{"id":"https://openalex.org/W4388192715","doi":"https://doi.org/10.1145/3581783.3612190","title":"Multi-view Graph Clustering via Efficient Global-Local Spectral Embedding Fusion","display_name":"Multi-view Graph Clustering via Efficient Global-Local Spectral Embedding Fusion","publication_year":2023,"publication_date":"2023-10-26","ids":{"openalex":"https://openalex.org/W4388192715","doi":"https://doi.org/10.1145/3581783.3612190"},"language":"en","primary_location":{"id":"doi:10.1145/3581783.3612190","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3581783.3612190","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 31st ACM International Conference on Multimedia","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/A5103196395","display_name":"Penglei Wang","orcid":"https://orcid.org/0000-0001-9469-3917"},"institutions":[{"id":"https://openalex.org/I17145004","display_name":"Northwestern Polytechnical University","ror":"https://ror.org/01y0j0j86","country_code":"CN","type":"education","lineage":["https://openalex.org/I17145004"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Penglei Wang","raw_affiliation_strings":["Northwestern Polytechnical University, Xi'an, China"],"raw_orcid":"https://orcid.org/0000-0001-9469-3917","affiliations":[{"raw_affiliation_string":"Northwestern Polytechnical University, Xi'an, China","institution_ids":["https://openalex.org/I17145004"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5027472534","display_name":"Danyang Wu","orcid":"https://orcid.org/0000-0002-0309-1409"},"institutions":[{"id":"https://openalex.org/I87445476","display_name":"Xi'an Jiaotong University","ror":"https://ror.org/017zhmm22","country_code":"CN","type":"education","lineage":["https://openalex.org/I87445476"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Danyang Wu","raw_affiliation_strings":["Xi'an Jiaotong University, Xi'an, China"],"raw_orcid":"https://orcid.org/0000-0002-0309-1409","affiliations":[{"raw_affiliation_string":"Xi'an Jiaotong University, Xi'an, China","institution_ids":["https://openalex.org/I87445476"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5030351224","display_name":"Rong Wang","orcid":"https://orcid.org/0000-0001-9240-6726"},"institutions":[{"id":"https://openalex.org/I17145004","display_name":"Northwestern Polytechnical University","ror":"https://ror.org/01y0j0j86","country_code":"CN","type":"education","lineage":["https://openalex.org/I17145004"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Rong Wang","raw_affiliation_strings":["Northwestern Polytechnical University, Xi'an, China"],"raw_orcid":"https://orcid.org/0000-0001-9240-6726","affiliations":[{"raw_affiliation_string":"Northwestern Polytechnical University, Xi'an, China","institution_ids":["https://openalex.org/I17145004"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5003222421","display_name":"Feiping Nie","orcid":"https://orcid.org/0000-0002-0871-6519"},"institutions":[{"id":"https://openalex.org/I17145004","display_name":"Northwestern Polytechnical University","ror":"https://ror.org/01y0j0j86","country_code":"CN","type":"education","lineage":["https://openalex.org/I17145004"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Feiping Nie","raw_affiliation_strings":["Northwestern Polytechnical University, Xi'an, China"],"raw_orcid":"https://orcid.org/0000-0002-0871-6519","affiliations":[{"raw_affiliation_string":"Northwestern Polytechnical University, Xi'an, China","institution_ids":["https://openalex.org/I17145004"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5103196395"],"corresponding_institution_ids":["https://openalex.org/I17145004"],"apc_list":null,"apc_paid":null,"fwci":1.4959,"has_fulltext":false,"cited_by_count":13,"citation_normalized_percentile":{"value":0.85053905,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":98,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"3268","last_page":"3276"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10057","display_name":"Face and Expression Recognition","score":0.9984999895095825,"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":0.9984999895095825,"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/T10689","display_name":"Remote-Sensing Image Classification","score":0.9872000217437744,"subfield":{"id":"https://openalex.org/subfields/2214","display_name":"Media Technology"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T13731","display_name":"Advanced Computing and Algorithms","score":0.9842000007629395,"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/cluster-analysis","display_name":"Cluster analysis","score":0.7202790975570679},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7051881551742554},{"id":"https://openalex.org/keywords/embedding","display_name":"Embedding","score":0.6862645149230957},{"id":"https://openalex.org/keywords/spectral-clustering","display_name":"Spectral clustering","score":0.6353448629379272},{"id":"https://openalex.org/keywords/discriminative-model","display_name":"Discriminative model","score":0.5706026554107666},{"id":"https://openalex.org/keywords/convergence","display_name":"Convergence (economics)","score":0.469562292098999},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.429991215467453},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.4250674843788147},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4174203872680664},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.37234586477279663},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.3079666197299957}],"concepts":[{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.7202790975570679},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7051881551742554},{"id":"https://openalex.org/C41608201","wikidata":"https://www.wikidata.org/wiki/Q980509","display_name":"Embedding","level":2,"score":0.6862645149230957},{"id":"https://openalex.org/C105611402","wikidata":"https://www.wikidata.org/wiki/Q2976589","display_name":"Spectral clustering","level":3,"score":0.6353448629379272},{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.5706026554107666},{"id":"https://openalex.org/C2777303404","wikidata":"https://www.wikidata.org/wiki/Q759757","display_name":"Convergence (economics)","level":2,"score":0.469562292098999},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.429991215467453},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.4250674843788147},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4174203872680664},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.37234586477279663},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.3079666197299957},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.0},{"id":"https://openalex.org/C50522688","wikidata":"https://www.wikidata.org/wiki/Q189833","display_name":"Economic growth","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3581783.3612190","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3581783.3612190","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 31st ACM International Conference on Multimedia","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G3539416056","display_name":null,"funder_award_id":"2022M722532","funder_id":"https://openalex.org/F4320321543","funder_display_name":"China Postdoctoral Science Foundation"},{"id":"https://openalex.org/G6873721585","display_name":null,"funder_award_id":"62276212","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320321543","display_name":"China Postdoctoral Science Foundation","ror":"https://ror.org/0426zh255"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":38,"referenced_works":["https://openalex.org/W1975172027","https://openalex.org/W2097308346","https://openalex.org/W2134529554","https://openalex.org/W2149380762","https://openalex.org/W2149857793","https://openalex.org/W2155904486","https://openalex.org/W2332282745","https://openalex.org/W2740464254","https://openalex.org/W2756626360","https://openalex.org/W2895210058","https://openalex.org/W2921065608","https://openalex.org/W2965510190","https://openalex.org/W2972477132","https://openalex.org/W2981206386","https://openalex.org/W2996771119","https://openalex.org/W2997739739","https://openalex.org/W3008644971","https://openalex.org/W3024343454","https://openalex.org/W3035396932","https://openalex.org/W3044063956","https://openalex.org/W3102873796","https://openalex.org/W3116451788","https://openalex.org/W3160331336","https://openalex.org/W3163532483","https://openalex.org/W3176947484","https://openalex.org/W3193406360","https://openalex.org/W3205485865","https://openalex.org/W3205595867","https://openalex.org/W3209503744","https://openalex.org/W4200226646","https://openalex.org/W4205660500","https://openalex.org/W4214624799","https://openalex.org/W4286267876","https://openalex.org/W4304091655","https://openalex.org/W4304091792","https://openalex.org/W4304091814","https://openalex.org/W4304098891","https://openalex.org/W4312238451"],"related_works":["https://openalex.org/W2965546495","https://openalex.org/W4389116644","https://openalex.org/W2153315159","https://openalex.org/W3103844505","https://openalex.org/W259157601","https://openalex.org/W4205463238","https://openalex.org/W2761785940","https://openalex.org/W2110523656","https://openalex.org/W1482912984","https://openalex.org/W2972477132"],"abstract_inverted_index":{"With":[0],"the":[1,15,36,54,59,79,85,120,125,132,145,172],"proliferation":[2],"of":[3,18,61,176],"multimedia":[4],"applications,":[5],"data":[6],"is":[7],"frequently":[8],"derived":[9],"from":[10],"multiple":[11,40],"sources,":[12],"leading":[13],"to":[14,34,68,76,84,88,123,136,179],"accelerated":[16],"advancement":[17],"multi-view":[19],"clustering":[20],"(MVC)":[21],"methods.":[22,183],"In":[23],"this":[24,44],"paper,":[25],"we":[26,150],"propose":[27],"a":[28,48,115],"novel":[29],"MVC":[30,182],"method,":[31],"termed":[32],"GLSEF,":[33,149],"handle":[35],"inconsistency":[37],"existing":[38,180],"in":[39,97,148],"spectral":[41],"embeddings.":[42],"To":[43,143],"end,":[45],"GLSEF":[46,57,82,129,177],"contains":[47],"two-level":[49],"learning":[50],"mechanism.":[51],"Specifically,":[52],"on":[53,167],"global":[55],"level,":[56,81],"considers":[58],"diversity":[60],"features":[62,72],"and":[63,91,94,104,118,160,174],"selectively":[64],"assigns":[65],"smooth":[66],"weights":[67],"partial":[69],"more":[70],"discriminative":[71],"that":[73,113],"are":[74],"conducive":[75],"clustering.":[77,107],"On":[78],"local":[80,95],"resorts":[83],"Grassmann":[86],"manifold":[87],"maintain":[89],"spatial":[90],"topological":[92],"information":[93,139],"structure":[96],"each":[98],"view,":[99],"thereby":[100],"enhancing":[101],"its":[102],"suitability":[103],"accuracy":[105],"for":[106],"Moreover,":[108],"unlike":[109],"most":[110],"previous":[111],"methods":[112],"learn":[114],"low-dimension":[116],"embedding":[117],"perform":[119],"k-means":[121],"algorithm":[122,156],"obtain":[124],"final":[126],"cluster":[127],"labels,":[128],"directly":[130],"acquires":[131],"discrete":[133],"indicator":[134],"matrix":[135],"prevent":[137],"potential":[138],"loss":[140],"during":[141],"post-processing.":[142],"address":[144],"optimization":[146,155],"involved":[147],"present":[151],"an":[152],"efficient":[153],"alternating":[154],"accompanied":[157],"by":[158],"convergence":[159],"time":[161],"complexity":[162],"analyses.":[163],"Extensive":[164],"empirical":[165],"results":[166],"nine":[168],"real-world":[169],"datasets":[170],"demonstrate":[171],"effectiveness":[173],"efficiency":[175],"compared":[178],"state-of-the-art":[181]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2025,"cited_by_count":6},{"year":2024,"cited_by_count":5}],"updated_date":"2026-05-09T13:55:54.758798","created_date":"2025-10-10T00:00:00"}