{"id":"https://openalex.org/W4409983083","doi":"https://doi.org/10.1137/1.9781611978520.26","title":"Multi-View Spectral Clustering for Graphs with Multiple View Structures","display_name":"Multi-View Spectral Clustering for Graphs with Multiple View Structures","publication_year":2025,"publication_date":"2025-01-01","ids":{"openalex":"https://openalex.org/W4409983083","doi":"https://doi.org/10.1137/1.9781611978520.26"},"language":"en","primary_location":{"id":"doi:10.1137/1.9781611978520.26","is_oa":false,"landing_page_url":"https://doi.org/10.1137/1.9781611978520.26","pdf_url":null,"source":{"id":"https://openalex.org/S4306463922","display_name":"Society for Industrial and Applied Mathematics eBooks","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/P4310320508","host_organization_name":"Society for Industrial and Applied Mathematics","host_organization_lineage":["https://openalex.org/P4310320508"],"host_organization_lineage_names":["Society for Industrial and Applied Mathematics"],"type":"ebook platform"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2025 SIAM International Conference on Data Mining (SDM)","raw_type":"book-chapter"},"type":"book-chapter","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/A5026125754","display_name":"Yorgos Tsitsikas","orcid":"https://orcid.org/0000-0003-2620-9067"},"institutions":[{"id":"https://openalex.org/I103635307","display_name":"University of California, Riverside","ror":"https://ror.org/03nawhv43","country_code":"US","type":"education","lineage":["https://openalex.org/I103635307"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Yorgos Tsitsikas","raw_affiliation_strings":["University of California, Riverside"],"affiliations":[{"raw_affiliation_string":"University of California, Riverside","institution_ids":["https://openalex.org/I103635307"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5054849323","display_name":"Evangelos E. Papalexakis","orcid":"https://orcid.org/0000-0002-3411-8483"},"institutions":[{"id":"https://openalex.org/I103635307","display_name":"University of California, Riverside","ror":"https://ror.org/03nawhv43","country_code":"US","type":"education","lineage":["https://openalex.org/I103635307"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Evangelos E. Papalexakis","raw_affiliation_strings":["University of California, Riverside"],"affiliations":[{"raw_affiliation_string":"University of California, Riverside","institution_ids":["https://openalex.org/I103635307"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5026125754"],"corresponding_institution_ids":["https://openalex.org/I103635307"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.26917217,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"270","last_page":"278"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10064","display_name":"Complex Network Analysis Techniques","score":0.8885999917984009,"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":0.8885999917984009,"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/T12292","display_name":"Graph Theory and Algorithms","score":0.8406999707221985,"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/T10637","display_name":"Advanced Clustering Algorithms Research","score":0.8026000261306763,"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/cluster-analysis","display_name":"Cluster analysis","score":0.5762313604354858},{"id":"https://openalex.org/keywords/spectral-clustering","display_name":"Spectral clustering","score":0.5709874629974365},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5192508697509766},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.22529327869415283}],"concepts":[{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.5762313604354858},{"id":"https://openalex.org/C105611402","wikidata":"https://www.wikidata.org/wiki/Q2976589","display_name":"Spectral clustering","level":3,"score":0.5709874629974365},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5192508697509766},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.22529327869415283}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1137/1.9781611978520.26","is_oa":false,"landing_page_url":"https://doi.org/10.1137/1.9781611978520.26","pdf_url":null,"source":{"id":"https://openalex.org/S4306463922","display_name":"Society for Industrial and Applied Mathematics eBooks","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/P4310320508","host_organization_name":"Society for Industrial and Applied Mathematics","host_organization_lineage":["https://openalex.org/P4310320508"],"host_organization_lineage_names":["Society for Industrial and Applied Mathematics"],"type":"ebook platform"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2025 SIAM International Conference on Data Mining (SDM)","raw_type":"book-chapter"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W4391375266","https://openalex.org/W2899084033","https://openalex.org/W2748952813","https://openalex.org/W2390279801","https://openalex.org/W4391913857","https://openalex.org/W2358668433","https://openalex.org/W4396701345","https://openalex.org/W2376932109","https://openalex.org/W2001405890","https://openalex.org/W1482912984"],"abstract_inverted_index":{"Despite":[0],"the":[1,11,29,71,79,82,92,144,186],"fundamental":[2],"importance":[3],"of":[4,10,25,61,81,88,96,119,125,152,188],"clustering,":[5,127],"to":[6,21,70,91,133,143,190],"this":[7,39,120,149],"day,":[8],"much":[9],"relevant":[12],"research":[13],"is":[14,85,115,164],"still":[15],"based":[16],"on":[17],"ambiguous":[18],"foundations,":[19],"leading":[20],"an":[22,43,117],"unclear":[23],"understanding":[24],"whether":[26],"or":[27,175],"how":[28],"various":[30,67],"clustering":[31,55,64,75,177],"methods":[32,68,147],"are":[33],"connected":[34],"with":[35],"each":[36,100],"other.":[37],"In":[38,77,107],"work,":[40],"we":[41,109,156],"provide":[42],"additional":[44],"stepping":[45],"stone":[46],"towards":[47],"resolving":[48],"such":[49],"ambiguities":[50],"by":[51],"presenting":[52],"a":[53,59,112,123,140,180],"general":[54],"framework":[56,84,121],"that":[57,114,162],"subsumes":[58],"series":[60],"seemingly":[62],"disparate":[63],"methods,":[65,170],"including":[66],"belonging":[69],"widely":[72],"popular":[73],"spectral":[74,126],"framework.":[76],"fact,":[78],"generality":[80],"proposed":[83],"additionally":[86],"capable":[87],"shedding":[89],"light":[90],"largely":[93],"unexplored":[94],"area":[95],"multi-view":[97,153],"graphs":[98],"where":[99],"view":[101],"may":[102],"have":[103],"differently":[104],"clustered":[105],"nodes.":[106],"turn,":[108],"propose":[110],"GenClus:":[111],"method":[113],"simultaneously":[116],"instance":[118],"and":[122],"generalization":[124],"while":[128,171],"also":[129,172],"being":[130],"closely":[131],"related":[132],"k-means":[134],"as":[135],"well.":[136],"This":[137],"results":[138],"in":[139],"principled":[141],"alternative":[142],"few":[145],"existing":[146,169],"studying":[148],"special":[150],"type":[151],"graphs.":[154],"Then,":[155],"conduct":[157],"in-depth":[158],"experiments,":[159],"which":[160],"demonstrate":[161],"GenClus":[163,189],"more":[165],"computationally":[166],"efficient":[167],"than":[168],"attaining":[173],"similar":[174],"better":[176],"performance.":[178],"Lastly,":[179],"qualitative":[181],"real-world":[182],"case-study":[183],"further":[184],"demonstrates":[185],"ability":[187],"produce":[191],"meaningful":[192],"clusterings.":[193]},"counts_by_year":[],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
