{"id":"https://openalex.org/W2106435568","doi":"https://doi.org/10.1145/1081870.1081948","title":"A fast kernel-based multilevel algorithm for graph clustering","display_name":"A fast kernel-based multilevel algorithm for graph clustering","publication_year":2005,"publication_date":"2005-08-21","ids":{"openalex":"https://openalex.org/W2106435568","doi":"https://doi.org/10.1145/1081870.1081948","mag":"2106435568"},"language":"en","primary_location":{"id":"doi:10.1145/1081870.1081948","is_oa":false,"landing_page_url":"https://doi.org/10.1145/1081870.1081948","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining","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/A5063459703","display_name":"Inderjit S. Dhillon","orcid":"https://orcid.org/0000-0002-2759-1416"},"institutions":[{"id":"https://openalex.org/I86519309","display_name":"The University of Texas at Austin","ror":"https://ror.org/00hj54h04","country_code":"US","type":"education","lineage":["https://openalex.org/I86519309"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Inderjit Dhillon","raw_affiliation_strings":["University of Texas at Austin, Austin, TX"],"affiliations":[{"raw_affiliation_string":"University of Texas at Austin, Austin, TX","institution_ids":["https://openalex.org/I86519309"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5004682787","display_name":"Yuqiang Guan","orcid":null},"institutions":[{"id":"https://openalex.org/I86519309","display_name":"The University of Texas at Austin","ror":"https://ror.org/00hj54h04","country_code":"US","type":"education","lineage":["https://openalex.org/I86519309"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Yuqiang Guan","raw_affiliation_strings":["University of Texas at Austin, Austin, TX"],"affiliations":[{"raw_affiliation_string":"University of Texas at Austin, Austin, TX","institution_ids":["https://openalex.org/I86519309"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5049134079","display_name":"Brian Kulis","orcid":"https://orcid.org/0000-0002-1704-3838"},"institutions":[{"id":"https://openalex.org/I86519309","display_name":"The University of Texas at Austin","ror":"https://ror.org/00hj54h04","country_code":"US","type":"education","lineage":["https://openalex.org/I86519309"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Brian Kulis","raw_affiliation_strings":["University of Texas at Austin, Austin, TX"],"affiliations":[{"raw_affiliation_string":"University of Texas at Austin, Austin, TX","institution_ids":["https://openalex.org/I86519309"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5063459703"],"corresponding_institution_ids":["https://openalex.org/I86519309"],"apc_list":null,"apc_paid":null,"fwci":7.7127,"has_fulltext":false,"cited_by_count":121,"citation_normalized_percentile":{"value":0.98017223,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":94,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"629","last_page":"634"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10064","display_name":"Complex Network Analysis Techniques","score":0.9990000128746033,"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":0.9990000128746033,"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/T11522","display_name":"VLSI and FPGA Design Techniques","score":0.9950000047683716,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic Engineering"},"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/T11273","display_name":"Advanced Graph Neural Networks","score":0.9908999800682068,"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":0.7656933069229126},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6068962216377258},{"id":"https://openalex.org/keywords/correlation-clustering","display_name":"Correlation clustering","score":0.5672751665115356},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.5624039173126221},{"id":"https://openalex.org/keywords/graph-partition","display_name":"Graph partition","score":0.5207992792129517},{"id":"https://openalex.org/keywords/clustering-coefficient","display_name":"Clustering coefficient","score":0.5126308798789978},{"id":"https://openalex.org/keywords/spectral-clustering","display_name":"Spectral clustering","score":0.48346051573753357},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.42916280031204224},{"id":"https://openalex.org/keywords/cure-data-clustering-algorithm","display_name":"CURE data clustering algorithm","score":0.4217299818992615},{"id":"https://openalex.org/keywords/power-graph-analysis","display_name":"Power graph analysis","score":0.421329528093338},{"id":"https://openalex.org/keywords/canopy-clustering-algorithm","display_name":"Canopy clustering algorithm","score":0.4139768183231354},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.398475706577301},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.32086411118507385},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.16566309332847595}],"concepts":[{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.7656933069229126},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6068962216377258},{"id":"https://openalex.org/C94641424","wikidata":"https://www.wikidata.org/wiki/Q5172845","display_name":"Correlation clustering","level":3,"score":0.5672751665115356},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.5624039173126221},{"id":"https://openalex.org/C48903430","wikidata":"https://www.wikidata.org/wiki/Q491370","display_name":"Graph partition","level":3,"score":0.5207992792129517},{"id":"https://openalex.org/C22047676","wikidata":"https://www.wikidata.org/wiki/Q898680","display_name":"Clustering coefficient","level":3,"score":0.5126308798789978},{"id":"https://openalex.org/C105611402","wikidata":"https://www.wikidata.org/wiki/Q2976589","display_name":"Spectral clustering","level":3,"score":0.48346051573753357},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.42916280031204224},{"id":"https://openalex.org/C33704608","wikidata":"https://www.wikidata.org/wiki/Q5014717","display_name":"CURE data clustering algorithm","level":4,"score":0.4217299818992615},{"id":"https://openalex.org/C106937863","wikidata":"https://www.wikidata.org/wiki/Q7236518","display_name":"Power graph analysis","level":3,"score":0.421329528093338},{"id":"https://openalex.org/C104047586","wikidata":"https://www.wikidata.org/wiki/Q5033439","display_name":"Canopy clustering algorithm","level":4,"score":0.4139768183231354},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.398475706577301},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.32086411118507385},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.16566309332847595}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1145/1081870.1081948","is_oa":false,"landing_page_url":"https://doi.org/10.1145/1081870.1081948","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining","raw_type":"proceedings-article"},{"id":"pmh:oai:CiteSeerX.psu:10.1.1.80.3187","is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.80.3187","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://www.cs.utexas.edu/users/inderjit/public_papers/multilevel_kdd.pdf","raw_type":"text"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":9,"referenced_works":["https://openalex.org/W96060697","https://openalex.org/W1986007546","https://openalex.org/W2033852356","https://openalex.org/W2070232376","https://openalex.org/W2118953734","https://openalex.org/W2121853761","https://openalex.org/W2121947440","https://openalex.org/W2135674549","https://openalex.org/W2155754954"],"related_works":["https://openalex.org/W1694785277","https://openalex.org/W1966256533","https://openalex.org/W4352976663","https://openalex.org/W3133691047","https://openalex.org/W1979094538","https://openalex.org/W4210453992","https://openalex.org/W4287724928","https://openalex.org/W3041674974","https://openalex.org/W2114694099","https://openalex.org/W2091173929"],"abstract_inverted_index":{"Graph":[0],"clustering":[1,7,29,57,113,118,168],"(also":[2],"called":[3],"graph":[4,12,28,56,93,112,147],"partitioning)":[5],"---":[6,13],"the":[8,128,142,146],"nodes":[9,182],"of":[10,27,105,173,196,206],"a":[11,59,87,138,165,171,233],"is":[14,215],"an":[15,71],"important":[16],"problem":[17],"in":[18,194,204],"diverse":[19],"data":[20],"mining":[21],"applications.":[22],"Traditional":[23],"approaches":[24,96],"involve":[25,97],"optimization":[26],"objectives":[30],"such":[31,120],"as":[32,121,162],"normalized":[33,191],"cut":[34,61,148,192],"or":[35],"ratio":[36,201],"association;":[37],"spectral":[38,167,219,229,252],"methods":[39],"are":[40],"widely":[41],"used":[42],"for":[43,92],"these":[44],"objectives,":[45],"but":[46],"they":[47],"require":[48],"eigenvector":[49],"computation":[50],"which":[51,106,242],"can":[52],"be":[53,67,108,132,248],"slow.":[54],"Recently,":[55],"with":[58,177,251],"general":[60],"objective":[62,76,149,159],"has":[63],"been":[64],"shown":[65],"to":[66,70,85,110,131,164,179,244],"mathematically":[68],"equivalent":[69],"appropriate":[72],"weighted":[73],"kernel":[74],"k-means":[75],"function.":[77],"In":[78],"this":[79,83],"paper,":[80],"we":[81,155,231],"exploit":[82],"equivalence":[84],"develop":[86],"very":[88],"fast":[89],"multilevel":[90,117],"algorithm":[91,124,188,214,223],"clustering.":[94],"Multilevel":[95],"coarsening,":[98],"initial":[99],"partitioning":[100],"and":[101,183,199],"refinement":[102,143],"phases,":[103],"all":[104],"may":[107],"specialized":[109],"different":[111],"objectives.":[114],"Unlike":[115],"existing":[116],"approaches,":[119],"METIS,":[122],"our":[123,187,197,207,213,222],"does":[125],"not":[126],"constrain":[127],"cluster":[129,232],"sizes":[130],"nearly":[133],"equal.":[134],"Our":[135],"approach":[136],"gives":[137],"theoretical":[139],"guarantee":[140],"that":[141,154],"step":[144],"decreases":[145],"under":[150],"consideration.":[151],"Experiments":[152],"show":[153],"achieve":[156],"better":[157],"final":[158],"function":[160],"values":[161,193,203],"compared":[163],"state-of-the-art":[166],"algorithm:":[169],"on":[170,210],"series":[172],"benchmark":[174],"test":[175],"graphs":[176],"up":[178],"thirty":[180],"thousand":[181],"one":[184],"million":[185,235],"edges,":[186],"achieves":[189],"lower":[190],"67%":[195],"experiments":[198],"higher":[200],"association":[202],"100%":[205],"experiments.":[208],"Furthermore,":[209],"large":[211],"graphs,":[212],"significantly":[216],"faster":[217],"than":[218,228],"methods.":[220,253],"Finally,":[221],"requires":[224],"far":[225],"less":[226],"memory":[227,245],"methods;":[230],"1.2":[234],"node":[236],"movie":[237],"network":[238],"into":[239],"5000":[240],"clusters,":[241],"due":[243],"requirements":[246],"cannot":[247],"done":[249],"directly":[250]},"counts_by_year":[{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":3},{"year":2022,"cited_by_count":6},{"year":2021,"cited_by_count":6},{"year":2020,"cited_by_count":3},{"year":2019,"cited_by_count":3},{"year":2018,"cited_by_count":7},{"year":2017,"cited_by_count":4},{"year":2016,"cited_by_count":5},{"year":2015,"cited_by_count":7},{"year":2014,"cited_by_count":7},{"year":2013,"cited_by_count":8},{"year":2012,"cited_by_count":8}],"updated_date":"2026-04-04T16:13:02.066488","created_date":"2025-10-10T00:00:00"}
