{"id":"https://openalex.org/W2080072192","doi":"https://doi.org/10.1145/2433396.2433477","title":"Collective inference for network data with copula latent markov networks","display_name":"Collective inference for network data with copula latent markov networks","publication_year":2013,"publication_date":"2013-02-04","ids":{"openalex":"https://openalex.org/W2080072192","doi":"https://doi.org/10.1145/2433396.2433477","mag":"2080072192"},"language":"en","primary_location":{"id":"doi:10.1145/2433396.2433477","is_oa":false,"landing_page_url":"https://doi.org/10.1145/2433396.2433477","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the sixth ACM international conference on Web search and data mining","raw_type":"proceedings-article"},"type":"conference-paper","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/A5019579560","display_name":"Rongjing Xiang","orcid":null},"institutions":[{"id":"https://openalex.org/I219193219","display_name":"Purdue University West Lafayette","ror":"https://ror.org/02dqehb95","country_code":"US","type":"education","lineage":["https://openalex.org/I219193219"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Rongjing Xiang","raw_affiliation_strings":["Purdue University, West Lafayette, IN, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Purdue University, West Lafayette, IN, USA","institution_ids":["https://openalex.org/I219193219"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5064439579","display_name":"Jennifer Neville","orcid":"https://orcid.org/0000-0001-8108-4899"},"institutions":[{"id":"https://openalex.org/I219193219","display_name":"Purdue University West Lafayette","ror":"https://ror.org/02dqehb95","country_code":"US","type":"education","lineage":["https://openalex.org/I219193219"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Jennifer Neville","raw_affiliation_strings":["Purdue University, West Lafayette, IN, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Purdue University, West Lafayette, IN, USA","institution_ids":["https://openalex.org/I219193219"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I219193219"],"apc_list":null,"apc_paid":null,"fwci":2.5169,"has_fulltext":false,"cited_by_count":15,"citation_normalized_percentile":{"value":0.90930662,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":89,"max":98},"biblio":{"volume":"6","issue":null,"first_page":"647","last_page":"656"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11303","display_name":"Bayesian Modeling and Causal Inference","score":0.9995999932289124,"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/T11303","display_name":"Bayesian Modeling and Causal Inference","score":0.9995999932289124,"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":0.9984999895095825,"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":0.9948999881744385,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.7142503261566162},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.602034866809845},{"id":"https://openalex.org/keywords/markov-chain","display_name":"Markov chain","score":0.5634706020355225},{"id":"https://openalex.org/keywords/statistical-relational-learning","display_name":"Statistical relational learning","score":0.5373398661613464},{"id":"https://openalex.org/keywords/probabilistic-latent-semantic-analysis","display_name":"Probabilistic latent semantic analysis","score":0.4774887263774872},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.449624627828598},{"id":"https://openalex.org/keywords/variable-order-markov-model","display_name":"Variable-order Markov model","score":0.4383789598941803},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4333062171936035},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.4322489798069},{"id":"https://openalex.org/keywords/latent-variable","display_name":"Latent variable","score":0.4126679301261902},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.3792908191680908},{"id":"https://openalex.org/keywords/markov-model","display_name":"Markov model","score":0.3736557364463806},{"id":"https://openalex.org/keywords/relational-database","display_name":"Relational database","score":0.29099881649017334}],"concepts":[{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.7142503261566162},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.602034866809845},{"id":"https://openalex.org/C98763669","wikidata":"https://www.wikidata.org/wiki/Q176645","display_name":"Markov chain","level":2,"score":0.5634706020355225},{"id":"https://openalex.org/C177877439","wikidata":"https://www.wikidata.org/wiki/Q7604413","display_name":"Statistical relational learning","level":3,"score":0.5373398661613464},{"id":"https://openalex.org/C112933361","wikidata":"https://www.wikidata.org/wiki/Q2845258","display_name":"Probabilistic latent semantic analysis","level":2,"score":0.4774887263774872},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.449624627828598},{"id":"https://openalex.org/C54907487","wikidata":"https://www.wikidata.org/wiki/Q7915688","display_name":"Variable-order Markov model","level":4,"score":0.4383789598941803},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4333062171936035},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4322489798069},{"id":"https://openalex.org/C51167844","wikidata":"https://www.wikidata.org/wiki/Q4422623","display_name":"Latent variable","level":2,"score":0.4126679301261902},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.3792908191680908},{"id":"https://openalex.org/C163836022","wikidata":"https://www.wikidata.org/wiki/Q6771326","display_name":"Markov model","level":3,"score":0.3736557364463806},{"id":"https://openalex.org/C5655090","wikidata":"https://www.wikidata.org/wiki/Q192588","display_name":"Relational database","level":2,"score":0.29099881649017334}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1145/2433396.2433477","is_oa":false,"landing_page_url":"https://doi.org/10.1145/2433396.2433477","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the sixth ACM international conference on Web search and data mining","raw_type":"proceedings-article"},{"id":"pmh:oai:CiteSeerX.psu:10.1.1.298.5436","is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.298.5436","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://www.cs.purdue.edu/homes/neville/papers/xiang-neville-wsdm2013.pdf","raw_type":"text"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/1","score":0.699999988079071,"display_name":"No poverty"}],"awards":[],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":52,"referenced_works":["https://openalex.org/W105785986","https://openalex.org/W196162695","https://openalex.org/W1497163089","https://openalex.org/W1653444175","https://openalex.org/W1905898145","https://openalex.org/W1908728294","https://openalex.org/W1934021597","https://openalex.org/W1965520710","https://openalex.org/W1977970897","https://openalex.org/W1994473607","https://openalex.org/W2015953751","https://openalex.org/W2021774297","https://openalex.org/W2042557026","https://openalex.org/W2045012549","https://openalex.org/W2046253692","https://openalex.org/W2066459332","https://openalex.org/W2075672181","https://openalex.org/W2098961198","https://openalex.org/W2099442408","https://openalex.org/W2101705355","https://openalex.org/W2107107106","https://openalex.org/W2111521164","https://openalex.org/W2119436275","https://openalex.org/W2129458072","https://openalex.org/W2133299088","https://openalex.org/W2137213923","https://openalex.org/W2137253512","https://openalex.org/W2137892504","https://openalex.org/W2139823104","https://openalex.org/W2148534792","https://openalex.org/W2153959628","https://openalex.org/W2154455818","https://openalex.org/W2158122241","https://openalex.org/W2158535911","https://openalex.org/W2164319128","https://openalex.org/W2167947196","https://openalex.org/W2168673502","https://openalex.org/W2295202330","https://openalex.org/W2401131346","https://openalex.org/W2798180542","https://openalex.org/W2949194372","https://openalex.org/W2962735828","https://openalex.org/W2999625102","https://openalex.org/W4237080436","https://openalex.org/W6639917875","https://openalex.org/W6675204935","https://openalex.org/W6678119076","https://openalex.org/W6682494755","https://openalex.org/W6682657107","https://openalex.org/W6697359872","https://openalex.org/W6763581751","https://openalex.org/W7058302550"],"related_works":["https://openalex.org/W2884410131","https://openalex.org/W1603253275","https://openalex.org/W120501756","https://openalex.org/W111011176","https://openalex.org/W2138996412","https://openalex.org/W2951183560","https://openalex.org/W2763292376","https://openalex.org/W2097596242","https://openalex.org/W2906932471","https://openalex.org/W1985514205"],"abstract_inverted_index":{"The":[0],"popularity":[1],"of":[2,13,95,158,223,231],"online":[3],"social":[4,7],"networks":[5,24,40,67],"and":[6,21,113,149,188,205],"media":[8],"has":[9,194],"increased":[10],"the":[11,62,80,83,93,96,100,105,151,189],"amount":[12],"linked":[14],"data":[15,187],"available":[16],"in":[17,32,58,117,140,162,183],"Web":[18],"domains.":[19],"Relational":[20],"Gaussian":[22,38],"Markov":[23,39,66,213],"have":[25,168,176],"both":[26],"been":[27,170,178,196],"applied":[28],"successfully":[29],"for":[30,86,172,180],"classification":[31,182,226,234],"these":[33],"relational":[34,65,129,160,225,233],"settings.":[35],"However,":[36],"since":[37,76],"model":[41,68,130],"joint":[42],"distributions":[43,70],"over":[44,71,228],"continuous":[45,164],"label":[46,73,143],"space,":[47,74],"it":[48],"is":[49],"difficult":[50],"to":[51,54,137],"use":[52,136],"them":[53],"reason":[55],"about":[56],"uncertainty":[57],"discrete":[59,72,142],"labels.":[60],"On":[61],"other":[63],"hand,":[64],"probability":[69,85],"but":[75],"they":[77,175],"condition":[78],"on":[79,92,132,217],"graph":[81],"structure,":[82],"marginal":[84],"an":[87,201],"instance":[88],"will":[89,107],"vary":[90],"based":[91,131,216],"structure":[94],"subnetwork":[97],"observed":[98],"around":[99],"instance.":[101],"This":[102],"implies":[103],"that":[104,208],"marginals":[106,148],"not":[108,177,195],"be":[109],"identical":[110,147],"across":[111],"instances":[112],"can":[114],"sometimes":[115],"result":[116],"poor":[118],"prediction":[119],"performance.":[120],"In":[121],"this":[122],"work,":[123],"we":[124],"propose":[125],"a":[126,141,163,221,229],"novel":[127],"latent":[128],"copulas":[133,167],"which":[134],"allows":[135],"make":[138],"predictions":[139],"space":[144],"while":[145],"ensuring":[146],"at":[150],"same":[152],"time":[153],"incorporating":[154],"some":[155],"desirable":[156],"properties":[157],"modeling":[159],"dependencies":[161],"space.":[165],"While":[166],"recently":[169],"used":[171,179],"descriptive":[173],"modeling,":[174],"collective":[181],"large":[184],"scale":[185],"network":[186],"associated":[190],"conditional":[191],"inference":[192,203,219],"problem":[193],"considered":[197],"before.":[198],"We":[199],"develop":[200],"approximate":[202,218],"algorithm,":[204],"demonstrate":[206],"empirically":[207],"our":[209],"proposed":[210],"Copula":[211],"Latent":[212],"Network":[214],"models":[215,227],"outperform":[220],"number":[222],"competing":[224],"range":[230],"real-world":[232],"tasks.":[235]},"counts_by_year":[{"year":2022,"cited_by_count":1},{"year":2019,"cited_by_count":2},{"year":2017,"cited_by_count":1},{"year":2016,"cited_by_count":1},{"year":2015,"cited_by_count":5},{"year":2014,"cited_by_count":5}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}