{"id":"https://openalex.org/W3008519205","doi":"https://doi.org/10.3233/faia200282","title":"Struct-MMSB: Mixed Membership Stochastic Blockmodels with Interpretable Structured Priors","display_name":"Struct-MMSB: Mixed Membership Stochastic Blockmodels with Interpretable Structured Priors","publication_year":2020,"publication_date":"2020-01-01","ids":{"openalex":"https://openalex.org/W3008519205","doi":"https://doi.org/10.3233/faia200282","mag":"3008519205"},"language":"en","primary_location":{"id":"doi:10.3233/faia200282","is_oa":true,"landing_page_url":"https://doi.org/10.3233/faia200282","pdf_url":null,"source":{"id":"https://openalex.org/S4210201731","display_name":"Frontiers in artificial intelligence and applications","issn_l":"0922-6389","issn":["0922-6389","1879-8314"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":"cc-by-nc","license_id":"https://openalex.org/licenses/cc-by-nc","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Frontiers in Artificial Intelligence and Applications","raw_type":"book-chapter"},"type":"book-chapter","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"hybrid","oa_url":"https://doi.org/10.3233/faia200282","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5100333737","display_name":"Yue Zhang","orcid":"https://orcid.org/0000-0002-6304-3812"},"institutions":[{"id":"https://openalex.org/I123946342","display_name":"Binghamton University","ror":"https://ror.org/008rmbt77","country_code":"US","type":"education","lineage":["https://openalex.org/I123946342"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Zhang Yue","raw_affiliation_strings":["Binghamton University, Binghamton, United States"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Binghamton University, Binghamton, United States","institution_ids":["https://openalex.org/I123946342"]}]},{"author_position":"last","author":{"id":null,"display_name":"Ramesh Arti","orcid":null},"institutions":[{"id":"https://openalex.org/I123946342","display_name":"Binghamton University","ror":"https://ror.org/008rmbt77","country_code":"US","type":"education","lineage":["https://openalex.org/I123946342"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Ramesh Arti","raw_affiliation_strings":["Binghamton University, Binghamton, United States"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Binghamton University, Binghamton, United States","institution_ids":["https://openalex.org/I123946342"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I123946342"],"apc_list":null,"apc_paid":null,"fwci":0.9318,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.74123255,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":93},"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":0.9990000128746033,"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":0.9990000128746033,"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/T11273","display_name":"Advanced Graph Neural Networks","score":0.9950000047683716,"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/T10637","display_name":"Advanced Clustering Algorithms Research","score":0.9919999837875366,"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/prior-probability","display_name":"Prior probability","score":0.5653510093688965},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5303666591644287},{"id":"https://openalex.org/keywords/interpretability","display_name":"Interpretability","score":0.48911339044570923},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.4805460572242737},{"id":"https://openalex.org/keywords/latent-variable","display_name":"Latent variable","score":0.4426056742668152},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.40702447295188904},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.36356016993522644},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.3450847864151001},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.33925768733024597},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.14423194527626038}],"concepts":[{"id":"https://openalex.org/C177769412","wikidata":"https://www.wikidata.org/wiki/Q278090","display_name":"Prior probability","level":3,"score":0.5653510093688965},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5303666591644287},{"id":"https://openalex.org/C2781067378","wikidata":"https://www.wikidata.org/wiki/Q17027399","display_name":"Interpretability","level":2,"score":0.48911339044570923},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.4805460572242737},{"id":"https://openalex.org/C51167844","wikidata":"https://www.wikidata.org/wiki/Q4422623","display_name":"Latent variable","level":2,"score":0.4426056742668152},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.40702447295188904},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.36356016993522644},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.3450847864151001},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.33925768733024597},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.14423194527626038}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.3233/faia200282","is_oa":true,"landing_page_url":"https://doi.org/10.3233/faia200282","pdf_url":null,"source":{"id":"https://openalex.org/S4210201731","display_name":"Frontiers in artificial intelligence and applications","issn_l":"0922-6389","issn":["0922-6389","1879-8314"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":"cc-by-nc","license_id":"https://openalex.org/licenses/cc-by-nc","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Frontiers in Artificial Intelligence and Applications","raw_type":"book-chapter"}],"best_oa_location":{"id":"doi:10.3233/faia200282","is_oa":true,"landing_page_url":"https://doi.org/10.3233/faia200282","pdf_url":null,"source":{"id":"https://openalex.org/S4210201731","display_name":"Frontiers in artificial intelligence and applications","issn_l":"0922-6389","issn":["0922-6389","1879-8314"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":"cc-by-nc","license_id":"https://openalex.org/licenses/cc-by-nc","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Frontiers in Artificial Intelligence and Applications","raw_type":"book-chapter"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":26,"referenced_works":["https://openalex.org/W588062613","https://openalex.org/W1665569402","https://openalex.org/W2008225289","https://openalex.org/W2013661955","https://openalex.org/W2107107106","https://openalex.org/W2156338064","https://openalex.org/W2158535911","https://openalex.org/W2164278908","https://openalex.org/W2166848942","https://openalex.org/W2168420470","https://openalex.org/W2275569093","https://openalex.org/W2277093821","https://openalex.org/W2465934790","https://openalex.org/W2469279958","https://openalex.org/W2490208908","https://openalex.org/W2626837583","https://openalex.org/W2792402990","https://openalex.org/W2798962412","https://openalex.org/W2806351858","https://openalex.org/W2951101948","https://openalex.org/W2951604453","https://openalex.org/W2952962974","https://openalex.org/W2957415859","https://openalex.org/W2964311892","https://openalex.org/W3102647957","https://openalex.org/W3104833085"],"related_works":["https://openalex.org/W2905433371","https://openalex.org/W2888392564","https://openalex.org/W4310278675","https://openalex.org/W4388422664","https://openalex.org/W4390569940","https://openalex.org/W4361193272","https://openalex.org/W2963326959","https://openalex.org/W4388685194","https://openalex.org/W4312407344","https://openalex.org/W3168675052"],"abstract_inverted_index":{"The":[0],"mixed":[1,20],"membership":[2,21,38,156],"stochastic":[3,174],"blockmodel":[4],"(MMSB)":[5],"is":[6,112,134],"a":[7,18,47,70,79,92,115,149,172,181],"popular":[8],"framework":[9],"for":[10,23],"community":[11],"detection":[12],"and":[13,104,155,169,180,204,208,226],"network":[14,233],"generation.":[15],"It":[16],"learns":[17,166],"low-rank":[19],"representation":[22],"each":[24],"node":[25,100],"across":[26,198],"communities":[27],"by":[28],"exploiting":[29],"the":[30,37,41,128,177,185],"underlying":[31],"graph":[32,58],"structure.":[33],"MMSB":[34,74],"assumes":[35],"that":[36,60,77,120,165,210],"distributions":[39],"of":[40,136,152,176,187,202,219],"nodes":[42],"are":[43,213],"independently":[44],"drawn":[45],"from":[46],"Dirichlet":[48],"distribution,":[49],"which":[50,126],"limits":[51],"its":[52],"capability":[53],"to":[54,95,183,215,231],"model":[55,96,111,133,194],"highly":[56],"correlated":[57],"structures":[59],"exist":[61],"in":[62,140,223],"real-world":[63,141],"networks.":[64],"In":[65],"this":[66],"paper,":[67],"we":[68],"present":[69,159],"flexible":[71],"richly":[72],"structured":[73,93,189],"model,":[75,85],"\\textit{Struct-MMSB},":[76],"uses":[78,121],"recently":[80],"developed":[81],"statistical":[82],"relational":[83],"learning":[84,137],"hinge-loss":[86],"Markov":[87],"random":[88],"fields":[89],"(HL-MRFs),":[90],"as":[91,148],"prior":[94],"complex":[97,150],"dependencies":[98],"among":[99],"attributes,":[101],"multi-relational":[102],"links,":[103],"their":[105],"relationship":[106],"with":[107],"mixed-membership":[108],"distributions.":[109,157],"Our":[110],"specified":[113],"using":[114],"probabilistic":[116],"programming":[117],"templating":[118],"language":[119],"weighted":[122],"first-order":[123],"logic":[124],"rules,":[125],"enhances":[127],"model's":[129],"interpretability.":[130],"Further,":[131],"our":[132,193,211],"capable":[135],"latent":[138,145,167],"characteristics":[139],"networks":[142,203],"via":[143],"meaningful":[144],"variables":[146,168],"encoded":[147],"combination":[151],"observed":[153],"features":[154],"We":[158,191],"an":[160,217],"expectation-maximization":[161],"based":[162],"inference":[163,178],"algorithm":[164],"parameters":[170],"iteratively,":[171],"scalable":[173],"variation":[175],"algorithm,":[179],"method":[182],"learn":[184],"weights":[186],"HL-MRF":[188],"priors.":[190],"evaluate":[192],"on":[195,221],"six":[196],"datasets":[197],"three":[199],"different":[200],"types":[201],"corresponding":[205],"modeling":[206],"scenarios":[207],"demonstrate":[209],"models":[212],"able":[214],"achieve":[216],"improvement":[218],"15\\%":[220],"average":[222],"test":[224],"log-likelihood":[225],"faster":[227],"convergence":[228],"when":[229],"compared":[230],"state-of-the-art":[232],"models.":[234]},"counts_by_year":[{"year":2021,"cited_by_count":1}],"updated_date":"2025-12-22T23:10:17.713674","created_date":"2020-03-06T00:00:00"}