{"id":"https://openalex.org/W4415536795","doi":"https://doi.org/10.1145/3746027.3754969","title":"Unsupervised Cross-view Message Passing Method for Multi-view Graph Clustering","display_name":"Unsupervised Cross-view Message Passing Method for Multi-view Graph Clustering","publication_year":2025,"publication_date":"2025-10-25","ids":{"openalex":"https://openalex.org/W4415536795","doi":"https://doi.org/10.1145/3746027.3754969"},"language":null,"primary_location":{"id":"doi:10.1145/3746027.3754969","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3746027.3754969","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 33rd 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/A5116142649","display_name":"Ziming Quan","orcid":null},"institutions":[{"id":"https://openalex.org/I90610280","display_name":"South China University of Technology","ror":"https://ror.org/0530pts50","country_code":"CN","type":"education","lineage":["https://openalex.org/I90610280"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Ziming Quan","raw_affiliation_strings":["South China University of Technology, Guangzhou, China"],"raw_orcid":"https://orcid.org/0009-0006-9816-4290","affiliations":[{"raw_affiliation_string":"South China University of Technology, Guangzhou, China","institution_ids":["https://openalex.org/I90610280"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5103196395","display_name":"Penglei Wang","orcid":"https://orcid.org/0000-0001-9469-3917"},"institutions":[{"id":"https://openalex.org/I90610280","display_name":"South China University of Technology","ror":"https://ror.org/0530pts50","country_code":"CN","type":"education","lineage":["https://openalex.org/I90610280"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Penglei Wang","raw_affiliation_strings":["South China University of Technology, Guangzhou, China"],"raw_orcid":"https://orcid.org/0000-0001-9469-3917","affiliations":[{"raw_affiliation_string":"South China University of Technology, Guangzhou, China","institution_ids":["https://openalex.org/I90610280"]}]},{"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/I89652312","display_name":"Northwest A&F University","ror":"https://ror.org/0051rme32","country_code":"CN","type":"education","lineage":["https://openalex.org/I89652312"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Danyang Wu","raw_affiliation_strings":["Northwest A&amp;F University, Yangling, China"],"raw_orcid":"https://orcid.org/0000-0002-0309-1409","affiliations":[{"raw_affiliation_string":"Northwest A&amp;F University, Yangling, China","institution_ids":["https://openalex.org/I89652312"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5114357209","display_name":"Jin Xu","orcid":"https://orcid.org/0009-0001-8735-3532"},"institutions":[{"id":"https://openalex.org/I90610280","display_name":"South China University of Technology","ror":"https://ror.org/0530pts50","country_code":"CN","type":"education","lineage":["https://openalex.org/I90610280"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jin Xu","raw_affiliation_strings":["South China University of Technology, Guangzhou, China"],"raw_orcid":"https://orcid.org/0009-0001-8735-3532","affiliations":[{"raw_affiliation_string":"South China University of Technology, Guangzhou, China","institution_ids":["https://openalex.org/I90610280"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5116142649"],"corresponding_institution_ids":["https://openalex.org/I90610280"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.15581472,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1122","last_page":"1131"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11273","display_name":"Advanced Graph Neural Networks","score":0.9997000098228455,"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":0.9997000098228455,"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/T10064","display_name":"Complex Network Analysis Techniques","score":0.9986000061035156,"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":0.9941999912261963,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/cluster-analysis","display_name":"Cluster analysis","score":0.8192999958992004},{"id":"https://openalex.org/keywords/correlation-clustering","display_name":"Correlation clustering","score":0.4984999895095825},{"id":"https://openalex.org/keywords/node","display_name":"Node (physics)","score":0.4893999993801117},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.4713999927043915},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.4652000069618225},{"id":"https://openalex.org/keywords/feature-learning","display_name":"Feature learning","score":0.445499986410141},{"id":"https://openalex.org/keywords/fuzzy-clustering","display_name":"Fuzzy clustering","score":0.44190001487731934},{"id":"https://openalex.org/keywords/conceptual-clustering","display_name":"Conceptual clustering","score":0.43160000443458557},{"id":"https://openalex.org/keywords/canopy-clustering-algorithm","display_name":"Canopy clustering algorithm","score":0.41130000352859497}],"concepts":[{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.8192999958992004},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7631000280380249},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5329999923706055},{"id":"https://openalex.org/C94641424","wikidata":"https://www.wikidata.org/wiki/Q5172845","display_name":"Correlation clustering","level":3,"score":0.4984999895095825},{"id":"https://openalex.org/C62611344","wikidata":"https://www.wikidata.org/wiki/Q1062658","display_name":"Node (physics)","level":2,"score":0.4893999993801117},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.4713999927043915},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.4652000069618225},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.445499986410141},{"id":"https://openalex.org/C17212007","wikidata":"https://www.wikidata.org/wiki/Q5511111","display_name":"Fuzzy clustering","level":3,"score":0.44190001487731934},{"id":"https://openalex.org/C39235581","wikidata":"https://www.wikidata.org/wiki/Q5158434","display_name":"Conceptual clustering","level":5,"score":0.43160000443458557},{"id":"https://openalex.org/C104047586","wikidata":"https://www.wikidata.org/wiki/Q5033439","display_name":"Canopy clustering algorithm","level":4,"score":0.41130000352859497},{"id":"https://openalex.org/C854659","wikidata":"https://www.wikidata.org/wiki/Q1859284","display_name":"Message passing","level":2,"score":0.39989998936653137},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.39980000257492065},{"id":"https://openalex.org/C8038995","wikidata":"https://www.wikidata.org/wiki/Q1152135","display_name":"Unsupervised learning","level":2,"score":0.3977000117301941},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.38769999146461487},{"id":"https://openalex.org/C192209626","wikidata":"https://www.wikidata.org/wiki/Q190909","display_name":"Focus (optics)","level":2,"score":0.3700000047683716},{"id":"https://openalex.org/C27964816","wikidata":"https://www.wikidata.org/wiki/Q5164359","display_name":"Constrained clustering","level":5,"score":0.35600000619888306},{"id":"https://openalex.org/C193143536","wikidata":"https://www.wikidata.org/wiki/Q5227360","display_name":"Data stream clustering","level":5,"score":0.3463999927043915},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3382999897003174},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.33160001039505005},{"id":"https://openalex.org/C74193536","wikidata":"https://www.wikidata.org/wiki/Q574844","display_name":"Kernel (algebra)","level":2,"score":0.32499998807907104},{"id":"https://openalex.org/C33704608","wikidata":"https://www.wikidata.org/wiki/Q5014717","display_name":"CURE data clustering algorithm","level":4,"score":0.31619998812675476},{"id":"https://openalex.org/C2776562905","wikidata":"https://www.wikidata.org/wiki/Q306610","display_name":"Hypersphere","level":2,"score":0.3124000132083893},{"id":"https://openalex.org/C122280245","wikidata":"https://www.wikidata.org/wiki/Q620622","display_name":"Kernel method","level":3,"score":0.296099990606308},{"id":"https://openalex.org/C105611402","wikidata":"https://www.wikidata.org/wiki/Q2976589","display_name":"Spectral clustering","level":3,"score":0.28189998865127563},{"id":"https://openalex.org/C144817290","wikidata":"https://www.wikidata.org/wiki/Q2976575","display_name":"Biclustering","level":5,"score":0.27320000529289246},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.26179999113082886},{"id":"https://openalex.org/C2777212361","wikidata":"https://www.wikidata.org/wiki/Q5127848","display_name":"Class (philosophy)","level":2,"score":0.25130000710487366}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3746027.3754969","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3746027.3754969","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 33rd ACM International Conference on Multimedia","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G2234319012","display_name":null,"funder_award_id":"62372187","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G6687295176","display_name":null,"funder_award_id":"2022YFC3601005","funder_id":"https://openalex.org/F4320335777","funder_display_name":"National Key Research and Development Program 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/F4320335777","display_name":"National Key Research and Development Program of China","ror":null}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":23,"referenced_works":["https://openalex.org/W1501856433","https://openalex.org/W2095425746","https://openalex.org/W2103560185","https://openalex.org/W2116341502","https://openalex.org/W2131681506","https://openalex.org/W2293546752","https://openalex.org/W2436321460","https://openalex.org/W2563515532","https://openalex.org/W2921065608","https://openalex.org/W2962756421","https://openalex.org/W3012918605","https://openalex.org/W3034903580","https://openalex.org/W3168126734","https://openalex.org/W3174945605","https://openalex.org/W3210242454","https://openalex.org/W4226142973","https://openalex.org/W4309364607","https://openalex.org/W4312881178","https://openalex.org/W4321479930","https://openalex.org/W4380875852","https://openalex.org/W4396843892","https://openalex.org/W4400193246","https://openalex.org/W4401567221"],"related_works":[],"abstract_inverted_index":{"In":[0,37],"recent":[1],"years,":[2],"multi-view":[3,173,188],"graph":[4],"clustering":[5,65,133,152,158,189],"(MVGC)":[6],"has":[7],"attracted":[8],"increasing":[9],"attention":[10],"from":[11,59,113],"researchers.":[12],"However,":[13],"many":[14],"existing":[15,186],"MVGC":[16],"methods":[17,55],"focus":[18],"on":[19,99,156,164,171],"view-level":[20],"integration":[21],"through":[22],"strategies":[23],"like":[24],"assigning":[25],"weights":[26],"to":[27],"different":[28,114],"views,":[29,115],"for":[30,46,80],"example,":[31],"ignoring":[32],"cross-view":[33,39,76,89,96],"interactions":[34,40,109],"between":[35],"nodes.":[36],"fact,":[38],"at":[41],"node":[42,100,140],"level":[43],"are":[44],"crucial":[45],"extraction":[47,119],"and":[48,102,120,131,167],"fusion":[49],"of":[50,84,110,122,150],"semantic":[51],"information.":[52,104,124],"Additionally,":[53],"some":[54],"separate":[56],"representation":[57,129],"learning":[58,130],"clustering,":[60],"which":[61,92,116,138],"results":[62,153,176],"in":[63,134],"suboptimal":[64],"performance.":[66],"To":[67],"address":[68],"these":[69],"problems,":[70],"we":[71],"propose":[72],"a":[73,143],"novel":[74],"unsupervised":[75],"message":[77],"passing":[78],"method":[79,86,127,170,180],"MVGC.":[81],"The":[82,105,175],"kernel":[83],"our":[85,126,165,169,179],"is":[87],"the":[88,118],"interaction":[90],"mechanism,":[91],"dynamically":[93],"constructs":[94],"node-specific":[95],"edges":[97],"based":[98],"features":[101],"structural":[103],"mechanism":[106],"enables":[107],"adaptive":[108],"informative":[111],"nodes":[112],"promotes":[117],"propagation":[121],"complementary":[123],"Besides,":[125],"unifies":[128],"hyperspherical":[132],"an":[135],"end-to-end":[136],"framework,":[137],"projects":[139],"representations":[141],"into":[142],"hypersphere":[144],"space,":[145],"thereby":[146],"enabling":[147],"direct":[148],"acquisition":[149],"balanced":[151],"without":[154],"dependence":[155],"external":[157],"methods.":[159,190],"We":[160],"provide":[161],"comprehensive":[162],"analyses":[163],"method,":[166],"evaluate":[168],"six":[172],"datasets.":[174],"show":[177],"that":[178],"consistently":[181],"achieves":[182],"superior":[183],"performance":[184],"than":[185],"state-of-the-art":[187]},"counts_by_year":[],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-25T00:00:00"}
