{"id":"https://openalex.org/W3040662337","doi":"https://doi.org/10.1145/3394486.3403222","title":"Minimizing Localized Ratio Cut Objectives in Hypergraphs","display_name":"Minimizing Localized Ratio Cut Objectives in Hypergraphs","publication_year":2020,"publication_date":"2020-08-20","ids":{"openalex":"https://openalex.org/W3040662337","doi":"https://doi.org/10.1145/3394486.3403222","mag":"3040662337"},"language":"en","primary_location":{"id":"doi:10.1145/3394486.3403222","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3394486.3403222","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3394486.3403222","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery &amp; Data Mining","raw_type":"proceedings-article"},"type":"preprint","indexed_in":["arxiv","crossref","datacite"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://dl.acm.org/doi/pdf/10.1145/3394486.3403222","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5046315164","display_name":"Nate Veldt","orcid":"https://orcid.org/0000-0002-0117-3304"},"institutions":[{"id":"https://openalex.org/I205783295","display_name":"Cornell University","ror":"https://ror.org/05bnh6r87","country_code":"US","type":"education","lineage":["https://openalex.org/I205783295"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Nate Veldt","raw_affiliation_strings":["Cornell University, Ithaca, NY, USA","cornell University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Cornell University, Ithaca, NY, USA","institution_ids":["https://openalex.org/I205783295"]},{"raw_affiliation_string":"cornell University","institution_ids":["https://openalex.org/I205783295"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5009319874","display_name":"Austin R. Benson","orcid":"https://orcid.org/0000-0001-6110-1583"},"institutions":[{"id":"https://openalex.org/I205783295","display_name":"Cornell University","ror":"https://ror.org/05bnh6r87","country_code":"US","type":"education","lineage":["https://openalex.org/I205783295"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Austin R. Benson","raw_affiliation_strings":["Cornell University, Ithaca, NY, USA","cornell University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Cornell University, Ithaca, NY, USA","institution_ids":["https://openalex.org/I205783295"]},{"raw_affiliation_string":"cornell University","institution_ids":["https://openalex.org/I205783295"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5055710645","display_name":"Jon Kleinberg","orcid":"https://orcid.org/0000-0002-1929-2512"},"institutions":[{"id":"https://openalex.org/I205783295","display_name":"Cornell University","ror":"https://ror.org/05bnh6r87","country_code":"US","type":"education","lineage":["https://openalex.org/I205783295"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Jon Kleinberg","raw_affiliation_strings":["Cornell University, Ithaca, NY, USA","cornell University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Cornell University, Ithaca, NY, USA","institution_ids":["https://openalex.org/I205783295"]},{"raw_affiliation_string":"cornell University","institution_ids":["https://openalex.org/I205783295"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I205783295"],"apc_list":null,"apc_paid":null,"fwci":0.3943,"has_fulltext":true,"cited_by_count":3,"citation_normalized_percentile":{"value":0.57857863,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":94},"biblio":{"volume":null,"issue":null,"first_page":"1708","last_page":"1718"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10064","display_name":"Complex Network Analysis Techniques","score":0.9993000030517578,"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":0.9993000030517578,"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":0.9965000152587891,"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/T11273","display_name":"Advanced Graph Neural Networks","score":0.9951000213623047,"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/hypergraph","display_name":"Hypergraph","score":0.9709111452102661},{"id":"https://openalex.org/keywords/cluster-analysis","display_name":"Cluster analysis","score":0.7026017904281616},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.5447940826416016},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.5347498655319214},{"id":"https://openalex.org/keywords/cluster","display_name":"Cluster (spacecraft)","score":0.5335890054702759},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5332216024398804},{"id":"https://openalex.org/keywords/clustering-coefficient","display_name":"Clustering coefficient","score":0.5237212777137756},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.44368869066238403},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.3606768250465393},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.3483082056045532},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.32698965072631836},{"id":"https://openalex.org/keywords/combinatorics","display_name":"Combinatorics","score":0.2488916516304016},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.12063592672348022}],"concepts":[{"id":"https://openalex.org/C2781221856","wikidata":"https://www.wikidata.org/wiki/Q840247","display_name":"Hypergraph","level":2,"score":0.9709111452102661},{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.7026017904281616},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.5447940826416016},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.5347498655319214},{"id":"https://openalex.org/C164866538","wikidata":"https://www.wikidata.org/wiki/Q367351","display_name":"Cluster (spacecraft)","level":2,"score":0.5335890054702759},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5332216024398804},{"id":"https://openalex.org/C22047676","wikidata":"https://www.wikidata.org/wiki/Q898680","display_name":"Clustering coefficient","level":3,"score":0.5237212777137756},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.44368869066238403},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.3606768250465393},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3483082056045532},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.32698965072631836},{"id":"https://openalex.org/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","level":1,"score":0.2488916516304016},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.12063592672348022},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0}],"mesh":[],"locations_count":4,"locations":[{"id":"doi:10.1145/3394486.3403222","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3394486.3403222","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3394486.3403222","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery &amp; Data Mining","raw_type":"proceedings-article"},{"id":"pmh:oai:arXiv.org:2002.09441","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2002.09441","pdf_url":"https://arxiv.org/pdf/2002.09441","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"text"},{"id":"mag:3040662337","is_oa":true,"landing_page_url":"https://arxiv.org/pdf/2002.09441.pdf","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"arXiv (Cornell University)","raw_type":null},{"id":"doi:10.48550/arxiv.2002.09441","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2002.09441","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.1145/3394486.3403222","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3394486.3403222","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3394486.3403222","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery &amp; Data Mining","raw_type":"proceedings-article"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G2687707333","display_name":null,"funder_award_id":"DMS-1830274","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G3884127047","display_name":"ATD: Collaborative Research: Statistically Principled Real-Time Detection of Anomalies for Temporal Network Data","funder_award_id":"1830274","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G4553646684","display_name":null,"funder_award_id":"W911NF19-1-0057","funder_id":"https://openalex.org/F4320338281","funder_display_name":"Army Research Office"},{"id":"https://openalex.org/G6497051240","display_name":null,"funder_award_id":"W911NF19-1-0057, MURI","funder_id":"https://openalex.org/F4320338281","funder_display_name":"Army Research Office"}],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"},{"id":"https://openalex.org/F4320306078","display_name":"U.S. Department of Defense","ror":"https://ror.org/0447fe631"},{"id":"https://openalex.org/F4320314817","display_name":"Heising-Simons Foundation","ror":"https://ror.org/01mp52y34"},{"id":"https://openalex.org/F4320333591","display_name":"Multidisciplinary University Research Initiative","ror":null},{"id":"https://openalex.org/F4320338281","display_name":"Army Research Office","ror":"https://ror.org/05epdh915"}],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W3040662337.pdf","grobid_xml":"https://content.openalex.org/works/W3040662337.grobid-xml"},"referenced_works_count":39,"referenced_works":["https://openalex.org/W143174683","https://openalex.org/W1483985387","https://openalex.org/W1578099820","https://openalex.org/W1585385982","https://openalex.org/W1588155622","https://openalex.org/W1669076080","https://openalex.org/W1898392233","https://openalex.org/W1973113780","https://openalex.org/W1983383464","https://openalex.org/W2005676288","https://openalex.org/W2011646234","https://openalex.org/W2012329067","https://openalex.org/W2035575256","https://openalex.org/W2050511894","https://openalex.org/W2059933941","https://openalex.org/W2086254934","https://openalex.org/W2088759008","https://openalex.org/W2118544668","https://openalex.org/W2121947440","https://openalex.org/W2148070710","https://openalex.org/W2158579916","https://openalex.org/W2170057991","https://openalex.org/W2593313548","https://openalex.org/W2743418339","https://openalex.org/W2787887656","https://openalex.org/W2809192845","https://openalex.org/W2946721537","https://openalex.org/W2962804156","https://openalex.org/W2962935106","https://openalex.org/W2963189394","https://openalex.org/W2963361089","https://openalex.org/W2971196067","https://openalex.org/W2971267355","https://openalex.org/W2982394413","https://openalex.org/W2999163148","https://openalex.org/W2999782657","https://openalex.org/W3101676988","https://openalex.org/W3126033509","https://openalex.org/W4234920499"],"related_works":["https://openalex.org/W3007143691","https://openalex.org/W3080389405","https://openalex.org/W3152772521","https://openalex.org/W2999163148","https://openalex.org/W3124903408","https://openalex.org/W3099517909","https://openalex.org/W2784329359","https://openalex.org/W2232162753","https://openalex.org/W1459831051","https://openalex.org/W3180415497","https://openalex.org/W2756494378","https://openalex.org/W3053565644","https://openalex.org/W2752425240","https://openalex.org/W3094166141","https://openalex.org/W3081374773","https://openalex.org/W2092235254","https://openalex.org/W2898502017","https://openalex.org/W3106228791","https://openalex.org/W3035789548","https://openalex.org/W3213393422"],"abstract_inverted_index":{"Hypergraphs":[0],"are":[1,34,52],"a":[2,54,66,89,93,105],"useful":[3],"abstraction":[4],"for":[5,37,58,68],"modeling":[6],"multiway":[7],"relationships":[8],"in":[9,24,61,88,101],"data,":[10],"and":[11,32,91],"hypergraph":[12,70,90,96],"clustering":[13,27,60,71],"is":[14],"the":[15,115],"task":[16],"of":[17,20,85,95,109],"detecting":[18,38],"groups":[19],"closely":[21],"related":[22],"nodes":[23,87,110],"such":[25],"data.Graph":[26],"has":[28],"been":[29],"studied":[30],"extensively,":[31],"there":[33,51],"numerous":[35],"methods":[36],"small,":[39],"localized":[40,59,75],"clusters":[41],"without":[42],"having":[43],"to":[44,103],"explore":[45],"an":[46,82],"entire":[47],"input":[48,83,116],"graph.":[49],"However,":[50],"only":[53],"few":[55],"specialized":[56],"approaches":[57],"hypergraphs.":[62],"Here":[63],"we":[64],"present":[65],"framework":[67,80],"local":[69],"based":[72],"on":[73],"minimizing":[74],"ratio":[76],"cut":[77,99],"objectives.":[78],"Our":[79],"takes":[81],"set":[84],"reference":[86],"solves":[92],"sequence":[94],"minimum":[97],"s-t":[98],"problems":[100],"order":[102],"identify":[104],"nearby":[106],"well-connected":[107],"cluster":[108],"that":[111],"overlaps":[112],"substantially":[113],"with":[114],"set.":[117]},"counts_by_year":[{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":1},{"year":2020,"cited_by_count":1}],"updated_date":"2026-07-01T08:55:40.977307","created_date":"2025-10-10T00:00:00"}
