{"id":"https://openalex.org/W4233921497","doi":"https://doi.org/10.1109/asonam.2014.6921566","title":"A unified generative Bayesian model for community discovery and role assignment based upon latent interaction factors","display_name":"A unified generative Bayesian model for community discovery and role assignment based upon latent interaction factors","publication_year":2014,"publication_date":"2014-08-01","ids":{"openalex":"https://openalex.org/W4233921497","doi":"https://doi.org/10.1109/asonam.2014.6921566"},"language":"en","primary_location":{"id":"doi:10.1109/asonam.2014.6921566","is_oa":false,"landing_page_url":"https://doi.org/10.1109/asonam.2014.6921566","pdf_url":null,"source":{"id":"https://openalex.org/S4363608209","display_name":"2014 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2014)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2014 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2014)","raw_type":"proceedings-article"},"type":"article","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/A5076708603","display_name":"Gianni Costa","orcid":"https://orcid.org/0000-0003-2267-9280"},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Gianni Costa","raw_affiliation_strings":["ICAR-CNR, Italy"],"affiliations":[{"raw_affiliation_string":"ICAR-CNR, Italy","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5078466922","display_name":"Riccardo Ortale","orcid":"https://orcid.org/0000-0002-1763-9121"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Riccardo Ortale","raw_affiliation_strings":["ICAR-CNR, Italy"],"affiliations":[{"raw_affiliation_string":"ICAR-CNR, Italy","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5076708603"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":1.2287,"has_fulltext":false,"cited_by_count":7,"citation_normalized_percentile":{"value":0.83860759,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":96},"biblio":{"volume":null,"issue":null,"first_page":"93","last_page":"100"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10064","display_name":"Complex Network Analysis Techniques","score":0.9998999834060669,"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":0.9998999834060669,"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/T12592","display_name":"Opinion Dynamics and Social Influence","score":0.9994000196456909,"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/T11252","display_name":"Evolutionary Game Theory and Cooperation","score":0.9900000095367432,"subfield":{"id":"https://openalex.org/subfields/3312","display_name":"Sociology and Political Science"},"field":{"id":"https://openalex.org/fields/33","display_name":"Social Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7271637320518494},{"id":"https://openalex.org/keywords/generative-grammar","display_name":"Generative grammar","score":0.6735129952430725},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.6305529475212097},{"id":"https://openalex.org/keywords/generative-model","display_name":"Generative model","score":0.6197463870048523},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5491901636123657},{"id":"https://openalex.org/keywords/node","display_name":"Node (physics)","score":0.5244596004486084},{"id":"https://openalex.org/keywords/graphical-model","display_name":"Graphical model","score":0.5073520541191101},{"id":"https://openalex.org/keywords/markov-chain-monte-carlo","display_name":"Markov chain Monte Carlo","score":0.48351410031318665},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4784807562828064},{"id":"https://openalex.org/keywords/probabilistic-logic","display_name":"Probabilistic logic","score":0.46972429752349854},{"id":"https://openalex.org/keywords/bayesian-inference","display_name":"Bayesian inference","score":0.44118532538414},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.44038864970207214},{"id":"https://openalex.org/keywords/bayesian-network","display_name":"Bayesian network","score":0.4216378629207611},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.4019961357116699}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7271637320518494},{"id":"https://openalex.org/C39890363","wikidata":"https://www.wikidata.org/wiki/Q36108","display_name":"Generative grammar","level":2,"score":0.6735129952430725},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.6305529475212097},{"id":"https://openalex.org/C167966045","wikidata":"https://www.wikidata.org/wiki/Q5532625","display_name":"Generative model","level":3,"score":0.6197463870048523},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5491901636123657},{"id":"https://openalex.org/C62611344","wikidata":"https://www.wikidata.org/wiki/Q1062658","display_name":"Node (physics)","level":2,"score":0.5244596004486084},{"id":"https://openalex.org/C155846161","wikidata":"https://www.wikidata.org/wiki/Q1143367","display_name":"Graphical model","level":2,"score":0.5073520541191101},{"id":"https://openalex.org/C111350023","wikidata":"https://www.wikidata.org/wiki/Q1191869","display_name":"Markov chain Monte Carlo","level":3,"score":0.48351410031318665},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4784807562828064},{"id":"https://openalex.org/C49937458","wikidata":"https://www.wikidata.org/wiki/Q2599292","display_name":"Probabilistic logic","level":2,"score":0.46972429752349854},{"id":"https://openalex.org/C160234255","wikidata":"https://www.wikidata.org/wiki/Q812535","display_name":"Bayesian inference","level":3,"score":0.44118532538414},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.44038864970207214},{"id":"https://openalex.org/C33724603","wikidata":"https://www.wikidata.org/wiki/Q812540","display_name":"Bayesian network","level":2,"score":0.4216378629207611},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.4019961357116699},{"id":"https://openalex.org/C66938386","wikidata":"https://www.wikidata.org/wiki/Q633538","display_name":"Structural engineering","level":1,"score":0.0},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/asonam.2014.6921566","is_oa":false,"landing_page_url":"https://doi.org/10.1109/asonam.2014.6921566","pdf_url":null,"source":{"id":"https://openalex.org/S4363608209","display_name":"2014 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2014)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2014 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2014)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/10","score":0.4300000071525574,"display_name":"Reduced inequalities"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":33,"referenced_works":["https://openalex.org/W950821216","https://openalex.org/W1532017810","https://openalex.org/W1544007753","https://openalex.org/W1568436970","https://openalex.org/W1995996823","https://openalex.org/W2001130345","https://openalex.org/W2004539934","https://openalex.org/W2011559506","https://openalex.org/W2017939541","https://openalex.org/W2060622768","https://openalex.org/W2090876554","https://openalex.org/W2097565406","https://openalex.org/W2100348674","https://openalex.org/W2110591510","https://openalex.org/W2111002549","https://openalex.org/W2111565546","https://openalex.org/W2112090702","https://openalex.org/W2113959870","https://openalex.org/W2120043163","https://openalex.org/W2127048411","https://openalex.org/W2127375420","https://openalex.org/W2131689821","https://openalex.org/W2136796925","https://openalex.org/W2160938187","https://openalex.org/W2170246630","https://openalex.org/W2797328151","https://openalex.org/W3102641634","https://openalex.org/W4292081303","https://openalex.org/W6631953657","https://openalex.org/W6640570641","https://openalex.org/W6674832525","https://openalex.org/W6676274861","https://openalex.org/W6713243226"],"related_works":["https://openalex.org/W3183113072","https://openalex.org/W2126934800","https://openalex.org/W2187019487","https://openalex.org/W1715419791","https://openalex.org/W643788828","https://openalex.org/W2158940596","https://openalex.org/W4200510307","https://openalex.org/W3087071515","https://openalex.org/W3125134981","https://openalex.org/W2188106786"],"abstract_inverted_index":{"We":[0],"propose":[1],"a":[2,28,115],"generative":[3,69],"probabilistic":[4],"approach":[5,126],"to":[6,77],"modeling":[7],"interactions":[8,37],"in":[9,18,34,127],"directed":[10,86],"graphs":[11],"for":[12,101],"unveiling":[13],"the":[14,23,59,72,102,121,136],"participation":[15],"of":[16,56,63,71,124,129,135],"nodes":[17,76],"multiple":[19],"communities":[20,65,78],"along":[21],"with":[22,79],"roles":[24,81,139],"played":[25],"therein.":[26],"Precisely,":[27],"hierarchical":[29],"Bayesian":[30],"model":[31,74,104],"is":[32],"developed,":[33],"which":[35],"node":[36],"are":[38,53,61,89],"governed":[39],"by":[40,91],"latent":[41],"explicative":[42],"reasons":[43],"regarded":[44],"as":[45],"personal":[46,93],"and":[47,66,82,94,132,140],"contextual":[48,95],"interaction":[49,96,141],"factors.":[50,142],"The":[51,68],"former":[52],"inherently":[54],"descriptive":[55],"nodes,":[57],"while":[58],"latter":[60],"characterizations":[62],"individual":[64],"roles.":[67],"process":[70],"devised":[73],"assigns":[75],"respective":[80],"connects":[83],"them":[84],"through":[85],"links,":[87],"that":[88],"ruled":[90],"their":[92],"factors.We":[97],"derive":[98],"posterior":[99],"inference":[100],"presented":[103],"based":[105],"on":[106,118],"approximate":[107],"Markov-Chain":[108],"Monte-Carlo":[109],"methods.":[110],"Finally,":[111],"we":[112],"demonstrate":[113],"via":[114],"comparative":[116],"analysis":[117],"real-world":[119],"networks":[120],"superior":[122],"performance":[123],"our":[125],"terms":[128],"community":[130],"compactness":[131],"predictive":[133],"power":[134],"discovered":[137],"communities,":[138]},"counts_by_year":[{"year":2019,"cited_by_count":2},{"year":2018,"cited_by_count":1},{"year":2017,"cited_by_count":1},{"year":2016,"cited_by_count":2},{"year":2014,"cited_by_count":1}],"updated_date":"2026-01-13T01:12:25.745995","created_date":"2025-10-10T00:00:00"}