{"id":"https://openalex.org/W4411270973","doi":"https://doi.org/10.1109/tnse.2025.3579427","title":"Multiresolution Clustering on Massive Attributed Graphs by Means of Optimal Aggregated Markov Chains","display_name":"Multiresolution Clustering on Massive Attributed Graphs by Means of Optimal Aggregated Markov Chains","publication_year":2025,"publication_date":"2025-06-13","ids":{"openalex":"https://openalex.org/W4411270973","doi":"https://doi.org/10.1109/tnse.2025.3579427"},"language":"en","primary_location":{"id":"doi:10.1109/tnse.2025.3579427","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tnse.2025.3579427","pdf_url":null,"source":{"id":"https://openalex.org/S2484352698","display_name":"IEEE Transactions on Network Science and Engineering","issn_l":"2327-4697","issn":["2327-4697","2334-329X"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["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 Network Science and Engineering","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/A5084848995","display_name":"Huijia Li","orcid":"https://orcid.org/0009-0001-1235-2818"},"institutions":[{"id":"https://openalex.org/I205237279","display_name":"Nankai University","ror":"https://ror.org/01y1kjr75","country_code":"CN","type":"education","lineage":["https://openalex.org/I205237279"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Huijia Li","raw_affiliation_strings":["School of Statistics and Data Science, AAIS, LPMC and KLMDASR, Nankai University, Tianjin, China"],"raw_orcid":"https://orcid.org/0009-0001-1235-2818","affiliations":[{"raw_affiliation_string":"School of Statistics and Data Science, AAIS, LPMC and KLMDASR, Nankai University, Tianjin, China","institution_ids":["https://openalex.org/I205237279"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5117242600","display_name":"Fanghao Lou","orcid":null},"institutions":[{"id":"https://openalex.org/I205237279","display_name":"Nankai University","ror":"https://ror.org/01y1kjr75","country_code":"CN","type":"education","lineage":["https://openalex.org/I205237279"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Fanghao Lou","raw_affiliation_strings":["School of Statistics and Data Science, AAIS, LPMC and KLMDASR, Nankai University, Tianjin, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Statistics and Data Science, AAIS, LPMC and KLMDASR, Nankai University, Tianjin, China","institution_ids":["https://openalex.org/I205237279"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101519070","display_name":"Qiqi Wang","orcid":"https://orcid.org/0000-0002-7181-148X"},"institutions":[{"id":"https://openalex.org/I205237279","display_name":"Nankai University","ror":"https://ror.org/01y1kjr75","country_code":"CN","type":"education","lineage":["https://openalex.org/I205237279"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Qiqi Wang","raw_affiliation_strings":["School of Statistics and Data Science, AAIS, LPMC and KLMDASR, Nankai University, Tianjin, China"],"raw_orcid":"https://orcid.org/0000-0002-7181-148X","affiliations":[{"raw_affiliation_string":"School of Statistics and Data Science, AAIS, LPMC and KLMDASR, Nankai University, Tianjin, China","institution_ids":["https://openalex.org/I205237279"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101582115","display_name":"Guijun Li","orcid":"https://orcid.org/0000-0002-5720-8369"},"institutions":[{"id":"https://openalex.org/I94611258","display_name":"Hebei Normal University","ror":"https://ror.org/004rbbw49","country_code":"CN","type":"education","lineage":["https://openalex.org/I94611258"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Guijun Li","raw_affiliation_strings":["School of Mathematical Sciences, Hebei Normal University, Shijiazhuang, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Mathematical Sciences, Hebei Normal University, Shijiazhuang, China","institution_ids":["https://openalex.org/I94611258"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5044431363","display_name":"Matja\u017e Perc","orcid":"https://orcid.org/0000-0002-3087-541X"},"institutions":[{"id":"https://openalex.org/I37696226","display_name":"University of Maribor","ror":"https://ror.org/01d5jce07","country_code":"SI","type":"education","lineage":["https://openalex.org/I37696226"]}],"countries":["SI"],"is_corresponding":false,"raw_author_name":"Matja\u017e Perc","raw_affiliation_strings":["Faculty of Natural Sciences and Mathematics, University of Maribor, Maribor, Slovenia"],"raw_orcid":"https://orcid.org/0000-0002-3087-541X","affiliations":[{"raw_affiliation_string":"Faculty of Natural Sciences and Mathematics, University of Maribor, Maribor, Slovenia","institution_ids":["https://openalex.org/I37696226"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":5,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":10.7582,"has_fulltext":false,"cited_by_count":11,"citation_normalized_percentile":{"value":0.98587112,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":98,"max":99},"biblio":{"volume":"13","issue":null,"first_page":"1","last_page":"10"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10064","display_name":"Complex Network Analysis Techniques","score":0.9458000063896179,"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":0.9458000063896179,"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/T10637","display_name":"Advanced Clustering Algorithms Research","score":0.9383000135421753,"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/markov-chain","display_name":"Markov chain","score":0.7316737174987793},{"id":"https://openalex.org/keywords/cluster-analysis","display_name":"Cluster analysis","score":0.6597653031349182},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5122389197349548},{"id":"https://openalex.org/keywords/markov-process","display_name":"Markov process","score":0.4727305471897125},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.39052510261535645},{"id":"https://openalex.org/keywords/mathematical-optimization","display_name":"Mathematical optimization","score":0.35580572485923767},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.2281983494758606},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.144270658493042},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.08976486325263977}],"concepts":[{"id":"https://openalex.org/C98763669","wikidata":"https://www.wikidata.org/wiki/Q176645","display_name":"Markov chain","level":2,"score":0.7316737174987793},{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.6597653031349182},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5122389197349548},{"id":"https://openalex.org/C159886148","wikidata":"https://www.wikidata.org/wiki/Q176645","display_name":"Markov process","level":2,"score":0.4727305471897125},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.39052510261535645},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.35580572485923767},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.2281983494758606},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.144270658493042},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.08976486325263977}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tnse.2025.3579427","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tnse.2025.3579427","pdf_url":null,"source":{"id":"https://openalex.org/S2484352698","display_name":"IEEE Transactions on Network Science and Engineering","issn_l":"2327-4697","issn":["2327-4697","2334-329X"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["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 Network Science and Engineering","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G4335549240","display_name":null,"funder_award_id":"P1-0403","funder_id":"https://openalex.org/F4320322554","funder_display_name":"Javna Agencija za Raziskovalno Dejavnost RS"}],"funders":[{"id":"https://openalex.org/F4320322554","display_name":"Javna Agencija za Raziskovalno Dejavnost RS","ror":"https://ror.org/059bp8k51"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":35,"referenced_works":["https://openalex.org/W36838763","https://openalex.org/W1976702945","https://openalex.org/W1993591232","https://openalex.org/W2012921801","https://openalex.org/W2018049970","https://openalex.org/W2033590892","https://openalex.org/W2056897951","https://openalex.org/W2083637347","https://openalex.org/W2099481135","https://openalex.org/W2128366083","https://openalex.org/W2131681506","https://openalex.org/W2139694940","https://openalex.org/W2141585940","https://openalex.org/W2150844583","https://openalex.org/W2164998314","https://openalex.org/W2329912647","https://openalex.org/W2747781258","https://openalex.org/W2757577862","https://openalex.org/W2769157493","https://openalex.org/W2769166455","https://openalex.org/W2909291893","https://openalex.org/W2921596575","https://openalex.org/W3031414651","https://openalex.org/W3044063956","https://openalex.org/W3092583268","https://openalex.org/W3194841521","https://openalex.org/W4224211868","https://openalex.org/W4312856860","https://openalex.org/W4322706754","https://openalex.org/W4375861821","https://openalex.org/W4377021937","https://openalex.org/W4389170216","https://openalex.org/W4389317742","https://openalex.org/W4399879414","https://openalex.org/W4400944807"],"related_works":["https://openalex.org/W4391375266","https://openalex.org/W1979597421","https://openalex.org/W2007980826","https://openalex.org/W2061531152","https://openalex.org/W2379651310","https://openalex.org/W2113019827","https://openalex.org/W1541249122","https://openalex.org/W2413828414","https://openalex.org/W4236062181","https://openalex.org/W2367222340"],"abstract_inverted_index":{"The":[0,137,154,217],"efficient":[1],"clustering":[2,34,52,225],"of":[3,77,86,163,170,180,195,198,205,229],"attributed":[4,41],"graphs":[5],"is":[6],"a":[7,31,49,78,118,128,150,160],"critical":[8],"and":[9,36,92,106,126,213,232],"challenging":[10],"problem":[11],"that":[12,103,122,220],"has":[13],"attracted":[14],"much":[15],"attentions":[16],"across":[17,174],"various":[18],"research":[19],"fields.":[20],"Despite":[21],"recent":[22],"advancements,":[23],"many":[24],"challenges":[25],"still":[26],"exist,":[27],"particularly":[28],"in":[29,67,227],"achieving":[30],"balance":[32],"between":[33,172],"quality":[35],"computational":[37],"efficiency":[38,231],"for":[39],"massive":[40],"graphs.":[42,216],"To":[43],"address":[44],"these":[45],"problems,":[46],"we":[47],"propose":[48],"new":[50],"graph":[51,224],"algorithm":[53,139],"based":[54,185],"on":[55,112,186,211],"Optimal":[56],"Aggregated":[57],"Markov":[58,71],"Chains":[59],"(OAMC),":[60],"designed":[61],"to":[62,97,132],"identify":[63],"an":[64,191],"optimal":[65,100],"partition":[66,124],"which":[68],"the":[69,75,84,94,99,104,134,168,178,187,196,203],"aggregated":[70,88,107,155],"chain":[72],"closely":[73],"approximates":[74],"behavior":[76],"network-induced":[79],"random":[80],"walker.":[81],"We":[82,201],"formalize":[83],"concept":[85],"lifted":[87],"transition":[89,108,156],"probability":[90,109,157],"matrix":[91,158],"utilizes":[93],"Kullback-Leibler":[95],"divergence":[96],"determine":[98],"partition.":[101],"Given":[102],"initial":[105],"matrices":[110],"reside":[111],"different":[113],"state":[114],"spaces,":[115],"OAMC":[116,221],"constructs":[117],"relaxed":[119],"alternative":[120],"space":[121],"contains":[123],"information":[125],"employs":[127],"multiplicative":[129],"optimization":[130],"method":[131],"minimize":[133],"KL":[135],"divergence.":[136],"proposed":[138],"effectively":[140],"uncovers":[141],"hierarchical":[142,175],"cluster":[143,199],"structures":[144],"at":[145],"multiple":[146],"resolutions,":[147],"governed":[148],"by":[149],"single":[151],"resolution":[152,188],"parameter.":[153],"provides":[159],"natural":[161],"representation":[162],"block":[164],"coupling":[165],"strengths,":[166],"facilitating":[167],"tracing":[169],"relationships":[171],"clusters":[173,181],"layers.":[176],"Moreover,":[177],"number":[179],"can":[182],"be":[183],"specified":[184],"parameter,":[189],"providing":[190],"meaningful":[192],"interpretable":[193],"metric":[194],"significance":[197],"configuration.":[200],"validate":[202],"performance":[204],"our":[206],"framework":[207],"through":[208],"comprehensive":[209],"experiments":[210],"synthetic":[212],"real-world":[214],"benchmark":[215],"results":[218],"demonstrate":[219],"outperforms":[222],"state-of-the-art":[223],"methods":[226],"terms":[228],"both":[230],"effectiveness.":[233]},"counts_by_year":[{"year":2026,"cited_by_count":4},{"year":2025,"cited_by_count":7}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}