbdata.2019.2931532","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tbdata.2019.2931532","pdf_url":null,"source":{"id":"https://openalex.org/S2491400915","display_name":"IEEE Transactions on Big Data","issn_l":"2332-7790","issn":["2332-7790","2372-2096"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320439","host_organization_name":"IEEE Computer Society","host_organization_lineage":["https://openalex.org/P4310320439","https://openalex.org/P4310319808"],"host_organization_lineage_names":["IEEE Computer Society","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 Big Data","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/A5100416893","display_name":"Ting Li","orcid":"https://orcid.org/0000-0002-9593-7650"},"institutions":[{"id":"https://openalex.org/I4210103986","display_name":"Jingdong (China)","ror":"https://ror.org/01dkjkq64","country_code":"CN","type":"company","lineage":["https://openalex.org/I4210103986"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Ting Li","raw_affiliation_strings":["NiceX Lab and AI Platform Division of Urban Computing Business Unit, JingDong Digits, Beijing Economic and Technological Development Area, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"NiceX Lab and AI Platform Division of Urban Computing Business Unit, JingDong Digits, Beijing Economic and Technological Development Area, Beijing, China","institution_ids":["https://openalex.org/I4210103986"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100395351","display_name":"Yiming Zhang","orcid":"https://orcid.org/0000-0001-6450-8485"},"institutions":[{"id":"https://openalex.org/I183067930","display_name":"Shanghai Jiao Tong University","ror":"https://ror.org/0220qvk04","country_code":"CN","type":"education","lineage":["https://openalex.org/I183067930"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yiming Zhang","raw_affiliation_strings":["Department of Computer Science and Engineering, NiceX Lab, Shanghai Jiao Tong University, Shanghai, China"],"raw_orcid":"https://orcid.org/0000-0001-6450-8485","affiliations":[{"raw_affiliation_string":"Department of Computer Science and Engineering, NiceX Lab, Shanghai Jiao Tong University, Shanghai, China","institution_ids":["https://openalex.org/I183067930"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100458897","display_name":"Hao Liu","orcid":"https://orcid.org/0000-0003-4271-1567"},"institutions":[{"id":"https://openalex.org/I4210129579","display_name":"National Engineering Laboratory of Deep Learning Technology and Application","ror":"https://ror.org/03z8p5796","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4210129579"]},{"id":"https://openalex.org/I98301712","display_name":"Baidu (China)","ror":"https://ror.org/03vs3wt56","country_code":"CN","type":"company","lineage":["https://openalex.org/I98301712"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Hao Liu","raw_affiliation_strings":["Business Intelligence Lab, Baidu Research, National Engineering Laboratory of Deep Learning Technology and Application, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0003-4271-1567","affiliations":[{"raw_affiliation_string":"Business Intelligence Lab, Baidu Research, National Engineering Laboratory of Deep Learning Technology and Application, Beijing, China","institution_ids":["https://openalex.org/I4210129579","https://openalex.org/I98301712"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101490654","display_name":"Guangtao Xue","orcid":"https://orcid.org/0000-0002-1617-3593"},"institutions":[{"id":"https://openalex.org/I183067930","display_name":"Shanghai Jiao Tong University","ror":"https://ror.org/0220qvk04","country_code":"CN","type":"education","lineage":["https://openalex.org/I183067930"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Guangtao Xue","raw_affiliation_strings":["Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai, China","institution_ids":["https://openalex.org/I183067930"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100343991","display_name":"Ling Liu","orcid":"https://orcid.org/0000-0002-4138-3082"},"institutions":[{"id":"https://openalex.org/I130701444","display_name":"Georgia Institute of Technology","ror":"https://ror.org/01zkghx44","country_code":"US","type":"education","lineage":["https://openalex.org/I130701444"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Ling Liu","raw_affiliation_strings":["School of Computer Science, Georgia Institute of Technology, Atlanta, GA, USA"],"raw_orcid":"https://orcid.org/0000-0002-4138-3082","affiliations":[{"raw_affiliation_string":"School of Computer Science, Georgia Institute of Technology, Atlanta, GA, USA","institution_ids":["https://openalex.org/I130701444"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":5,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.2464,"has_fulltext":false,"cited_by_count":7,"citation_normalized_percentile":{"value":0.49570254,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":97},"biblio":{"volume":"8","issue":"1","first_page":"193","last_page":"202"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10064","display_name":"Complex Network Analysis Techniques","score":0.9950000047683716,"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"}},"topics":[{"id":"https://openalex.org/T10064","display_name":"Complex Network Analysis Techniques","score":0.9950000047683716,"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"}},{"id":"https://openalex.org/T11273","display_name":"Advanced Graph Neural Networks","score":0.9937000274658203,"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":0.9803000092506409,"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/spectral-clustering","display_name":"Spectral clustering","score":0.814271867275238},{"id":"https://openalex.org/keywords/cluster-analysis","display_name":"Cluster analysis","score":0.778806209564209},{"id":"https://openalex.org/keywords/laplacian-matrix","display_name":"Laplacian matrix","score":0.6879310011863708},{"id":"https://openalex.org/keywords/compressed-sensing","display_name":"Compressed sensing","score":0.6084470748901367},{"id":"https://openalex.org/keywords/adjacency-matrix","display_name":"Adjacency matrix","score":0.5771257877349854},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5655044913291931},{"id":"https://openalex.org/keywords/eigenvalues-and-eigenvectors","display_name":"Eigenvalues and eigenvectors","score":0.5409418344497681},{"id":"https://openalex.org/keywords/sparse-matrix","display_name":"Sparse matrix","score":0.46719691157341003},{"id":"https://openalex.org/keywords/correlation-clustering","display_name":"Correlation clustering","score":0.4167637228965759},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.4149324297904968},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.3995038866996765},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.36685729026794434},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.33846157789230347},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.29521724581718445},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.23498547077178955}],"concepts":[{"id":"https://openalex.org/C105611402","wikidata":"https://www.wikidata.org/wiki/Q2976589","display_name":"Spectral clustering","level":3,"score":0.814271867275238},{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.778806209564209},{"id":"https://openalex.org/C115178988","wikidata":"https://www.wikidata.org/wiki/Q772067","display_name":"Laplacian matrix","level":3,"score":0.6879310011863708},{"id":"https://openalex.org/C124851039","wikidata":"https://www.wikidata.org/wiki/Q2665459","display_name":"Compressed sensing","level":2,"score":0.6084470748901367},{"id":"https://openalex.org/C180356752","wikidata":"https://www.wikidata.org/wiki/Q727035","display_name":"Adjacency matrix","level":3,"score":0.5771257877349854},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5655044913291931},{"id":"https://openalex.org/C158693339","wikidata":"https://www.wikidata.org/wiki/Q190524","display_name":"Eigenvalues and eigenvectors","level":2,"score":0.5409418344497681},{"id":"https://openalex.org/C56372850","wikidata":"https://www.wikidata.org/wiki/Q1050404","display_name":"Sparse matrix","level":3,"score":0.46719691157341003},{"id":"https://openalex.org/C94641424","wikidata":"https://www.wikidata.org/wiki/Q5172845","display_name":"Correlation clustering","level":3,"score":0.4167637228965759},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.4149324297904968},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3995038866996765},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.36685729026794434},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.33846157789230347},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.29521724581718445},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.23498547077178955},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tbdata.2019.2931532","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tbdata.2019.2931532","pdf_url":null,"source":{"id":"https://openalex.org/S2491400915","display_name":"IEEE Transactions on Big Data","issn_l":"2332-7790","issn":["2332-7790","2372-2096"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320439","host_organization_name":"IEEE Computer Society","host_organization_lineage":["https://openalex.org/P4310320439","https://openalex.org/P4310319808"],"host_organization_lineage_names":["IEEE Computer Society","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 Big Data","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/9","score":0.46000000834465027,"display_name":"Industry, innovation and infrastructure"}],"awards":[{"id":"https://openalex.org/G1748265143","display_name":null,"funder_award_id":"U1736207","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G2702767172","display_name":null,"funder_award_id":"NSF 1547102","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G4074552307","display_name":"\u4ee5\u7528\u6237\u4f53\u9a8c\u4e3a\u4e2d\u5fc3\u7684\u79fb\u52a8\u901a\u4fe1\u7f51\u7edc\u6027\u80fd\u4f18\u5316\u5173\u952e\u6280\u672f\u7814\u7a76","funder_award_id":"61572324","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G4765491843","display_name":"\u9762\u5411\u52a8\u6001\u5b9e\u65f6\u4eba\u5de5\u667a\u80fd\u5e94\u7528\u7684\u5927\u89c4\u6a21\u673a\u5668\u5b66\u4e60\u7cfb\u7edf\u534f\u540c\u8c03\u5ea6\u6280\u672f\u7814\u7a76","funder_award_id":"61872376","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G5206010609","display_name":"TWC: Medium: Privacy Preserving Computation in Big Data Clouds","funder_award_id":"1564097","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G6231216505","display_name":null,"funder_award_id":"61772541","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"},{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":42,"referenced_works":["https://openalex.org/W137313310","https://openalex.org/W1485063553","https://openalex.org/W2037757210","https://openalex.org/W2037783202","https://openalex.org/W2047244756","https://openalex.org/W2051549110","https://openalex.org/W2055663168","https://openalex.org/W2073737032","https://openalex.org/W2107042959","https://openalex.org/W2109726592","https://openalex.org/W2116810533","https://openalex.org/W2119456804","https://openalex.org/W2132914434","https://openalex.org/W2134370969","https://openalex.org/W2179147795","https://openalex.org/W2329958935","https://openalex.org/W2342588180","https://openalex.org/W2382141519","https://openalex.org/W2740420891","https://openalex.org/W2771379243","https://openalex.org/W2788096725","https://openalex.org/W2803475843","https://openalex.org/W2808858332","https://openalex.org/W2953879634","https://openalex.org/W2962947277","https://openalex.org/W2963169996","https://openalex.org/W2997188720","https://openalex.org/W4295161942","https://openalex.org/W4298154644","https://openalex.org/W4312258136","https://openalex.org/W6629836199","https://openalex.org/W6632724306","https://openalex.org/W6639623841","https://openalex.org/W6674539747","https://openalex.org/W6680408641","https://openalex.org/W6682541512","https://openalex.org/W6686271493","https://openalex.org/W6692113227","https://openalex.org/W6719772885","https://openalex.org/W6729311237","https://openalex.org/W6737307392","https://openalex.org/W6977528248"],"related_works":["https://openalex.org/W2963002212","https://openalex.org/W2584995636","https://openalex.org/W2902850141","https://openalex.org/W2607797544","https://openalex.org/W4321153334","https://openalex.org/W4293871700","https://openalex.org/W1533309322","https://openalex.org/W3168808485","https://openalex.org/W3017750840","https://openalex.org/W4352976663"],"abstract_inverted_index":{"Spectral":[0],"clustering":[1,19,23,159],"(SC)":[2],"is":[3,54,61,130],"an":[4],"unsupervised":[5],"learning":[6],"method":[7,84,90,150],"that":[8,97],"has":[9],"been":[10],"widely":[11],"used":[12],"in":[13,73,137],"industrial":[14],"product":[15,69],"analysis.":[16],"Compressive":[17],"spectral":[18,87],"(CSC)":[20],"effectively":[21],"accelerates":[22],"by":[24,95,110],"leveraging":[25],"graph":[26],"filter":[27],"and":[28,46,105,146],"random":[29],"sampling":[30],"techniques.":[31],"However,":[32],"CSC":[33],"suffers":[34],"from":[35],"two":[36],"major":[37],"problems.":[38],"First,":[39],"the":[40,44,57,92,98,107,112,133,153,166],"direct":[41],"use":[42],"of":[43,59,128,135,168],"dichotomy":[45],"eigencount":[47],"techniques":[48],"for":[49,70],"estimating":[50],"Laplacian":[51,138],"matrix&#x0027;s$k$kth":[52],"eigenvalue":[53],"expensive.":[55],"Second,":[56],"computation":[58,154],"interpolation":[60],"time-consuming":[62],"because":[63],"it":[64],"requires":[65],"to":[66,120,162],"repeat":[67],"matrix-vector":[68],"every":[71],"cluster":[72],"each":[74],"iteration.":[75],"To":[76],"address":[77],"these":[78],"problems,":[79],"we":[80],"propose":[81],"a":[82],"new":[83],"calledfast":[85],"compressive":[86],"clustering(FCSC).":[88],"Our":[89],"addresses":[91,106],"first":[93],"problem":[94,109],"assuming":[96],"eigenvalues":[99],"approximately":[100],"satisfy":[101],"local":[102],"uniform":[103],"distribution,":[104],"second":[108],"recalculating":[111],"pairwise":[113],"similarity":[114],"between":[115],"nodes":[116],"with":[117,132],"low-dimensional":[118],"representation":[119],"reconstruct":[121],"denoised":[122],"laplacian":[123],"matrix.":[124,139],"The":[125],"time":[126,155],"complexity":[127],"reconstruction":[129],"linear":[131],"number":[134],"non-zeros":[136],"As":[140],"experimentally":[141],"demonstrated":[142],"on":[143],"both":[144],"artificial":[145],"real-world":[147],"datasets,":[148],"our":[149],"significantly":[151],"reduces":[152],"while":[156],"preserving":[157],"high":[158],"accuracy":[160],"comparable":[161],"previous":[163],"designs,":[164],"demonstrating":[165],"effectiveness":[167],"FCSC.":[169]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":1},{"year":2021,"cited_by_count":2}],"updated_date":"2026-07-29T09:40:50.615796","created_date":"2025-10-10T00:00:00"}
