{"id":"https://openalex.org/W4415538307","doi":"https://doi.org/10.1145/3746027.3755787","title":"Robust Tensor Learning with Graph Diffusion for Scalable Multi-view Graph Clustering","display_name":"Robust Tensor Learning with Graph Diffusion for Scalable Multi-view Graph Clustering","publication_year":2025,"publication_date":"2025-10-25","ids":{"openalex":"https://openalex.org/W4415538307","doi":"https://doi.org/10.1145/3746027.3755787"},"language":null,"primary_location":{"id":"doi:10.1145/3746027.3755787","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3746027.3755787","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/A5107464775","display_name":"Jiale Zou","orcid":null},"institutions":[{"id":"https://openalex.org/I181877577","display_name":"Shanxi University","ror":"https://ror.org/03y3e3s17","country_code":"CN","type":"education","lineage":["https://openalex.org/I181877577"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Jiale Zou","raw_affiliation_strings":["Shanxi University, Taiyuan, China"],"affiliations":[{"raw_affiliation_string":"Shanxi University, Taiyuan, China","institution_ids":["https://openalex.org/I181877577"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100572369","display_name":"Yan Chen","orcid":"https://orcid.org/0000-0003-3898-2399"},"institutions":[{"id":"https://openalex.org/I9086337","display_name":"Taiyuan University of Technology","ror":"https://ror.org/03kv08d37","country_code":"CN","type":"education","lineage":["https://openalex.org/I9086337"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yan Chen","raw_affiliation_strings":["Taiyuan University of Technology, Taiyuan, China"],"affiliations":[{"raw_affiliation_string":"Taiyuan University of Technology, Taiyuan, China","institution_ids":["https://openalex.org/I9086337"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5013029508","display_name":"Bingbing Jiang","orcid":"https://orcid.org/0000-0003-2217-6202"},"institutions":[{"id":"https://openalex.org/I163151501","display_name":"Hangzhou Normal University","ror":"https://ror.org/014v1mr15","country_code":"CN","type":"education","lineage":["https://openalex.org/I163151501"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Bingbing Jiang","raw_affiliation_strings":["Hangzhou Normal University, Hangzhou, China"],"affiliations":[{"raw_affiliation_string":"Hangzhou Normal University, Hangzhou, China","institution_ids":["https://openalex.org/I163151501"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5004080941","display_name":"Peng Zhou","orcid":"https://orcid.org/0000-0002-3675-4985"},"institutions":[{"id":"https://openalex.org/I143868143","display_name":"Anhui University","ror":"https://ror.org/05th6yx34","country_code":"CN","type":"education","lineage":["https://openalex.org/I143868143"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Peng Zhou","raw_affiliation_strings":["Anhui University, Hefei, China"],"affiliations":[{"raw_affiliation_string":"Anhui University, Hefei, China","institution_ids":["https://openalex.org/I143868143"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5016197750","display_name":"Liang Du","orcid":"https://orcid.org/0000-0002-3294-5071"},"institutions":[{"id":"https://openalex.org/I181877577","display_name":"Shanxi University","ror":"https://ror.org/03y3e3s17","country_code":"CN","type":"education","lineage":["https://openalex.org/I181877577"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Liang Du","raw_affiliation_strings":["Shanxi University, Taiyuan, China"],"affiliations":[{"raw_affiliation_string":"Shanxi University, Taiyuan, China","institution_ids":["https://openalex.org/I181877577"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5103153652","display_name":"Lei Duan","orcid":"https://orcid.org/0000-0001-7254-1832"},"institutions":[{"id":"https://openalex.org/I24185976","display_name":"Sichuan University","ror":"https://ror.org/011ashp19","country_code":"CN","type":"education","lineage":["https://openalex.org/I24185976"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Lei Duan","raw_affiliation_strings":["Sichuan University, Chengdu, China"],"affiliations":[{"raw_affiliation_string":"Sichuan University, Chengdu, China","institution_ids":["https://openalex.org/I24185976"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5003563112","display_name":"Yuhua Qian","orcid":"https://orcid.org/0000-0001-6772-4247"},"institutions":[{"id":"https://openalex.org/I181877577","display_name":"Shanxi University","ror":"https://ror.org/03y3e3s17","country_code":"CN","type":"education","lineage":["https://openalex.org/I181877577"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yuhua Qian","raw_affiliation_strings":["Shanxi University, Taiyuan, China"],"affiliations":[{"raw_affiliation_string":"Shanxi University, Taiyuan, China","institution_ids":["https://openalex.org/I181877577"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":7,"corresponding_author_ids":["https://openalex.org/A5107464775"],"corresponding_institution_ids":["https://openalex.org/I181877577"],"apc_list":null,"apc_paid":null,"fwci":2.3568,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.91571899,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":96,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"2207","last_page":"2215"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11273","display_name":"Advanced Graph Neural Networks","score":0.9990000128746033,"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":0.9990000128746033,"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/T10627","display_name":"Advanced Image and Video Retrieval Techniques","score":0.9986000061035156,"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/T10057","display_name":"Face and Expression Recognition","score":0.9969000220298767,"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/scalability","display_name":"Scalability","score":0.6105999946594238},{"id":"https://openalex.org/keywords/cluster-analysis","display_name":"Cluster analysis","score":0.6093000173568726},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.5278000235557556},{"id":"https://openalex.org/keywords/embedding","display_name":"Embedding","score":0.4975000023841858},{"id":"https://openalex.org/keywords/discriminative-model","display_name":"Discriminative model","score":0.46869999170303345},{"id":"https://openalex.org/keywords/graph-embedding","display_name":"Graph embedding","score":0.4372999966144562},{"id":"https://openalex.org/keywords/tensor","display_name":"Tensor (intrinsic definition)","score":0.3935000002384186},{"id":"https://openalex.org/keywords/bipartite-graph","display_name":"Bipartite graph","score":0.39239999651908875},{"id":"https://openalex.org/keywords/exponential-growth","display_name":"Exponential growth","score":0.3497999906539917}],"concepts":[{"id":"https://openalex.org/C48044578","wikidata":"https://www.wikidata.org/wiki/Q727490","display_name":"Scalability","level":2,"score":0.6105999946594238},{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.6093000173568726},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5924000144004822},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.5602999925613403},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.5278000235557556},{"id":"https://openalex.org/C41608201","wikidata":"https://www.wikidata.org/wiki/Q980509","display_name":"Embedding","level":2,"score":0.4975000023841858},{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.46869999170303345},{"id":"https://openalex.org/C75564084","wikidata":"https://www.wikidata.org/wiki/Q5597085","display_name":"Graph embedding","level":3,"score":0.4372999966144562},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.4036000072956085},{"id":"https://openalex.org/C155281189","wikidata":"https://www.wikidata.org/wiki/Q3518150","display_name":"Tensor (intrinsic definition)","level":2,"score":0.3935000002384186},{"id":"https://openalex.org/C197657726","wikidata":"https://www.wikidata.org/wiki/Q174733","display_name":"Bipartite graph","level":3,"score":0.39239999651908875},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3547999858856201},{"id":"https://openalex.org/C75235859","wikidata":"https://www.wikidata.org/wiki/Q582659","display_name":"Exponential growth","level":2,"score":0.3497999906539917},{"id":"https://openalex.org/C22047676","wikidata":"https://www.wikidata.org/wiki/Q898680","display_name":"Clustering coefficient","level":3,"score":0.336899995803833},{"id":"https://openalex.org/C151376022","wikidata":"https://www.wikidata.org/wiki/Q168698","display_name":"Exponential function","level":2,"score":0.3125999867916107},{"id":"https://openalex.org/C105611402","wikidata":"https://www.wikidata.org/wiki/Q2976589","display_name":"Spectral clustering","level":3,"score":0.3091000020503998},{"id":"https://openalex.org/C184898388","wikidata":"https://www.wikidata.org/wiki/Q1435712","display_name":"Pairwise comparison","level":2,"score":0.3050999939441681},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.3041999936103821},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.299699991941452},{"id":"https://openalex.org/C2779172887","wikidata":"https://www.wikidata.org/wiki/Q184316","display_name":"PageRank","level":2,"score":0.29600000381469727},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.29409998655319214},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.28780001401901245},{"id":"https://openalex.org/C48903430","wikidata":"https://www.wikidata.org/wiki/Q491370","display_name":"Graph partition","level":3,"score":0.2872999906539917},{"id":"https://openalex.org/C88230418","wikidata":"https://www.wikidata.org/wiki/Q131476","display_name":"Graph theory","level":2,"score":0.28110000491142273},{"id":"https://openalex.org/C55128770","wikidata":"https://www.wikidata.org/wiki/Q5275440","display_name":"Diffusion map","level":4,"score":0.28029999136924744},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.27900001406669617},{"id":"https://openalex.org/C64339825","wikidata":"https://www.wikidata.org/wiki/Q722659","display_name":"Graph property","level":5,"score":0.2736999988555908},{"id":"https://openalex.org/C191795146","wikidata":"https://www.wikidata.org/wiki/Q3878446","display_name":"Norm (philosophy)","level":2,"score":0.27300000190734863},{"id":"https://openalex.org/C75553542","wikidata":"https://www.wikidata.org/wiki/Q178161","display_name":"A priori and a posteriori","level":2,"score":0.27059999108314514},{"id":"https://openalex.org/C92207270","wikidata":"https://www.wikidata.org/wiki/Q939253","display_name":"Matrix norm","level":3,"score":0.26019999384880066}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3746027.3755787","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3746027.3755787","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/G4315245821","display_name":null,"funder_award_id":"62376146,62472294,62176001","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"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":26,"referenced_works":["https://openalex.org/W2171878761","https://openalex.org/W2565835729","https://openalex.org/W2997739739","https://openalex.org/W3044063956","https://openalex.org/W4200226646","https://openalex.org/W4283792797","https://openalex.org/W4283827931","https://openalex.org/W4315926876","https://openalex.org/W4361984358","https://openalex.org/W4367280449","https://openalex.org/W4377079796","https://openalex.org/W4382202889","https://openalex.org/W4390415045","https://openalex.org/W4390874086","https://openalex.org/W4391620713","https://openalex.org/W4391984607","https://openalex.org/W4392729272","https://openalex.org/W4393160394","https://openalex.org/W4394596463","https://openalex.org/W4398764921","https://openalex.org/W4399619583","https://openalex.org/W4401725932","https://openalex.org/W4401835920","https://openalex.org/W4401863482","https://openalex.org/W4406138274","https://openalex.org/W4406978111"],"related_works":[],"abstract_inverted_index":{"The":[0],"rapid":[1],"proliferation":[2],"of":[3,14,116],"multi-view":[4,69,163],"data":[5],"has":[6,25],"necessitated":[7],"robust":[8],"and":[9,45,67,82,119,140,165],"scalable":[10,68],"clustering":[11],"techniques":[12],"capable":[13],"capturing":[15,91],"complex,":[16],"high-dimensional":[17],"patterns.":[18,167],"While":[19],"Multi-view":[20],"Bipartite":[21],"Graph":[22,62],"Clustering":[23],"(MVBGC)":[24],"shown":[26],"promising":[27],"results,":[28],"existing":[29],"approaches":[30],"often":[31],"overlook":[32],"that":[33,150],"the":[34,110,114],"generated":[35],"bipartite":[36],"graph":[37,70,75],"is":[38,103],"susceptible":[39],"to":[40,78,86,160],"disturbances":[41],"from":[42],"complex":[43],"structures":[44],"noise.":[46],"To":[47],"address":[48],"these":[49,124],"challenges,":[50],"we":[51],"propose":[52],"RTGD-MVC,":[53],"a":[54,74,97,106,127],"novel":[55],"framework":[56],"for":[57,65,109],"Robust":[58],"Tensor":[59,99],"Learning":[60],"with":[61,131],"Diffusion":[63],"tailored":[64],"efficient":[66],"clustering.":[71],"RTGD-MVC":[72,135,151],"integrates":[73],"diffusion":[76,85],"mechanism":[77],"suppress":[79],"noise":[80],"propagation":[81],"employs":[83],"cross-view":[84],"enhance":[87],"global":[88],"consistency":[89],"while":[90],"complementary":[92],"information":[93],"across":[94],"views.":[95],"Additionally,":[96],"non-convex":[98],"Exponential":[100],"Norm":[101],"(TEN)":[102],"introduced":[104],"as":[105],"tighter":[107],"surrogate":[108],"tensor":[111],"rank,":[112],"enabling":[113],"learning":[115],"more":[117],"discriminative":[118],"noise-robust":[120],"representations.":[121],"By":[122],"embedding":[123],"components":[125],"into":[126],"unified":[128],"optimization":[129],"model":[130],"linear":[132],"computational":[133],"complexity,":[134],"achieves":[136],"both":[137],"theoretical":[138],"efficiency":[139],"practical":[141],"scalability.":[142],"Extensive":[143],"experiments":[144],"on":[145],"diverse":[146],"benchmark":[147],"datasets":[148],"demonstrate":[149],"significantly":[152],"outperforms":[153],"state-of-the-art":[154],"methods,":[155],"highlighting":[156],"its":[157],"superior":[158],"ability":[159],"capture":[161],"intricate":[162],"correlations":[164],"structural":[166]},"counts_by_year":[{"year":2026,"cited_by_count":1}],"updated_date":"2026-04-09T08:11:56.329763","created_date":"2025-10-25T00:00:00"}
