{"id":"https://openalex.org/W2553023665","doi":"https://doi.org/10.1109/mlsp.2016.7738845","title":"Bayesian latent feature modeling for modeling bipartite networks with overlapping groups","display_name":"Bayesian latent feature modeling for modeling bipartite networks with overlapping groups","publication_year":2016,"publication_date":"2016-09-01","ids":{"openalex":"https://openalex.org/W2553023665","doi":"https://doi.org/10.1109/mlsp.2016.7738845","mag":"2553023665"},"language":"en","primary_location":{"id":"doi:10.1109/mlsp.2016.7738845","is_oa":false,"landing_page_url":"https://doi.org/10.1109/mlsp.2016.7738845","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 IEEE 26th International Workshop on Machine Learning for Signal Processing (MLSP)","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/A5076853629","display_name":"Philip H. Jorgensen","orcid":null},"institutions":[{"id":"https://openalex.org/I96673099","display_name":"Technical University of Denmark","ror":"https://ror.org/04qtj9h94","country_code":"DK","type":"education","lineage":["https://openalex.org/I96673099"]}],"countries":["DK"],"is_corresponding":false,"raw_author_name":"Philip H. Jorgensen","raw_affiliation_strings":["Department of Applied Mathematics and Computer Science, Technical University of Denmark, Kgs. Lyngby, Denmark"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Applied Mathematics and Computer Science, Technical University of Denmark, Kgs. Lyngby, Denmark","institution_ids":["https://openalex.org/I96673099"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5059840085","display_name":"Morten M\u00f8rup","orcid":"https://orcid.org/0000-0003-4985-4368"},"institutions":[{"id":"https://openalex.org/I96673099","display_name":"Technical University of Denmark","ror":"https://ror.org/04qtj9h94","country_code":"DK","type":"education","lineage":["https://openalex.org/I96673099"]}],"countries":["DK"],"is_corresponding":false,"raw_author_name":"Morten Morup","raw_affiliation_strings":["Department of Applied Mathematics and Computer Science, Technical University of Denmark, Kgs. Lyngby, Denmark"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Applied Mathematics and Computer Science, Technical University of Denmark, Kgs. Lyngby, Denmark","institution_ids":["https://openalex.org/I96673099"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5073902328","display_name":"Mikkel N. Schmidt","orcid":"https://orcid.org/0000-0001-6927-8869"},"institutions":[{"id":"https://openalex.org/I96673099","display_name":"Technical University of Denmark","ror":"https://ror.org/04qtj9h94","country_code":"DK","type":"education","lineage":["https://openalex.org/I96673099"]}],"countries":["DK"],"is_corresponding":false,"raw_author_name":"Mikkel N. Schmidt","raw_affiliation_strings":["Department of Applied Mathematics and Computer Science, Technical University of Denmark, Kgs. Lyngby, Denmark"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Applied Mathematics and Computer Science, Technical University of Denmark, Kgs. Lyngby, Denmark","institution_ids":["https://openalex.org/I96673099"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5037856158","display_name":"Tue Herlau","orcid":"https://orcid.org/0000-0001-7288-6953"},"institutions":[{"id":"https://openalex.org/I96673099","display_name":"Technical University of Denmark","ror":"https://ror.org/04qtj9h94","country_code":"DK","type":"education","lineage":["https://openalex.org/I96673099"]}],"countries":["DK"],"is_corresponding":false,"raw_author_name":"Tue Herlau","raw_affiliation_strings":["Department of Applied Mathematics and Computer Science, Technical University of Denmark, Kgs. Lyngby, Denmark"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Applied Mathematics and Computer Science, Technical University of Denmark, Kgs. Lyngby, Denmark","institution_ids":["https://openalex.org/I96673099"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I96673099"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.16915688,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"6"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10064","display_name":"Complex Network Analysis Techniques","score":0.9998000264167786,"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":0.9998000264167786,"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/T10887","display_name":"Bioinformatics and Genomic Networks","score":0.9937999844551086,"subfield":{"id":"https://openalex.org/subfields/1312","display_name":"Molecular Biology"},"field":{"id":"https://openalex.org/fields/13","display_name":"Biochemistry, Genetics and Molecular Biology"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},{"id":"https://openalex.org/T11273","display_name":"Advanced Graph Neural Networks","score":0.9908999800682068,"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/feature","display_name":"Feature (linguistics)","score":0.7430338263511658},{"id":"https://openalex.org/keywords/latent-class-model","display_name":"Latent class model","score":0.6063591241836548},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.5883700251579285},{"id":"https://openalex.org/keywords/feature-model","display_name":"Feature model","score":0.5778087377548218},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.565644383430481},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.5645924806594849},{"id":"https://openalex.org/keywords/bipartite-graph","display_name":"Bipartite graph","score":0.4925869107246399},{"id":"https://openalex.org/keywords/binary-number","display_name":"Binary number","score":0.4606611132621765},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4544716775417328},{"id":"https://openalex.org/keywords/probabilistic-latent-semantic-analysis","display_name":"Probabilistic latent semantic analysis","score":0.4370664060115814},{"id":"https://openalex.org/keywords/bayesian-network","display_name":"Bayesian network","score":0.43558943271636963},{"id":"https://openalex.org/keywords/mixture-model","display_name":"Mixture model","score":0.4175829589366913},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.33736535906791687},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.3241904675960541},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.2865276634693146},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.25673535466194153}],"concepts":[{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.7430338263511658},{"id":"https://openalex.org/C70727504","wikidata":"https://www.wikidata.org/wiki/Q1806878","display_name":"Latent class model","level":2,"score":0.6063591241836548},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.5883700251579285},{"id":"https://openalex.org/C101814296","wikidata":"https://www.wikidata.org/wiki/Q5439685","display_name":"Feature model","level":3,"score":0.5778087377548218},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.565644383430481},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.5645924806594849},{"id":"https://openalex.org/C197657726","wikidata":"https://www.wikidata.org/wiki/Q174733","display_name":"Bipartite graph","level":3,"score":0.4925869107246399},{"id":"https://openalex.org/C48372109","wikidata":"https://www.wikidata.org/wiki/Q3913","display_name":"Binary number","level":2,"score":0.4606611132621765},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4544716775417328},{"id":"https://openalex.org/C112933361","wikidata":"https://www.wikidata.org/wiki/Q2845258","display_name":"Probabilistic latent semantic analysis","level":2,"score":0.4370664060115814},{"id":"https://openalex.org/C33724603","wikidata":"https://www.wikidata.org/wiki/Q812540","display_name":"Bayesian network","level":2,"score":0.43558943271636963},{"id":"https://openalex.org/C61224824","wikidata":"https://www.wikidata.org/wiki/Q2260434","display_name":"Mixture model","level":2,"score":0.4175829589366913},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.33736535906791687},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.3241904675960541},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.2865276634693146},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.25673535466194153},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.0},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C94375191","wikidata":"https://www.wikidata.org/wiki/Q11205","display_name":"Arithmetic","level":1,"score":0.0},{"id":"https://openalex.org/C2777904410","wikidata":"https://www.wikidata.org/wiki/Q7397","display_name":"Software","level":2,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0},{"id":"https://openalex.org/C17744445","wikidata":"https://www.wikidata.org/wiki/Q36442","display_name":"Political science","level":0,"score":0.0},{"id":"https://openalex.org/C94625758","wikidata":"https://www.wikidata.org/wiki/Q7163","display_name":"Politics","level":2,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C199539241","wikidata":"https://www.wikidata.org/wiki/Q7748","display_name":"Law","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/mlsp.2016.7738845","is_oa":false,"landing_page_url":"https://doi.org/10.1109/mlsp.2016.7738845","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 IEEE 26th International Workshop on Machine Learning for Signal Processing (MLSP)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Reduced inequalities","id":"https://metadata.un.org/sdg/10","score":0.8199999928474426}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":22,"referenced_works":["https://openalex.org/W1850515838","https://openalex.org/W1975389666","https://openalex.org/W1977713568","https://openalex.org/W1987885244","https://openalex.org/W2070323919","https://openalex.org/W2097266862","https://openalex.org/W2106813372","https://openalex.org/W2127048411","https://openalex.org/W2133133974","https://openalex.org/W2135029798","https://openalex.org/W2147841915","https://openalex.org/W2151936673","https://openalex.org/W2158535911","https://openalex.org/W2159407085","https://openalex.org/W2400765812","https://openalex.org/W2806351592","https://openalex.org/W3102641634","https://openalex.org/W3188864409","https://openalex.org/W4292081303","https://openalex.org/W6639041904","https://openalex.org/W6680012447","https://openalex.org/W6712706839"],"related_works":["https://openalex.org/W1505497582","https://openalex.org/W2242083226","https://openalex.org/W1603253275","https://openalex.org/W4295205890","https://openalex.org/W2953287518","https://openalex.org/W4293521981","https://openalex.org/W1604092967","https://openalex.org/W2270140793","https://openalex.org/W2763292376","https://openalex.org/W823895634"],"abstract_inverted_index":{"Bi-partite":[0],"networks":[1,128,179,190],"are":[2],"commonly":[3],"modelled":[4],"using":[5,191],"latent":[6,9,15,30,55,74,148,174,193],"class":[7,16],"or":[8],"feature":[10,31,48,56,149,175,194],"models.":[11,160],"Whereas":[12],"the":[13,23,39,43,47,82,89,115,130,135,143,165],"existing":[14,29,159],"models":[17,32],"admit":[18,35],"marginalization":[19,37,61],"of":[20,25,38,62,84,109,114,151,164,167],"parameters":[21,40],"specifying":[22],"strength":[24,45],"interaction":[26,44,63],"between":[27,97],"groups,":[28],"do":[33],"not":[34],"analytical":[36,60],"accounting":[41,185],"for":[42,184,186],"within":[46,92],"representation.":[49],"We":[50,76,140],"propose":[51,77],"a":[52,78,146,181],"new":[53,147,182],"binary":[54,173,192],"model":[57,67,100,133,144],"that":[58,66,88,103,142,199],"admits":[59],"strengths":[64],"such":[65,87],"inference":[68,171],"reduces":[69],"to":[70,73,129,158,172],"assigning":[71],"nodes":[72],"features.":[75],"constraint":[79],"inspired":[80],"by":[81,205],"notion":[83,166],"community":[85],"structure":[86,152,187,202],"edge":[90],"density":[91],"groups":[93],"is":[94,120],"higher":[95],"than":[96],"groups.":[98],"Our":[99,161],"further":[101],"assumes":[102],"entities":[104],"can":[105],"have":[106],"different":[107],"propensities":[108],"generating":[110],"links":[111],"in":[112,154,177,188],"one":[113],"modes.":[116],"The":[117],"proposed":[118],"framework":[119,183],"contrasted":[121],"on":[122],"both":[123],"synthetic":[124],"and":[125,134,169],"real":[126],"bi-partite":[127,189],"infinite":[131,136],"relational":[132],"Bernoulli":[137],"mixture":[138],"model.":[139],"find":[141],"provides":[145,180],"representation":[150],"while":[153],"link-prediction":[155],"performing":[156],"close":[157],"current":[162],"extension":[163],"communities":[168],"collapsed":[170],"representations":[176,195,198],"bipartite":[178],"providing":[196],"interpretable":[197],"well":[200],"characterize":[201],"as":[203],"quantified":[204],"link":[206],"prediction.":[207]},"counts_by_year":[],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}