{"id":"https://openalex.org/W2519716980","doi":"https://doi.org/10.1109/isit.2014.6875186","title":"Information recovery from pairwise measurements","display_name":"Information recovery from pairwise measurements","publication_year":2014,"publication_date":"2014-06-01","ids":{"openalex":"https://openalex.org/W2519716980","doi":"https://doi.org/10.1109/isit.2014.6875186","mag":"2519716980"},"language":"en","primary_location":{"id":"doi:10.1109/isit.2014.6875186","is_oa":false,"landing_page_url":"https://doi.org/10.1109/isit.2014.6875186","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2014 IEEE International Symposium on Information Theory","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/A5100416078","display_name":"Yuxin Chen","orcid":"https://orcid.org/0000-0001-9256-5815"},"institutions":[{"id":"https://openalex.org/I97018004","display_name":"Stanford University","ror":"https://ror.org/00f54p054","country_code":"US","type":"education","lineage":["https://openalex.org/I97018004"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Yuxin Chen","raw_affiliation_strings":["Electrical Engineering, Stanford University"],"affiliations":[{"raw_affiliation_string":"Electrical Engineering, Stanford University","institution_ids":["https://openalex.org/I97018004"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5074116801","display_name":"Andrea Goldsmith","orcid":"https://orcid.org/0000-0001-5686-800X"},"institutions":[{"id":"https://openalex.org/I97018004","display_name":"Stanford University","ror":"https://ror.org/00f54p054","country_code":"US","type":"education","lineage":["https://openalex.org/I97018004"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Andrea J. Goldsmith","raw_affiliation_strings":["Electrical Engineering, Stanford University"],"affiliations":[{"raw_affiliation_string":"Electrical Engineering, Stanford University","institution_ids":["https://openalex.org/I97018004"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5100416078"],"corresponding_institution_ids":["https://openalex.org/I97018004"],"apc_list":null,"apc_paid":null,"fwci":3.7845,"has_fulltext":false,"cited_by_count":18,"citation_normalized_percentile":{"value":0.93154755,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":89,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"2012","last_page":"2016"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10500","display_name":"Sparse and Compressive Sensing Techniques","score":0.9986000061035156,"subfield":{"id":"https://openalex.org/subfields/2206","display_name":"Computational Mechanics"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10500","display_name":"Sparse and Compressive Sensing Techniques","score":0.9986000061035156,"subfield":{"id":"https://openalex.org/subfields/2206","display_name":"Computational Mechanics"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T12072","display_name":"Machine Learning and Algorithms","score":0.9937000274658203,"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/T12879","display_name":"Distributed Sensor Networks and Detection Algorithms","score":0.989799976348877,"subfield":{"id":"https://openalex.org/subfields/1705","display_name":"Computer Networks and Communications"},"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/unobservable","display_name":"Unobservable","score":0.7243872880935669},{"id":"https://openalex.org/keywords/pairwise-comparison","display_name":"Pairwise comparison","score":0.708587646484375},{"id":"https://openalex.org/keywords/minimax","display_name":"Minimax","score":0.6410608291625977},{"id":"https://openalex.org/keywords/combinatorics","display_name":"Combinatorics","score":0.5831421613693237},{"id":"https://openalex.org/keywords/random-graph","display_name":"Random graph","score":0.5558844804763794},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.5164054036140442},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.46637633442878723},{"id":"https://openalex.org/keywords/outlier","display_name":"Outlier","score":0.46084073185920715},{"id":"https://openalex.org/keywords/discrete-mathematics","display_name":"Discrete mathematics","score":0.4419366717338562},{"id":"https://openalex.org/keywords/stochastic-block-model","display_name":"Stochastic block model","score":0.4315554201602936},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.37891125679016113},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.3725264072418213},{"id":"https://openalex.org/keywords/mathematical-optimization","display_name":"Mathematical optimization","score":0.28667232394218445},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.20629924535751343},{"id":"https://openalex.org/keywords/cluster-analysis","display_name":"Cluster analysis","score":0.15496277809143066}],"concepts":[{"id":"https://openalex.org/C2780695315","wikidata":"https://www.wikidata.org/wiki/Q3799040","display_name":"Unobservable","level":2,"score":0.7243872880935669},{"id":"https://openalex.org/C184898388","wikidata":"https://www.wikidata.org/wiki/Q1435712","display_name":"Pairwise comparison","level":2,"score":0.708587646484375},{"id":"https://openalex.org/C149728462","wikidata":"https://www.wikidata.org/wiki/Q751319","display_name":"Minimax","level":2,"score":0.6410608291625977},{"id":"https://openalex.org/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","level":1,"score":0.5831421613693237},{"id":"https://openalex.org/C47458327","wikidata":"https://www.wikidata.org/wiki/Q910404","display_name":"Random graph","level":3,"score":0.5558844804763794},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.5164054036140442},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.46637633442878723},{"id":"https://openalex.org/C79337645","wikidata":"https://www.wikidata.org/wiki/Q779824","display_name":"Outlier","level":2,"score":0.46084073185920715},{"id":"https://openalex.org/C118615104","wikidata":"https://www.wikidata.org/wiki/Q121416","display_name":"Discrete mathematics","level":1,"score":0.4419366717338562},{"id":"https://openalex.org/C2779982251","wikidata":"https://www.wikidata.org/wiki/Q25053762","display_name":"Stochastic block model","level":3,"score":0.4315554201602936},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.37891125679016113},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.3725264072418213},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.28667232394218445},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.20629924535751343},{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.15496277809143066},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/isit.2014.6875186","is_oa":false,"landing_page_url":"https://doi.org/10.1109/isit.2014.6875186","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2014 IEEE International Symposium on Information Theory","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/F4320338279","display_name":"Air Force Office of Scientific Research","ror":"https://ror.org/011e9bt93"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":21,"referenced_works":["https://openalex.org/W1546318054","https://openalex.org/W1866515447","https://openalex.org/W1993336109","https://openalex.org/W2004238554","https://openalex.org/W2057198330","https://openalex.org/W2066003352","https://openalex.org/W2144792840","https://openalex.org/W2145799156","https://openalex.org/W2145962650","https://openalex.org/W2147554450","https://openalex.org/W2151975094","https://openalex.org/W2158121106","https://openalex.org/W2963537367","https://openalex.org/W2963655172","https://openalex.org/W3100598149","https://openalex.org/W4230202445","https://openalex.org/W4230371388","https://openalex.org/W6638936255","https://openalex.org/W6648821057","https://openalex.org/W6681562239","https://openalex.org/W6682136732"],"related_works":["https://openalex.org/W2843165098","https://openalex.org/W4385741157","https://openalex.org/W2964030270","https://openalex.org/W2286068019","https://openalex.org/W2796901439","https://openalex.org/W2977688136","https://openalex.org/W4301017148","https://openalex.org/W4283448257","https://openalex.org/W4316829854","https://openalex.org/W2754525922"],"abstract_inverted_index":{"A":[0],"variety":[1],"of":[2,22,40,57,65,82,91,118,135,165,171],"information":[3,72,125],"processing":[4],"tasks":[5],"in":[6,227],"practice":[7],"involve":[8],"recovering":[9],"n":[10,214],"objects":[11],"from":[12],"single-shot":[13],"graph-based":[14],"measurements,":[15],"particularly":[16],"those":[17],"taken":[18],"over":[19,37,61],"the":[20,30,109,115,154,162,166,180,195,207,228],"edges":[21],"some":[23],"measurement":[24,167],"graph":[25,142,168],"G.":[26,62],"This":[27,129,175],"paper":[28],"concerns":[29],"situation":[31],"where":[32,45],"each":[33],"object":[34],"takes":[35],"value":[36],"a":[38,76,88,102,131,185,191,199],"group":[39,181],"M":[41,183,211,230],"different":[42],"values,":[43],"and":[44],"one":[46],"is":[47,127,203,222],"interested":[48],"to":[49,205],"recover":[50],"all":[51],"these":[52],"values":[53],"based":[54],"on":[55,161],"observations":[56],"certain":[58],"pairwise":[59,136],"relations":[60],"The":[63],"imperfection":[64],"measurements":[66,83],"presents":[67],"two":[68],"major":[69],"challenges":[70],"for":[71,209],"recovery:":[73],"1)":[74],"inaccuracy:":[75],"(dominant)":[77],"portion":[78],"1":[79],"-":[80],"p":[81,121],"are":[84,93],"corrupted;":[85],"2)":[86],"incompleteness:":[87],"significant":[89],"fraction":[90],"pairs":[92],"unobservable,":[94],"i.e.":[95],"G":[96,169],"can":[97],"be":[98],"highly":[99],"sparse.":[100],"Under":[101,194],"natural":[103],"random":[104,141,146,148,197],"outlier":[105],"model,":[106],"we":[107],"characterize":[108],"minimax":[110,155],"recovery":[111,126,156,221],"rate,":[112],"that":[113],"is,":[114],"critical":[116],"threshold":[117],"non-corruption":[119],"rate":[120,157,188],"below":[122],"which":[123],"exact":[124],"infeasible.":[128],"accommodates":[130],"very":[132],"general":[133],"class":[134],"relations.":[137],"For":[138],"various":[139],"homogeneous":[140],"models":[143],"(e.g.":[144],"Erd\u00f6s-R\u00e9nyi":[145,196],"graphs,":[147,150],"geometric":[149],"small":[151,229],"world":[152],"graphs),":[153],"depends":[158],"almost":[159],"exclusively":[160],"edge":[163],"sparsity":[164],"irrespective":[170],"other":[172],"graphical":[173],"metrics.":[174],"fundamental":[176],"limit":[177,208],"decays":[178],"with":[179],"size":[182],"at":[184],"square":[186],"root":[187],"before":[189],"entering":[190],"connectivity-limited":[192],"regime.":[193,231],"graph,":[198],"tractable":[200],"combinatorial":[201],"algorithm":[202],"proposed":[204],"approach":[206],"large":[210],"(M":[212],"=":[213],"<sup":[215],"xmlns:mml=\"http://www.w3.org/1998/Math/MathML\"":[216],"xmlns:xlink=\"http://www.w3.org/1999/xlink\">\u03a9(1)</sup>":[217],"),":[218],"while":[219],"order-optimal":[220],"enabled":[223],"by":[224],"semidefinite":[225],"programs":[226]},"counts_by_year":[{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":1},{"year":2020,"cited_by_count":1},{"year":2019,"cited_by_count":2},{"year":2018,"cited_by_count":1},{"year":2016,"cited_by_count":4},{"year":2015,"cited_by_count":5},{"year":2014,"cited_by_count":3}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}