{"id":"https://openalex.org/W1985119862","doi":"https://doi.org/10.1109/icassp.2014.6854236","title":"Spectral subgraph detection with corrupt observations","display_name":"Spectral subgraph detection with corrupt observations","publication_year":2014,"publication_date":"2014-05-01","ids":{"openalex":"https://openalex.org/W1985119862","doi":"https://doi.org/10.1109/icassp.2014.6854236","mag":"1985119862"},"language":"en","primary_location":{"id":"doi:10.1109/icassp.2014.6854236","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icassp.2014.6854236","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2014 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/A5102934256","display_name":"Benjamin A. Miller","orcid":"https://orcid.org/0000-0002-1649-1401"},"institutions":[{"id":"https://openalex.org/I4210122954","display_name":"MIT Lincoln Laboratory","ror":"https://ror.org/022z6jk58","country_code":"US","type":"facility","lineage":["https://openalex.org/I4210122954","https://openalex.org/I63966007"]},{"id":"https://openalex.org/I63966007","display_name":"Massachusetts Institute of Technology","ror":"https://ror.org/042nb2s44","country_code":"US","type":"education","lineage":["https://openalex.org/I63966007"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Benjamin A. Miller","raw_affiliation_strings":["Lincoln Laboratory, Massachusetts Institute of Technology, Lexington, MA","[Lincoln Lab., Massachusetts Inst. of Technol., Lexington, MA, USA]"],"affiliations":[{"raw_affiliation_string":"Lincoln Laboratory, Massachusetts Institute of Technology, Lexington, MA","institution_ids":["https://openalex.org/I4210122954","https://openalex.org/I63966007"]},{"raw_affiliation_string":"[Lincoln Lab., Massachusetts Inst. of Technol., Lexington, MA, USA]","institution_ids":["https://openalex.org/I63966007"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5085288376","display_name":"Nicholas Arcolano","orcid":null},"institutions":[{"id":"https://openalex.org/I4210122954","display_name":"MIT Lincoln Laboratory","ror":"https://ror.org/022z6jk58","country_code":"US","type":"facility","lineage":["https://openalex.org/I4210122954","https://openalex.org/I63966007"]},{"id":"https://openalex.org/I63966007","display_name":"Massachusetts Institute of Technology","ror":"https://ror.org/042nb2s44","country_code":"US","type":"education","lineage":["https://openalex.org/I63966007"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Nicholas Arcolano","raw_affiliation_strings":["Lincoln Laboratory, Massachusetts Institute of Technology, Lexington, MA","[Lincoln Lab., Massachusetts Inst. of Technol., Lexington, MA, USA]"],"affiliations":[{"raw_affiliation_string":"Lincoln Laboratory, Massachusetts Institute of Technology, Lexington, MA","institution_ids":["https://openalex.org/I4210122954","https://openalex.org/I63966007"]},{"raw_affiliation_string":"[Lincoln Lab., Massachusetts Inst. of Technol., Lexington, MA, USA]","institution_ids":["https://openalex.org/I63966007"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5102934256"],"corresponding_institution_ids":["https://openalex.org/I4210122954","https://openalex.org/I63966007"],"apc_list":null,"apc_paid":null,"fwci":0.7099,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":{"value":0.69299131,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":96},"biblio":{"volume":null,"issue":null,"first_page":"3425","last_page":"3429"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10064","display_name":"Complex Network Analysis Techniques","score":0.9994999766349792,"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":0.9994999766349792,"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":0.9980000257492065,"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":0.9940999746322632,"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":0.7173977494239807},{"id":"https://openalex.org/keywords/imperfect","display_name":"Imperfect","score":0.5553052425384521},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.5090550780296326},{"id":"https://openalex.org/keywords/observable","display_name":"Observable","score":0.49413588643074036},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.46983596682548523},{"id":"https://openalex.org/keywords/detection-theory","display_name":"Detection theory","score":0.46526339650154114},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.450860857963562},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.44855010509490967},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.44211167097091675},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.3994418978691101},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3714197874069214},{"id":"https://openalex.org/keywords/detector","display_name":"Detector","score":0.0954587459564209}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7173977494239807},{"id":"https://openalex.org/C2780310539","wikidata":"https://www.wikidata.org/wiki/Q12547192","display_name":"Imperfect","level":2,"score":0.5553052425384521},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.5090550780296326},{"id":"https://openalex.org/C32848918","wikidata":"https://www.wikidata.org/wiki/Q845789","display_name":"Observable","level":2,"score":0.49413588643074036},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.46983596682548523},{"id":"https://openalex.org/C137270730","wikidata":"https://www.wikidata.org/wiki/Q120811","display_name":"Detection theory","level":3,"score":0.46526339650154114},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.450860857963562},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.44855010509490967},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.44211167097091675},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.3994418978691101},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3714197874069214},{"id":"https://openalex.org/C94915269","wikidata":"https://www.wikidata.org/wiki/Q1834857","display_name":"Detector","level":2,"score":0.0954587459564209},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"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/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"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/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icassp.2014.6854236","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icassp.2014.6854236","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Peace, Justice and strong institutions","score":0.8100000023841858,"id":"https://metadata.un.org/sdg/16"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":12,"referenced_works":["https://openalex.org/W1482680420","https://openalex.org/W1966105052","https://openalex.org/W1973836644","https://openalex.org/W2015953751","https://openalex.org/W2033790336","https://openalex.org/W2040840530","https://openalex.org/W2050537424","https://openalex.org/W2071321532","https://openalex.org/W2093486293","https://openalex.org/W2112643910","https://openalex.org/W2146008005","https://openalex.org/W4296763559"],"related_works":["https://openalex.org/W1994680671","https://openalex.org/W2374250903","https://openalex.org/W2000283393","https://openalex.org/W2002320543","https://openalex.org/W2150232912","https://openalex.org/W2054940838","https://openalex.org/W2061947244","https://openalex.org/W4321855183","https://openalex.org/W15793755","https://openalex.org/W3106170641"],"abstract_inverted_index":{"Recent":[0],"work":[1],"on":[2,9,77],"signal":[3,14,96],"detection":[4,11,59,78],"in":[5,19,68,95,107],"graph-based":[6],"data":[7,131],"focuses":[8],"classical":[10],"when":[12],"the":[13,20,30,58,62,69,75,93],"and":[15,25,83,97,102,136],"noise":[16,98],"are":[17],"both":[18],"form":[21],"of":[22,32,49,55,64,80],"discrete":[23],"entities":[24],"their":[26],"relationships.":[27],"In":[28,89],"practice,":[29],"relationships":[31],"interest":[33],"may":[34,40],"not":[35],"be":[36,41],"directly":[37],"observable,":[38],"or":[39],"observed":[42],"through":[43],"a":[44,133,137,145],"noisy":[45],"mechanism.":[46],"The":[47],"effects":[48,63],"imperfect":[50],"observations":[51],"add":[52],"another":[53],"layer":[54],"difficulty":[56],"to":[57,122,127,143],"problem,":[60],"beyond":[61],"typical":[65],"random":[66],"fluctuations":[67],"background":[70],"graph.":[71],"This":[72],"paper":[73],"analyzes":[74],"impact":[76],"performance":[79,125],"several":[81],"error":[82],"corruption":[84],"mechanisms":[85],"for":[86],"graph":[87],"data.":[88],"relatively":[90],"simple":[91],"scenarios,":[92],"change":[94],"power":[99],"is":[100,104,112,120,141],"analyzed,":[101],"this":[103],"demonstrated":[105],"empirically":[106],"more":[108],"complicated":[109],"models.":[110],"It":[111],"shown":[113,142],"that,":[114],"with":[115,129],"enough":[116],"side":[117],"information,":[118],"it":[119],"possible":[121],"fully":[123],"recover":[124],"equivalent":[126],"working":[128],"uncorrupted":[130],"using":[132],"Bayesian":[134],"approach,":[135],"simpler":[138],"cost-optimization":[139],"approach":[140],"provide":[144],"substantial":[146],"benefit":[147],"as":[148],"well.":[149]},"counts_by_year":[{"year":2015,"cited_by_count":1},{"year":2014,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}