{"id":"https://openalex.org/W2136793331","doi":"https://doi.org/10.1145/1055558.1055579","title":"Clustering via matrix powering","display_name":"Clustering via matrix powering","publication_year":2004,"publication_date":"2004-06-14","ids":{"openalex":"https://openalex.org/W2136793331","doi":"https://doi.org/10.1145/1055558.1055579","mag":"2136793331"},"language":"en","primary_location":{"id":"doi:10.1145/1055558.1055579","is_oa":false,"landing_page_url":"https://doi.org/10.1145/1055558.1055579","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the twenty-third ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems","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/A5007996900","display_name":"Hanson Zhou","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Hanson Zhou","raw_affiliation_strings":["Intelligence Laboratory, Cambridge, MA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Intelligence Laboratory, Cambridge, MA","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5102861589","display_name":"David P. Woodruff","orcid":"https://orcid.org/0000-0002-2158-1380"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"David Woodruff","raw_affiliation_strings":["Intelligence Laboratory, Cambridge, MA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Intelligence Laboratory, Cambridge, MA","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.2475,"has_fulltext":false,"cited_by_count":15,"citation_normalized_percentile":{"value":0.59348922,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"136","last_page":"142"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10064","display_name":"Complex Network Analysis Techniques","score":0.9897000193595886,"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":0.9897000193595886,"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/T11901","display_name":"Bayesian Methods and Mixture Models","score":0.9868999719619751,"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/T10637","display_name":"Advanced Clustering Algorithms Research","score":0.9857000112533569,"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/rounding","display_name":"Rounding","score":0.8254634737968445},{"id":"https://openalex.org/keywords/cluster-analysis","display_name":"Cluster analysis","score":0.730555534362793},{"id":"https://openalex.org/keywords/partition","display_name":"Partition (number theory)","score":0.6211978197097778},{"id":"https://openalex.org/keywords/pairwise-comparison","display_name":"Pairwise comparison","score":0.5973188877105713},{"id":"https://openalex.org/keywords/exponent","display_name":"Exponent","score":0.5812666416168213},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5095794200897217},{"id":"https://openalex.org/keywords/matrix","display_name":"Matrix (chemical analysis)","score":0.48195454478263855},{"id":"https://openalex.org/keywords/distance-matrix","display_name":"Distance matrix","score":0.48059365153312683},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.46875762939453125},{"id":"https://openalex.org/keywords/similarity","display_name":"Similarity (geometry)","score":0.44295814633369446},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.43178048729896545},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.42771533131599426},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.42426151037216187},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.3879631757736206},{"id":"https://openalex.org/keywords/combinatorics","display_name":"Combinatorics","score":0.34019678831100464},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.20077946782112122}],"concepts":[{"id":"https://openalex.org/C136625980","wikidata":"https://www.wikidata.org/wiki/Q663208","display_name":"Rounding","level":2,"score":0.8254634737968445},{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.730555534362793},{"id":"https://openalex.org/C42812","wikidata":"https://www.wikidata.org/wiki/Q1082910","display_name":"Partition (number theory)","level":2,"score":0.6211978197097778},{"id":"https://openalex.org/C184898388","wikidata":"https://www.wikidata.org/wiki/Q1435712","display_name":"Pairwise comparison","level":2,"score":0.5973188877105713},{"id":"https://openalex.org/C2780388253","wikidata":"https://www.wikidata.org/wiki/Q5421508","display_name":"Exponent","level":2,"score":0.5812666416168213},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5095794200897217},{"id":"https://openalex.org/C106487976","wikidata":"https://www.wikidata.org/wiki/Q685816","display_name":"Matrix (chemical analysis)","level":2,"score":0.48195454478263855},{"id":"https://openalex.org/C111208986","wikidata":"https://www.wikidata.org/wiki/Q901698","display_name":"Distance matrix","level":2,"score":0.48059365153312683},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.46875762939453125},{"id":"https://openalex.org/C103278499","wikidata":"https://www.wikidata.org/wiki/Q254465","display_name":"Similarity (geometry)","level":3,"score":0.44295814633369446},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.43178048729896545},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.42771533131599426},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.42426151037216187},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.3879631757736206},{"id":"https://openalex.org/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","level":1,"score":0.34019678831100464},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.20077946782112122},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0},{"id":"https://openalex.org/C159985019","wikidata":"https://www.wikidata.org/wiki/Q181790","display_name":"Composite material","level":1,"score":0.0},{"id":"https://openalex.org/C192562407","wikidata":"https://www.wikidata.org/wiki/Q228736","display_name":"Materials science","level":0,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.0},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/1055558.1055579","is_oa":false,"landing_page_url":"https://doi.org/10.1145/1055558.1055579","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the twenty-third ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":28,"referenced_works":["https://openalex.org/W1514107797","https://openalex.org/W1605711022","https://openalex.org/W1854155592","https://openalex.org/W1891203375","https://openalex.org/W1943182387","https://openalex.org/W1977541023","https://openalex.org/W1979740015","https://openalex.org/W1989042829","https://openalex.org/W2013089587","https://openalex.org/W2023770210","https://openalex.org/W2024128809","https://openalex.org/W2063392856","https://openalex.org/W2065060195","https://openalex.org/W2073583237","https://openalex.org/W2086486316","https://openalex.org/W2088272899","https://openalex.org/W2112263772","https://openalex.org/W2114772807","https://openalex.org/W2122837498","https://openalex.org/W2130891992","https://openalex.org/W2158319365","https://openalex.org/W2295815851","https://openalex.org/W2434205482","https://openalex.org/W2999905431","https://openalex.org/W3111753423","https://openalex.org/W4234689394","https://openalex.org/W4285719527","https://openalex.org/W6630727257"],"related_works":["https://openalex.org/W4220780102","https://openalex.org/W2410881844","https://openalex.org/W3196334750","https://openalex.org/W1502401885","https://openalex.org/W2004257129","https://openalex.org/W2116281088","https://openalex.org/W2357551824","https://openalex.org/W2016668641","https://openalex.org/W2023397165","https://openalex.org/W1649917817"],"abstract_inverted_index":{"Given":[0],"a":[1,7,56,59,65,82,105],"set":[2],"of":[3,9,52,70,92,99],"n":[4],"points":[5,19,25],"with":[6,96],"matrix":[8,38,72],"pairwise":[10],"similarity":[11],"measures,":[12],"one":[13],"would":[14],"like":[15],"to":[16],"partition":[17,66],"the":[18,74,89,93,100],"into":[20],"clusters":[21],"so":[22],"that":[23,40,88,104],"similar":[24],"are":[26],"together":[27],"and":[28,45],"different":[29],"ones":[30],"apart.":[31],"We":[32],"present":[33],"an":[34,47],"algorithm":[35,75,94],"requiring":[36],"only":[37,81],"powering":[39],"performs":[41],"well":[42],"in":[43,50],"practice":[44],"bears":[46],"elegant":[48],"interpretation":[49],"terms":[51],"random":[53],"walks":[54],"on":[55],"graph.":[57],"Under":[58],"certain":[60],"mixture":[61],"model":[62],"involving":[63],"planting":[64],"via":[67],"randomized":[68],"rounding":[69],"tailored":[71],"entries,":[73],"can":[76],"be":[77],"proven":[78],"effective":[79],"for":[80],"single":[83,106],"squaring.":[84],"It":[85],"is":[86,108],"shown":[87],"clustering":[90],"performance":[91],"degrades":[95],"larger":[97],"values":[98],"exponent,":[101],"thus":[102],"revealing":[103],"squaring":[107],"optimal.":[109]},"counts_by_year":[{"year":2018,"cited_by_count":1},{"year":2016,"cited_by_count":1},{"year":2015,"cited_by_count":1},{"year":2014,"cited_by_count":1},{"year":2012,"cited_by_count":5}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}