{"id":"https://openalex.org/W1985921582","doi":"https://doi.org/10.1145/2588555.2612662","title":"Efficient top-K SimRank-based similarity join","display_name":"Efficient top-K SimRank-based similarity join","publication_year":2014,"publication_date":"2014-06-18","ids":{"openalex":"https://openalex.org/W1985921582","doi":"https://doi.org/10.1145/2588555.2612662","mag":"1985921582"},"language":"en","primary_location":{"id":"doi:10.1145/2588555.2612662","is_oa":false,"landing_page_url":"https://doi.org/10.1145/2588555.2612662","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2014 ACM SIGMOD International Conference on Management of Data","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/A5018442175","display_name":"Wenbo Tao","orcid":null},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Wenbo Tao","raw_affiliation_strings":["Tsinghua University, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100451576","display_name":"Guoliang Li","orcid":"https://orcid.org/0000-0002-1398-0621"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Guoliang Li","raw_affiliation_strings":["Tsinghua University, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5018442175"],"corresponding_institution_ids":["https://openalex.org/I99065089"],"apc_list":null,"apc_paid":null,"fwci":2.89788631,"has_fulltext":false,"cited_by_count":12,"citation_normalized_percentile":{"value":0.90873148,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":89,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"1603","last_page":"1604"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11273","display_name":"Advanced Graph Neural Networks","score":0.9994999766349792,"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":0.9994999766349792,"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":0.9994000196456909,"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/T11106","display_name":"Data Management and Algorithms","score":0.9934999942779541,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"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/join","display_name":"Join (topology)","score":0.7658581733703613},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7531955242156982},{"id":"https://openalex.org/keywords/similarity","display_name":"Similarity (geometry)","score":0.7447350025177002},{"id":"https://openalex.org/keywords/random-walk","display_name":"Random walk","score":0.5488206148147583},{"id":"https://openalex.org/keywords/similarity-measure","display_name":"Similarity measure","score":0.5440331101417542},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.4995293617248535},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3854776620864868},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3513684570789337},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.3289671540260315},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.1710936427116394},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.08811667561531067},{"id":"https://openalex.org/keywords/combinatorics","display_name":"Combinatorics","score":0.07101523876190186}],"concepts":[{"id":"https://openalex.org/C2776124973","wikidata":"https://www.wikidata.org/wiki/Q3183033","display_name":"Join (topology)","level":2,"score":0.7658581733703613},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7531955242156982},{"id":"https://openalex.org/C103278499","wikidata":"https://www.wikidata.org/wiki/Q254465","display_name":"Similarity (geometry)","level":3,"score":0.7447350025177002},{"id":"https://openalex.org/C121194460","wikidata":"https://www.wikidata.org/wiki/Q856741","display_name":"Random walk","level":2,"score":0.5488206148147583},{"id":"https://openalex.org/C2776517306","wikidata":"https://www.wikidata.org/wiki/Q29017317","display_name":"Similarity measure","level":2,"score":0.5440331101417542},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.4995293617248535},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3854776620864868},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3513684570789337},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.3289671540260315},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.1710936427116394},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.08811667561531067},{"id":"https://openalex.org/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","level":1,"score":0.07101523876190186},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/2588555.2612662","is_oa":false,"landing_page_url":"https://doi.org/10.1145/2588555.2612662","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2014 ACM SIGMOD International Conference on Management of Data","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":20,"referenced_works":["https://openalex.org/W40096148","https://openalex.org/W103340358","https://openalex.org/W141197097","https://openalex.org/W1545879303","https://openalex.org/W1989492890","https://openalex.org/W1991765252","https://openalex.org/W1996849720","https://openalex.org/W2021758280","https://openalex.org/W2023210016","https://openalex.org/W2057169561","https://openalex.org/W2063131624","https://openalex.org/W2072501842","https://openalex.org/W2073699560","https://openalex.org/W2117831564","https://openalex.org/W2151584222","https://openalex.org/W2152437528","https://openalex.org/W2154610494","https://openalex.org/W2160680261","https://openalex.org/W2295820528","https://openalex.org/W3000082853"],"related_works":["https://openalex.org/W4205996836","https://openalex.org/W2151692181","https://openalex.org/W4392498349","https://openalex.org/W2319693127","https://openalex.org/W308539617","https://openalex.org/W2072263576","https://openalex.org/W2474567666","https://openalex.org/W1940044583","https://openalex.org/W2806903871","https://openalex.org/W4320802053"],"abstract_inverted_index":{"SimRank":[0,41],"is":[1,50],"an":[2],"effective":[3],"and":[4],"widely":[5],"adopted":[6],"measure":[7],"to":[8,54,63],"quantify":[9],"the":[10,25,39,44,51],"structural":[11],"similarity":[12,30],"between":[13],"pairs":[14,35],"of":[15,27,36,46],"nodes":[16,37],"in":[17],"a":[18,60],"graph.":[19],"In":[20],"this":[21,49,56],"paper":[22],"we":[23],"study":[24],"problem":[26],"top-k":[28,66],"SimRank-based":[29],"join,":[31],"which":[32],"finds":[33],"k":[34],"with":[38],"largest":[40],"values.":[42],"To":[43],"best":[45],"our":[47,75],"knowledge,":[48],"first":[52],"attempt":[53],"address":[55],"problem.":[57],"We":[58],"propose":[59],"random-walk-based":[61],"method":[62,76],"efficiently":[64],"identify":[65],"pairs.":[67],"Experiment":[68],"results":[69],"on":[70],"real":[71],"datasets":[72],"show":[73],"that":[74],"significantly":[77],"outperforms":[78],"baseline":[79],"approaches.":[80]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":2},{"year":2019,"cited_by_count":2},{"year":2017,"cited_by_count":3},{"year":2016,"cited_by_count":2},{"year":2015,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
