{"id":"https://openalex.org/W2805332017","doi":"https://doi.org/10.48550/arxiv.1806.01468","title":"Understanding Regularized Spectral Clustering via Graph Conductance","display_name":"Understanding Regularized Spectral Clustering via Graph Conductance","publication_year":2018,"publication_date":"2018-06-05","ids":{"openalex":"https://openalex.org/W2805332017","doi":"https://doi.org/10.48550/arxiv.1806.01468","mag":"2805332017"},"language":"en","primary_location":{"id":"pmh:oai:arXiv.org:1806.01468","doi":null,"is_oa":true,"landing_page_url":"http://arxiv.org/abs/1806.01468","pdf_url":"https://arxiv.org/pdf/1806.01468","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"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":null,"raw_type":"text"},"volume":null,"type":"preprint","indexed_in":["arxiv","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/1806.01468","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5100758234","display_name":"Yilin Zhang","orcid":"https://orcid.org/0000-0002-4448-0005","observed_orcids":["https://orcid.org/0000-0002-4448-0005","https://orcid.org/0009-0009-3631-892X","https://orcid.org/0009-0009-7861-6220"]},"institutions":[{"id":"https://openalex.org/I135310074","display_name":"University of Wisconsin\u2013Madison","ror":"https://ror.org/01y2jtd41","country_code":"US","type":"education","lineage":["https://openalex.org/I135310074"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Zhang, Yilin","raw_affiliation_strings":["Department of Statistics University of Wisconsin-Madison Madison , WI 53706"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Statistics University of Wisconsin-Madison Madison , WI 53706","institution_ids":["https://openalex.org/I135310074"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5072922535","display_name":"Karl Rohe","orcid":"https://orcid.org/0000-0003-4904-8876","observed_orcids":["https://orcid.org/0000-0003-4904-8876"]},"institutions":[{"id":"https://openalex.org/I135310074","display_name":"University of Wisconsin\u2013Madison","ror":"https://ror.org/01y2jtd41","country_code":"US","type":"education","lineage":["https://openalex.org/I135310074"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Rohe, Karl","raw_affiliation_strings":["Department of Statistics University of Wisconsin-Madison Madison , WI 53706"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Statistics University of Wisconsin-Madison Madison , WI 53706","institution_ids":["https://openalex.org/I135310074"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I135310074"],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":true,"cited_by_count":53,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10637","display_name":"Advanced Clustering Algorithms Research","score":0.9711999893188477,"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/T10637","display_name":"Advanced Clustering Algorithms Research","score":0.9711999893188477,"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":0.008100000210106373,"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/T10057","display_name":"Face and Expression Recognition","score":0.00419999985024333,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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/spectral-clustering","display_name":"spectral clustering","score":0.796999990940094},{"id":"https://openalex.org/keywords/graph-conductance","display_name":"graph conductance","score":0.9909999966621399},{"id":"https://openalex.org/keywords/regularization","display_name":"regularization","score":0.847000002861023},{"id":"https://openalex.org/keywords/cheeger-inequality","display_name":"Cheeger inequality","score":0.7839999794960022},{"id":"https://openalex.org/keywords/sparse-graphs","display_name":"sparse graphs","score":0.7480000257492065},{"id":"https://openalex.org/keywords/stochastic-graphs","display_name":"stochastic graphs","score":0.9520000219345093},{"id":"https://openalex.org/keywords/overfitting","display_name":"overfitting","score":0.7429999709129333},{"id":"https://openalex.org/keywords/graph-partitioning","display_name":"graph partitioning","score":0.5410000085830688}],"concepts":[{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.7281684279441833},{"id":"https://openalex.org/C105611402","wikidata":"https://www.wikidata.org/wiki/Q2976589","display_name":"Spectral clustering","level":3,"score":0.7060370445251465},{"id":"https://openalex.org/C22019652","wikidata":"https://www.wikidata.org/wiki/Q331309","display_name":"Overfitting","level":3,"score":0.5235345363616943},{"id":"https://openalex.org/C121932024","wikidata":"https://www.wikidata.org/wiki/Q5159376","display_name":"Conductance","level":2,"score":0.5077327489852905},{"id":"https://openalex.org/C2776135515","wikidata":"https://www.wikidata.org/wiki/Q17143721","display_name":"Regularization (linguistics)","level":2,"score":0.5000166893005371},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.4890483319759369},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.4477241635322571},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.42462867498397827},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.37951895594596863},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.36402058601379395},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.33891868591308594},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.32084208726882935},{"id":"https://openalex.org/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","level":1,"score":0.23406317830085754},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.07292094826698303}],"mesh":[],"locations_count":3,"locations":[{"id":"pmh:oai:arXiv.org:1806.01468","doi":null,"is_oa":true,"landing_page_url":"http://arxiv.org/abs/1806.01468","pdf_url":"https://arxiv.org/pdf/1806.01468","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"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":null,"raw_type":"text"},{"id":"mag:2952432209","doi":null,"is_oa":true,"landing_page_url":"http://export.arxiv.org/pdf/1806.01468","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"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.1806.01468","doi":"https://doi.org/10.48550/arxiv.1806.01468","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.1806.01468","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"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":"public-domain","license_id":"https://openalex.org/licenses/public-domain","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:1806.01468","doi":null,"is_oa":true,"landing_page_url":"http://arxiv.org/abs/1806.01468","pdf_url":"https://arxiv.org/pdf/1806.01468","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"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":null,"raw_type":"text"},"sustainable_development_goals":[],"sustainable_development_goals_aurora":[],"study_designs":[],"awards":[{"id":"https://openalex.org/G2253484458","display_name":"A Spectral Framework for Network-Driven Sampling","funder_award_id":"1612456","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G5385673730","display_name":null,"funder_award_id":"W911NF-15-1-","funder_id":"https://openalex.org/F4320338281","funder_display_name":"Army Research Office"},{"id":"https://openalex.org/G6710498217","display_name":null,"funder_award_id":"W911NF-15-1-0423","funder_id":"https://openalex.org/F4320338281","funder_display_name":"Army Research Office"},{"id":"https://openalex.org/G7452299184","display_name":null,"funder_award_id":"W911NF","funder_id":"https://openalex.org/F4320338281","funder_display_name":"Army Research Office"},{"id":"https://openalex.org/G8257659198","display_name":null,"funder_award_id":"W911NF-15","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/F4320338281","display_name":"Army Research Office","ror":"https://ror.org/05epdh915"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W4362597605","https://openalex.org/W1574414179","https://openalex.org/W3009056573","https://openalex.org/W4297676672","https://openalex.org/W2922073769","https://openalex.org/W4281702477","https://openalex.org/W2490526372","https://openalex.org/W4376166922","https://openalex.org/W4378510483","https://openalex.org/W1482912984"],"abstract_inverted_index":{"This":[0],"paper":[1,71],"uses":[2],"the":[3,14,21,43,46,70,89,98,101,140,160,186],"relationship":[4],"between":[5],"graph":[6,47,90,103,111],"conductance":[7,53,91,99,112],"and":[8,19,30,82,165,195],"spectral":[9,17,80,114,124,150,190],"clustering":[10,18,62,81,115,151],"to":[11,42,56,88,113,122,136,157,174,198],"study":[12],"(i)":[13],"failures":[15],"of":[16,23,36,45,69,78,128,162,189],"(ii)":[20],"benefits":[22],"regularization.":[24],"The":[25,66],"explanation":[26],"is":[27,54,86,133],"simple.":[28],"Sparse":[29],"stochastic":[31,166],"graphs":[32],"create":[33],"a":[34,74,93,163],"lot":[35],"small":[37,137],"trees":[38],"that":[39,84,109,146],"are":[40],"connected":[41],"core":[44],"by":[48],"only":[49],"one":[50],"edge.":[51],"Graph":[52],"sensitive":[55,135],"these":[57,143,178,200],"noisy":[58],"`dangling":[59],"sets'.":[60],"Spectral":[61],"inherits":[63],"this":[64,170,175],"sensitivity.":[65],"second":[67],"part":[68],"starts":[72],"from":[73,149],"previously":[75],"proposed":[76],"form":[77],"regularized":[79,102,123],"shows":[83],"it":[85,132],"related":[87],"on":[92,100],"`regularized":[94],"graph'.":[95],"We":[96,192],"call":[97],"CoreCut.":[104],"Based":[105],"upon":[106],"previous":[107],"arguments":[108],"relate":[110],"(e.g.":[116],"Cheeger":[117],"inequality),":[118],"minimizing":[119],"CoreCut":[120,129],"relaxes":[121],"clustering.":[125,191],"Simple":[126],"inspection":[127],"reveals":[130],"why":[131],"less":[134],"cuts":[138],"in":[139,159],"graph.":[141,167],"Together,":[142],"results":[144,179],"show":[145],"unbalanced":[147],"partitions":[148],"can":[152,184],"be":[153],"understood":[154],"as":[155],"overfitting":[156],"noise":[158],"periphery":[161],"sparse":[164],"Regularization":[168],"fixes":[169],"overfitting.":[171],"In":[172],"addition":[173],"statistical":[176],"benefit,":[177],"also":[180],"demonstrate":[181],"how":[182],"regularization":[183],"improve":[185],"computational":[187],"speed":[188],"provide":[193],"simulations":[194],"data":[196],"examples":[197],"illustrate":[199],"results.":[201]},"counts_by_year":[{"year":2025,"cited_by_count":3},{"year":2024,"cited_by_count":4},{"year":2023,"cited_by_count":9},{"year":2022,"cited_by_count":8},{"year":2021,"cited_by_count":9},{"year":2020,"cited_by_count":10},{"year":2019,"cited_by_count":8},{"year":2018,"cited_by_count":2}],"updated_date":"2026-10-16T09:59:22.235173","created_date":"2025-10-10T00:00:00"}
