[{"_1":2,"_3":-5,"_4":-5},"loaderData",{"_5":6,"_7":8,"_9":10},"actionData","errors","root",{"_268":269},"routes/_layout",{"_11":197},"routes/entity.$type.$id",{"_11":12,"_13":14,"_15":16,"_17":18,"_19":20,"_21":22,"_23":24,"_25":26,"_27":28},"dehydratedState",{"_72":73,"_74":75},"type","paper","id","paper_W2136935487","section","articles","entity",{"_15":16,"_29":30,"_31":-5,"_32":-5,"_33":-5,"_34":-5,"_35":36,"_37":38,"_39":-5,"_40":41,"_42":43,"_44":45,"_46":47,"_48":-5,"_49":50,"_51":30,"_52":53,"_54":55,"_56":57,"_58":-5,"_59":60,"_61":-5,"_62":63,"_64":-5,"_65":66,"_67":30,"_68":-5},"canonical","https://www.hugmap.com/entity/paper/paper_W2136935487/articles","siteBaseUrl","https://www.hugmap.com","sectionCount",0,"jsonLd","{\"@context\":\"https://schema.org\",\"@graph\":[{\"name\":\"Spectral clustering based on the graph p -Laplacian\",\"url\":\"https://www.hugmap.com/entity/paper/paper_W2136935487/articles\",\"@type\":\"ScholarlyArticle\",\"headline\":\"Spectral clustering based on the graph p -Laplacian\",\"datePublished\":\"2009-06-14\"},{\"@type\":\"CollectionPage\",\"name\":\"Spectral clustering based on the graph p -Laplacian 相关文章\",\"description\":\"Spectral clustering based on the graph p -Laplacian 在 HugMap 中的相关文章，含结构化关联信息与详情链接。\",\"url\":\"https://www.hugmap.com/entity/paper/paper_W2136935487/articles\",\"inLanguage\":\"zh-CN\"},{\"@type\":\"BreadcrumbList\",\"itemListElement\":[{\"@type\":\"ListItem\",\"position\":1,\"name\":\"首页\",\"item\":\"https://www.hugmap.com/\"},{\"@type\":\"ListItem\",\"position\":2,\"name\":\"论文\",\"item\":\"https://www.hugmap.com/search?type=paper\"},{\"@type\":\"ListItem\",\"position\":3,\"name\":\"Spectral clustering based on the graph p -Laplacian\",\"item\":\"https://www.hugmap.com/entity/paper/paper_W2136935487\"},{\"@type\":\"ListItem\",\"position\":4,\"name\":\"相关文章\",\"item\":\"https://www.hugmap.com/entity/paper/paper_W2136935487/articles\"}]}]}","name","Spectral clustering based on the graph p -Laplacian","nameZh","aliases","description","descriptionZh","status","active","externalIds","{\"openalex\":\"W2136935487\",\"doi\":\"10.1145/1553374.1553385\"}","properties","source","openalex","createdAt","2026-05-22T08:08:51.459852496","updatedAt","2026-05-22T08:08:51.459853897","doi","10.1145/1553374.1553385","arxivId","openalexId","W2136935487","title","abstractText","We present a generalized version of spectral clustering using the graph p-Laplacian, a nonlinear generalization of the standard graph Laplacian. We show that the second eigenvector of the graph p-Laplacian interpolates between a relaxation of the normalized and the Cheeger cut. Moreover, we prove that in the limit as p → 1 the cut found by thresholding the second eigenvector of the graph p-Laplacian converges to the optimal Cheeger cut. Furthermore, we provide an efficient numerical scheme to compute the second eigenvector of the graph p-Laplacian. The experiments show that the clustering found by p-spectral clustering is at least as good as normal spectral clustering, but often leads to significantly better results.","publishedDate","2009-06-14","publishedYear",2009,"venue","citationCount",255,"authors","keywords",[69,70,71],"pdfUrl","topVenue",false,"displayName","displayDescription","Complex Network Analysis Techniques","Graph theory and applications","Neural Networks and Applications","mutations",[],"queries",[76],{"_77":78,"_79":80,"_81":82,"_83":84},"dehydratedAt",1781205873399,"state",{"_86":87,"_88":89,"_90":78,"_91":-5,"_92":26,"_93":26,"_94":26,"_95":-5,"_96":-5,"_97":66,"_35":98,"_99":100},"queryKey",[85,19,14,16],"queryHash","[\"portal\",\"entity\",\"paper\",\"paper_W2136935487\"]","portal","data",{"_19":20,"_101":102,"_103":104,"_105":106,"_18":107,"_108":109,"_61":110},"dataUpdateCount",1,"dataUpdatedAt","error","errorUpdateCount","errorUpdatedAt","fetchFailureCount","fetchFailureReason","fetchMeta","isInvalidated","success","fetchStatus","idle","entityType","Paper","taxonomies",[153,154,155,156],"events",[],[],"technologies",[133,134],[111,112],{"_15":127,"_29":128,"_31":-5,"_32":-5,"_33":-5,"_34":-5,"_35":36,"_37":129,"_39":-5,"_40":41,"_42":130,"_44":131,"_118":132,"_51":-5,"_119":-5,"_120":-5,"_121":-5,"_59":-5,"_122":-5,"_123":-5,"_124":-5,"_125":-5,"_126":-5,"_67":128,"_68":-5},{"_15":113,"_29":114,"_31":-5,"_32":-5,"_33":-5,"_34":-5,"_35":36,"_37":115,"_39":-5,"_40":41,"_42":116,"_44":117,"_118":-5,"_51":-5,"_119":-5,"_120":-5,"_121":-5,"_59":-5,"_122":-5,"_123":-5,"_124":-5,"_125":-5,"_126":-5,"_67":114,"_68":-5},"person_A5023940535","Thomas Bühler","{\"openalex\":\"A5023940535\"}","2026-05-22T08:08:53.617104655","2026-05-22T08:08:53.617106772","orcid","affiliation","researchArea","paperCount","homepage","linkedin","twitter","github","hindex","person_A5025830560","Matthias Hein","{\"openalex\":\"A5025830560\",\"orcid\":\"0000-0002-8751-7760\"}","2026-05-14T21:35:02.428877684","2026-05-23T10:31:32.473372194","0000-0002-8751-7760",{"_15":149,"_29":71,"_31":-5,"_32":-5,"_33":-5,"_34":-5,"_35":36,"_37":150,"_39":-5,"_40":137,"_42":151,"_44":152,"_140":141,"_142":143,"_144":-5,"_145":146,"_147":-5,"_121":-5,"_148":-5,"_67":71,"_68":-5},{"_15":135,"_29":69,"_31":-5,"_32":-5,"_33":-5,"_34":-5,"_35":36,"_37":136,"_39":-5,"_40":137,"_42":138,"_44":139,"_140":141,"_142":143,"_144":-5,"_145":146,"_147":-5,"_121":-5,"_148":-5,"_67":69,"_68":-5},"tech_oa_T10064","{\"taxonomy_id\":\"tax_rd_T10064\"}","openalex_topics","2026-05-17T09:00:21.21739167","2026-05-17T09:00:21.217393598","techType","architecture","domain","Physical Sciences","proposedYear","maturityLevel","growing","wikipediaUrl","patentCount","tech_oa_T10320","{\"taxonomy_id\":\"tax_rd_T10320\"}","2026-05-17T09:00:01.255985187","2026-05-17T09:00:01.255987331",{"_15":187,"_29":188,"_31":-5,"_32":189,"_33":190,"_34":-5,"_35":36,"_37":-5,"_39":-5,"_40":191,"_42":-5,"_44":-5,"_161":192,"_163":-5,"_164":89,"_166":193,"_168":194,"_170":26,"_171":66,"_173":26,"_174":-5,"_67":188,"_68":190},{"_15":181,"_29":71,"_31":-5,"_32":-5,"_33":-5,"_34":-5,"_35":36,"_37":182,"_39":-5,"_40":41,"_42":183,"_44":184,"_161":162,"_163":-5,"_164":165,"_166":185,"_168":186,"_170":26,"_171":172,"_173":26,"_174":-5,"_67":71,"_68":-5},{"_15":175,"_29":69,"_31":-5,"_32":-5,"_33":-5,"_34":-5,"_35":36,"_37":176,"_39":-5,"_40":41,"_42":177,"_44":178,"_161":162,"_163":-5,"_164":165,"_166":179,"_168":180,"_170":26,"_171":172,"_173":26,"_174":-5,"_67":69,"_68":-5},{"_15":157,"_29":70,"_31":-5,"_32":-5,"_33":-5,"_34":-5,"_35":36,"_37":158,"_39":-5,"_40":41,"_42":159,"_44":160,"_161":162,"_163":-5,"_164":165,"_166":167,"_168":169,"_170":26,"_171":172,"_173":26,"_174":-5,"_67":70,"_68":-5},"tax_rd_T11476","{\"openalex_topic\":\"T11476\"}","2026-05-14T00:50:29.316961415","2026-05-23T10:31:33.214595894","taxonomyType","RESEARCH_DOMAIN","code","level",3,"parentId","tax_rd_2608","path","Physical Sciences/Mathematics/Geometry and Topology/Graph theory and applications","sortOrder","isLeaf",true,"entityCount","children","tax_rd_T10064","{\"openalex_topic\":\"T10064\"}","2026-05-14T00:32:00.37102808","2026-05-23T11:56:06.240435905","tax_rd_3109","Physical Sciences/Physics and Astronomy/Statistical and Nonlinear Physics/Complex Network Analysis Techniques","tax_rd_T10320","{\"openalex_topic\":\"T10320\"}","2026-05-14T00:27:16.239175777","2026-05-23T12:36:45.268811201","tax_rd_1702","Physical Sciences/Computer Science/Artificial Intelligence/Neural Networks and Applications","tax_ind_ai","人工智能",[195,196],"Artificial Intelligence","manual","INDUSTRY","tax_ind_root","科技行业/人工智能","AI","AI 技术",{"_72":198,"_74":199},[],[200],{"_77":201,"_79":202,"_81":203,"_83":204},1781205871518,{"_86":206,"_88":89,"_90":201,"_91":-5,"_92":26,"_93":26,"_94":26,"_95":-5,"_96":-5,"_97":66,"_35":98,"_99":100},[85,205],"[\"portal\",\"industries\"]","industries",[207,208,209,210,211,212,213,214,215,216,217,218],{"_15":187,"_29":188,"_31":-5,"_32":267,"_33":190,"_34":-5,"_35":36,"_37":-5,"_39":-5,"_40":191,"_42":-5,"_44":-5,"_161":192,"_163":-5,"_164":89,"_166":193,"_168":194,"_170":26,"_171":66,"_173":26,"_174":-5,"_67":188,"_68":190},{"_15":263,"_29":264,"_31":-5,"_32":-5,"_33":265,"_34":-5,"_35":36,"_37":-5,"_39":-5,"_40":191,"_42":-5,"_44":-5,"_161":192,"_163":-5,"_164":89,"_166":193,"_168":266,"_170":26,"_171":66,"_173":26,"_174":-5,"_67":264,"_68":265},{"_15":257,"_29":258,"_31":-5,"_32":259,"_33":260,"_34":-5,"_35":36,"_37":-5,"_39":-5,"_40":191,"_42":-5,"_44":-5,"_161":192,"_163":-5,"_164":89,"_166":193,"_168":261,"_170":26,"_171":66,"_173":26,"_174":-5,"_67":258,"_68":260},{"_15":253,"_29":254,"_31":-5,"_32":-5,"_33":255,"_34":-5,"_35":36,"_37":-5,"_39":-5,"_40":191,"_42":-5,"_44":-5,"_161":192,"_163":-5,"_164":89,"_166":193,"_168":256,"_170":26,"_171":66,"_173":26,"_174":-5,"_67":254,"_68":255},{"_15":249,"_29":250,"_31":-5,"_32":-5,"_33":251,"_34":-5,"_35":36,"_37":-5,"_39":-5,"_40":191,"_42":-5,"_44":-5,"_161":192,"_163":-5,"_164":89,"_166":193,"_168":252,"_170":26,"_171":66,"_173":26,"_174":-5,"_67":250,"_68":251},{"_15":243,"_29":244,"_31":-5,"_32":245,"_33":246,"_34":-5,"_35":36,"_37":-5,"_39":-5,"_40":191,"_42":-5,"_44":-5,"_161":192,"_163":-5,"_164":89,"_166":193,"_168":247,"_170":26,"_171":66,"_173":26,"_174":-5,"_67":244,"_68":246},{"_15":239,"_29":240,"_31":-5,"_32":-5,"_33":241,"_34":-5,"_35":36,"_37":-5,"_39":-5,"_40":191,"_42":-5,"_44":-5,"_161":192,"_163":-5,"_164":89,"_166":193,"_168":242,"_170":26,"_171":172,"_173":26,"_174":-5,"_67":240,"_68":241},{"_15":235,"_29":236,"_31":-5,"_32":-5,"_33":237,"_34":-5,"_35":36,"_37":-5,"_39":-5,"_40":191,"_42":-5,"_44":-5,"_161":192,"_163":-5,"_164":89,"_166":193,"_168":238,"_170":26,"_171":66,"_173":26,"_174":-5,"_67":236,"_68":237},{"_15":231,"_29":232,"_31":-5,"_32":-5,"_33":233,"_34":-5,"_35":36,"_37":-5,"_39":-5,"_40":191,"_42":-5,"_44":-5,"_161":192,"_163":-5,"_164":89,"_166":193,"_168":234,"_170":26,"_171":66,"_173":26,"_174":-5,"_67":232,"_68":233},{"_15":227,"_29":228,"_31":-5,"_32":-5,"_33":229,"_34":-5,"_35":36,"_37":-5,"_39":-5,"_40":191,"_42":-5,"_44":-5,"_161":192,"_163":-5,"_164":89,"_166":193,"_168":230,"_170":26,"_171":66,"_173":26,"_174":-5,"_67":228,"_68":229},{"_15":223,"_29":224,"_31":-5,"_32":-5,"_33":225,"_34":-5,"_35":36,"_37":-5,"_39":-5,"_40":191,"_42":-5,"_44":-5,"_161":192,"_163":-5,"_164":89,"_166":193,"_168":226,"_170":26,"_171":66,"_173":26,"_174":-5,"_67":224,"_68":225},{"_15":219,"_29":220,"_31":-5,"_32":-5,"_33":221,"_34":-5,"_35":36,"_37":-5,"_39":-5,"_40":191,"_42":-5,"_44":-5,"_161":192,"_163":-5,"_164":89,"_166":193,"_168":222,"_170":26,"_171":66,"_173":26,"_174":-5,"_67":220,"_68":221},"tax_ind_smartphone","智能手机","Smartphone","科技行业/智能手机","tax_ind_robot","机器人","Robotics","科技行业/机器人","tax_ind_wearable","智能穿戴","Smart Wearables","科技行业/智能穿戴","tax_ind_energy","新能源与智能制造","New Energy & Smart Manufacturing","科技行业/新能源与智能制造","tax_ind_biotech","生物科技","Biotech","科技行业/生物科技","tax_ind_quantum","量子计算","Quantum Computing","科技行业/量子计算","tax_ind_blockchain","区块链",[248],"Blockchain","科技行业/区块链","Web3","tax_ind_enterprise","企业软件","Enterprise Software","科技行业/企业软件","tax_ind_security","网络安全","Cybersecurity","科技行业/网络安全","tax_ind_semi","半导体",[262],"Semiconductor","科技行业/半导体","芯片","tax_ind_cloud","云计算","Cloud Computing","科技行业/云计算",[195,196],"env",{"_270":24},"SITE_BASE_URL"]

Spectral clustering based on the graph <i>p</i> -Laplacian 论文

Spectral clustering based on the graph <i>p</i> -Laplacian · 相关文章