{"id":"https://openalex.org/W327576307","doi":null,"title":"Mixed Membership Stochastic Block Models for Relational Data with Application to Protein-Protein Interactions","display_name":"Mixed Membership Stochastic Block Models for Relational Data with Application to Protein-Protein Interactions","publication_year":2006,"publication_date":"2006-01-01","ids":{"openalex":"https://openalex.org/W327576307","mag":"327576307"},"language":"en","primary_location":{"id":"pmh:oai:CiteSeerX.psu:10.1.1.139.6993","doi":null,"is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.139.6993","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://www.cs.princeton.edu/~blei/papers/AiroldiBleiFienbergXing2008.pdf","raw_type":"text"},"volume":null,"type":"article","indexed_in":[],"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/A5035398709","display_name":"Edoardo M. Airoldi","orcid":"https://orcid.org/0000-0002-3512-0542","observed_orcids":["https://orcid.org/0000-0002-3512-0542"]},"institutions":[{"id":"https://openalex.org/I74973139","display_name":"Carnegie Mellon University","ror":"https://ror.org/05x2bcf33","country_code":"US","type":"education","lineage":["https://openalex.org/I74973139"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Edoardo M. Airoldi","raw_affiliation_strings":["Carnegie Mellon University"],"raw_orcid":"https://orcid.org/0000-0002-3512-0542","affiliations":[{"raw_affiliation_string":"Carnegie Mellon University","institution_ids":["https://openalex.org/I74973139"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5070920982","display_name":"David M. Blei","orcid":"https://orcid.org/0000-0002-5588-4611","observed_orcids":["https://orcid.org/0000-0002-5588-4611"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"David M. Blei","raw_affiliation_strings":[],"raw_orcid":"https://orcid.org/0000-0002-5588-4611","affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5108604141","display_name":"Stephen E. Fienberg","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Stephen E. Fienberg","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5009547049","display_name":"Eric P. Xing","orcid":"https://orcid.org/0009-0005-9158-4201","observed_orcids":["https://orcid.org/0009-0005-9158-4201","https://orcid.org/0000-0002-3683-4280"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Eric P. Xing","raw_affiliation_strings":[],"raw_orcid":"https://orcid.org/0009-0005-9158-4201","affiliations":[]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":3.3102,"has_fulltext":false,"cited_by_count":108,"citation_normalized_percentile":{"value":0.92128963,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":93,"max":100},"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/T10064","display_name":"Complex Network Analysis Techniques","score":0.9028000235557556,"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":0.9028000235557556,"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/T11901","display_name":"Bayesian Methods and Mixture Models","score":0.01899999938905239,"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/T11303","display_name":"Bayesian Modeling and Causal Inference","score":0.012400000356137753,"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/mixed-membership-stochastic-blockmodel","display_name":"mixed membership stochastic blockmodel","score":0.9150000214576721},{"id":"https://openalex.org/keywords/relational-data","display_name":"relational data","score":0.6790000200271606},{"id":"https://openalex.org/keywords/latent-variable-modeling","display_name":"latent variable models","score":0.8880000114440918},{"id":"https://openalex.org/keywords/variational-bayesian-inference","display_name":"variational inference","score":0.8989999890327454},{"id":"https://openalex.org/keywords/protein-protein-interaction-network","display_name":"protein-protein interaction network","score":0.800000011920929},{"id":"https://openalex.org/keywords/social-networks","display_name":"social networks","score":0.9710000157356262},{"id":"https://openalex.org/keywords/gene-regulatory-networks","display_name":"gene regulatory networks","score":0.7879999876022339},{"id":"https://openalex.org/keywords/email-networks","display_name":"email networks","score":0.703000009059906},{"id":"https://openalex.org/keywords/community-structure","display_name":"community structure","score":0.4309999942779541}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6968019604682922},{"id":"https://openalex.org/C49937458","wikidata":"https://www.wikidata.org/wiki/Q2599292","display_name":"Probabilistic logic","level":2,"score":0.5045808553695679},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.490612655878067},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.4883846640586853},{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.47364866733551025},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.46176570653915405},{"id":"https://openalex.org/C31170391","wikidata":"https://www.wikidata.org/wiki/Q188619","display_name":"Hierarchy","level":2,"score":0.4596857726573944},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.45356935262680054},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4449043869972229},{"id":"https://openalex.org/C184337299","wikidata":"https://www.wikidata.org/wiki/Q1437428","display_name":"Semantics (computer science)","level":2,"score":0.4442082643508911},{"id":"https://openalex.org/C177877439","wikidata":"https://www.wikidata.org/wiki/Q7604413","display_name":"Statistical relational learning","level":3,"score":0.41672641038894653},{"id":"https://openalex.org/C5655090","wikidata":"https://www.wikidata.org/wiki/Q192588","display_name":"Relational database","level":2,"score":0.41452106833457947},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.39062952995300293},{"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/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.0},{"id":"https://openalex.org/C34447519","wikidata":"https://www.wikidata.org/wiki/Q179522","display_name":"Market economy","level":1,"score":0.0}],"mesh":[],"locations_count":3,"locations":[{"id":"pmh:oai:CiteSeerX.psu:10.1.1.139.6993","doi":null,"is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.139.6993","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://www.cs.princeton.edu/~blei/papers/AiroldiBleiFienbergXing2008.pdf","raw_type":"text"},{"id":"pmh:oai:CiteSeerX.psu:10.1.1.145.4295","doi":null,"is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.145.4295","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://www.cs.cmu.edu/~epxing/papers/2008/paper90_final.pdf","raw_type":"text"},{"id":"mag:327576307","doi":null,"is_oa":false,"landing_page_url":"https://people.eecs.berkeley.edu/~jordan/sail/readings/airoldi-blei-xing-fienberg.pdf","pdf_url":null,"source":null,"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":null}],"best_oa_location":null,"sustainable_development_goals":[],"sustainable_development_goals_aurora":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":23,"referenced_works":["https://openalex.org/W190008395","https://openalex.org/W1494853941","https://openalex.org/W1573937741","https://openalex.org/W1587900512","https://openalex.org/W1593528099","https://openalex.org/W1880262756","https://openalex.org/W1908709377","https://openalex.org/W2013502943","https://openalex.org/W2037036397","https://openalex.org/W2044808496","https://openalex.org/W2066459332","https://openalex.org/W2084619201","https://openalex.org/W2098126593","https://openalex.org/W2108346334","https://openalex.org/W2120340025","https://openalex.org/W2124354935","https://openalex.org/W2127345773","https://openalex.org/W2134206588","https://openalex.org/W2137471889","https://openalex.org/W2141042406","https://openalex.org/W2145677303","https://openalex.org/W2152322845","https://openalex.org/W2797343967"],"related_works":["https://openalex.org/W7098192783","https://openalex.org/W7098321888","https://openalex.org/W7095391950","https://openalex.org/W1542746141","https://openalex.org/W7096623410","https://openalex.org/W2013661955","https://openalex.org/W2785762350","https://openalex.org/W7099584242","https://openalex.org/W7096607892","https://openalex.org/W2082867555"],"abstract_inverted_index":{"In":[0,53],"many":[1],"settings,":[2],"such":[3,35],"as":[4],"protein":[5,125],"interactions":[6],"and":[7,15,85,124],"gene":[8],"regulatory":[9],"networks,":[10,17],"collections":[11],"of":[12,21,28,32,60,79,82,114],"author-recipient":[13],"email,":[14],"social":[16,122],"the":[18,44,94,112],"data":[19,36],"consist":[20],"pairwise":[22,65],"measurements,":[23],"e.g.,":[24],"presence":[25],"or":[26,47],"absence":[27],"links":[29],"between":[30],"pairs":[31],"objects.":[33],"Analyzing":[34],"with":[37,119],"probabilistic":[38],"models":[39,63],"requires":[40],"non-standard":[41],"assumptions,":[42],"since":[43],"usual":[45],"independence":[46],"exchangeability":[48],"assumptions":[49],"no":[50],"longer":[51],"hold.":[52],"this":[54,73],"paper,":[55],"we":[56],"introduce":[57],"a":[58,76,86,100],"class":[59,74],"latent":[61],"variable":[62],"for":[64,105],"measurements:":[66],"mixed":[67,115],"membership":[68,116],"stochastic":[69,117],"blockmodels.":[70],"Models":[71],"in":[72,93],"combine":[75],"global":[77],"model":[78,88],"dense":[80],"patches":[81],"connectivity":[83],"(blockmodel)":[84],"local":[87],"to":[89,121],"instantiate":[90],"node-specific":[91],"variability":[92],"connections":[95],"(mixed":[96],"membership).":[97],"We":[98,110],"develop":[99],"general":[101],"variational":[102],"inference":[103],"algorithm":[104],"fast":[106],"approximate":[107],"posterior":[108],"inference.":[109],"demonstrate":[111],"advantages":[113],"blockmodel":[118],"applications":[120],"networks":[123],"interaction":[126],"networks.":[127],"1":[128]},"counts_by_year":[{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":3},{"year":2022,"cited_by_count":2},{"year":2021,"cited_by_count":7},{"year":2020,"cited_by_count":10},{"year":2019,"cited_by_count":7},{"year":2018,"cited_by_count":2},{"year":2017,"cited_by_count":13},{"year":2016,"cited_by_count":7},{"year":2015,"cited_by_count":8},{"year":2014,"cited_by_count":6},{"year":2013,"cited_by_count":5},{"year":2012,"cited_by_count":10}],"updated_date":"2026-10-16T09:59:22.235173","created_date":"2016-06-24T00:00:00"}