{"id":"https://openalex.org/W3162828355","doi":"https://doi.org/10.1109/icassp39728.2021.9413541","title":"Learning Mixed Membership from Adjacency Graph Via Systematic Edge Query: Identifiability and Algorithm","display_name":"Learning Mixed Membership from Adjacency Graph Via Systematic Edge Query: Identifiability and Algorithm","publication_year":2021,"publication_date":"2021-05-13","ids":{"openalex":"https://openalex.org/W3162828355","doi":"https://doi.org/10.1109/icassp39728.2021.9413541","mag":"3162828355"},"language":"en","primary_location":{"id":"doi:10.1109/icassp39728.2021.9413541","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icassp39728.2021.9413541","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","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/A5005841750","display_name":"Shahana Ibrahim","orcid":"https://orcid.org/0000-0003-1951-5234"},"institutions":[{"id":"https://openalex.org/I131249849","display_name":"Oregon State University","ror":"https://ror.org/00ysfqy60","country_code":"US","type":"education","lineage":["https://openalex.org/I131249849"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Shahana Ibrahim","raw_affiliation_strings":["School of Electrical Engineering & Computer Science, Oregon State University, Corvallis, OR, United States"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Electrical Engineering & Computer Science, Oregon State University, Corvallis, OR, United States","institution_ids":["https://openalex.org/I131249849"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5015075381","display_name":"Xiao Fu","orcid":"https://orcid.org/0000-0003-4847-9586"},"institutions":[{"id":"https://openalex.org/I131249849","display_name":"Oregon State University","ror":"https://ror.org/00ysfqy60","country_code":"US","type":"education","lineage":["https://openalex.org/I131249849"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Xiao Fu","raw_affiliation_strings":["School of Electrical Engineering & Computer Science, Oregon State University, Corvallis, OR, United States"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Electrical Engineering & Computer Science, Oregon State University, Corvallis, OR, United States","institution_ids":["https://openalex.org/I131249849"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I131249849"],"apc_list":null,"apc_paid":null,"fwci":0.1313,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.4387208,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":97},"biblio":{"volume":"15","issue":null,"first_page":"5370","last_page":"5374"},"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/T11273","display_name":"Advanced Graph Neural Networks","score":0.9765999913215637,"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":0.967199981212616,"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/computer-science","display_name":"Computer science","score":0.7430757284164429},{"id":"https://openalex.org/keywords/scalability","display_name":"Scalability","score":0.5560358762741089},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.5446524024009705},{"id":"https://openalex.org/keywords/cluster-analysis","display_name":"Cluster analysis","score":0.5328790545463562},{"id":"https://openalex.org/keywords/adjacency-list","display_name":"Adjacency list","score":0.5067093968391418},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.4308279752731323},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.345703661441803},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.326002299785614},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.13445362448692322}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7430757284164429},{"id":"https://openalex.org/C48044578","wikidata":"https://www.wikidata.org/wiki/Q727490","display_name":"Scalability","level":2,"score":0.5560358762741089},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.5446524024009705},{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.5328790545463562},{"id":"https://openalex.org/C110484373","wikidata":"https://www.wikidata.org/wiki/Q264398","display_name":"Adjacency list","level":2,"score":0.5067093968391418},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.4308279752731323},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.345703661441803},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.326002299785614},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.13445362448692322},{"id":"https://openalex.org/C77088390","wikidata":"https://www.wikidata.org/wiki/Q8513","display_name":"Database","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icassp39728.2021.9413541","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icassp39728.2021.9413541","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"},{"id":"https://openalex.org/F4320316514","display_name":"Arm","ror":"https://ror.org/04mmhzs81"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":45,"referenced_works":["https://openalex.org/W143174683","https://openalex.org/W1932742904","https://openalex.org/W1976615758","https://openalex.org/W1977713568","https://openalex.org/W2005321445","https://openalex.org/W2029685080","https://openalex.org/W2051638434","https://openalex.org/W2096091969","https://openalex.org/W2106474670","https://openalex.org/W2107107106","https://openalex.org/W2111565546","https://openalex.org/W2114244473","https://openalex.org/W2117740169","https://openalex.org/W2121947440","https://openalex.org/W2125118959","https://openalex.org/W2129062845","https://openalex.org/W2132914434","https://openalex.org/W2136384819","https://openalex.org/W2146008005","https://openalex.org/W2165874743","https://openalex.org/W2295386361","https://openalex.org/W2513096609","https://openalex.org/W2552036965","https://openalex.org/W2614891641","https://openalex.org/W2743418339","https://openalex.org/W2766055969","https://openalex.org/W2791154158","https://openalex.org/W2955787666","https://openalex.org/W2963412369","https://openalex.org/W2970585637","https://openalex.org/W2976088223","https://openalex.org/W3009483570","https://openalex.org/W3100144903","https://openalex.org/W3100202987","https://openalex.org/W3101430755","https://openalex.org/W4292081303","https://openalex.org/W6676274861","https://openalex.org/W6680305118","https://openalex.org/W6684578312","https://openalex.org/W6729739045","https://openalex.org/W6737966065","https://openalex.org/W6741565038","https://openalex.org/W6745465384","https://openalex.org/W6765217860","https://openalex.org/W6767394681"],"related_works":["https://openalex.org/W2611163850","https://openalex.org/W1980812295","https://openalex.org/W2389214306","https://openalex.org/W3015250136","https://openalex.org/W1845849701","https://openalex.org/W2044218713","https://openalex.org/W2625268413","https://openalex.org/W2965083567","https://openalex.org/W4235240664","https://openalex.org/W2900798793"],"abstract_inverted_index":{"Graph":[0],"clustering":[1,19],"is":[2,56,68,148,157,180],"a":[3,17,39,119,129,151],"core":[4],"technique":[5],"for":[6,21,51],"network":[7,61],"analysis":[8,62],"problems,":[9,63],"e.g.,":[10,170],"community":[11],"detection.":[12],"This":[13,54,126],"work":[14,72,127],"puts":[15],"forth":[16],"node":[18,52],"approach":[20],"largely":[22],"incomplete":[23],"adjacency":[24],"graphs.":[25],"Under":[26],"the":[27,35,45,77,80,87,134,164,194],"considered":[28],"scenario,":[29],"instead":[30],"of":[31,42,121,137,196],"having":[32],"access":[33],"to":[34,118,160,163,192],"complete":[36,65],"graph,":[37],"only":[38,82],"small":[40],"amount":[41],"queries":[43],"about":[44],"graph":[46,66,174],"edges":[47],"can":[48],"be":[49],"made":[50],"clustering.":[53],"task":[55],"well-motivated":[57],"in":[58,100,167],"many":[59],"large-scale":[60],"where":[64],"acquisition":[67],"prohibitively":[69],"costly.":[70],"Prior":[71],"tackles":[73],"this":[74],"problem":[75],"under":[76],"setting":[78],"that":[79,131],"nodes":[81,94,138],"admit":[83],"single":[84],"membership":[85,93,136],"and":[86,95,111,123,182],"clusters":[88,97,141],"are":[89,186,190],"disjoint,":[90],"yet":[91],"multiple":[92],"overlapping":[96,140],"often":[98],"arise":[99],"practice.":[101],"Existing":[102],"approaches":[103],"also":[104],"rely":[105],"on":[106],"random":[107,165],"edge":[108,144,153],"query":[109,154],"patterns":[110],"convex":[112],"optimization-based":[113],"formulations,":[114],"which":[115,156],"give":[116],"rise":[117],"number":[120],"implementation":[122],"scalability":[124],"challenges.":[125],"offers":[128],"framework":[130],"provably":[132],"learns":[133],"mixed":[135],"from":[139],"using":[142],"limited":[143],"information.":[145],"Our":[146],"method":[147],"equipped":[149],"with":[150],"systematic":[152],"pattern,":[155],"arguably":[158],"easier":[159],"implement":[161],"relative":[162],"counterparts":[166],"certain":[168],"applications,":[169],"field":[171],"survey":[172],"based":[173],"analysis.":[175],"A":[176],"lightweight":[177],"scalable":[178],"algorithm":[179],"proposed,":[181],"its":[183],"performance":[184],"characterizations":[185],"presented.":[187],"Numerical":[188],"experiments":[189],"used":[191],"showcase":[193],"effectiveness":[195],"our":[197],"method.":[198]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2021,"cited_by_count":1}],"updated_date":"2026-06-26T08:34:08.712188","created_date":"2025-10-10T00:00:00"}