{"id":"https://openalex.org/W2389313459","doi":"https://doi.org/10.1109/cdc.2016.7798969","title":"Characterization of L<sup>1</sup>-norm statistic for anomaly detection in Erd\u0151s R\u00e9nyi graphs","display_name":"Characterization of L<sup>1</sup>-norm statistic for anomaly detection in Erd\u0151s R\u00e9nyi graphs","publication_year":2016,"publication_date":"2016-12-01","ids":{"openalex":"https://openalex.org/W2389313459","doi":"https://doi.org/10.1109/cdc.2016.7798969","mag":"2389313459"},"language":"en","primary_location":{"id":"doi:10.1109/cdc.2016.7798969","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cdc.2016.7798969","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 IEEE 55th Conference on Decision and Control (CDC)","raw_type":"proceedings-article"},"type":"preprint","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/A5056965886","display_name":"Arun Kadavankandy","orcid":"https://orcid.org/0000-0001-6466-5835"},"institutions":[{"id":"https://openalex.org/I1326498283","display_name":"Institut national de recherche en sciences et technologies du num\u00e9rique","ror":"https://ror.org/02kvxyf05","country_code":"FR","type":"government","lineage":["https://openalex.org/I1326498283"]}],"countries":["FR"],"is_corresponding":true,"raw_author_name":"Arun Kadavankandy","raw_affiliation_strings":["INRIA, Sophia Antipolis","University of Nice"],"affiliations":[{"raw_affiliation_string":"INRIA, Sophia Antipolis","institution_ids":["https://openalex.org/I1326498283"]},{"raw_affiliation_string":"University of Nice","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5064586027","display_name":"Laura Cottatellucci","orcid":"https://orcid.org/0000-0002-6641-8579"},"institutions":[{"id":"https://openalex.org/I1902872","display_name":"EURECOM","ror":"https://ror.org/00sse7z02","country_code":"FR","type":"education","lineage":["https://openalex.org/I1902872","https://openalex.org/I205703379"]}],"countries":["FR"],"is_corresponding":false,"raw_author_name":"Laura Cottatellucci","raw_affiliation_strings":["Eurecom, France"],"affiliations":[{"raw_affiliation_string":"Eurecom, France","institution_ids":["https://openalex.org/I1902872"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5053557735","display_name":"Konstantin Avrachenkov","orcid":"https://orcid.org/0000-0002-8124-8272"},"institutions":[{"id":"https://openalex.org/I1326498283","display_name":"Institut national de recherche en sciences et technologies du num\u00e9rique","ror":"https://ror.org/02kvxyf05","country_code":"FR","type":"government","lineage":["https://openalex.org/I1326498283"]}],"countries":["FR"],"is_corresponding":false,"raw_author_name":"Konstantin Avrachenkov","raw_affiliation_strings":["INRIA, Sophia Antipolis"],"affiliations":[{"raw_affiliation_string":"INRIA, Sophia Antipolis","institution_ids":["https://openalex.org/I1326498283"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5056965886"],"corresponding_institution_ids":["https://openalex.org/I1326498283"],"apc_list":null,"apc_paid":null,"fwci":0.39981072,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.68576695,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":94},"biblio":{"volume":"55","issue":null,"first_page":"4600","last_page":"4605"},"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/T11716","display_name":"Random Matrices and Applications","score":0.9950000047683716,"subfield":{"id":"https://openalex.org/subfields/2613","display_name":"Statistics and Probability"},"field":{"id":"https://openalex.org/fields/26","display_name":"Mathematics"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T12592","display_name":"Opinion Dynamics and Social Influence","score":0.9932000041007996,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/induced-subgraph-isomorphism-problem","display_name":"Induced subgraph isomorphism problem","score":0.741694450378418},{"id":"https://openalex.org/keywords/combinatorics","display_name":"Combinatorics","score":0.5752797722816467},{"id":"https://openalex.org/keywords/eigenvalues-and-eigenvectors","display_name":"Eigenvalues and eigenvectors","score":0.571919322013855},{"id":"https://openalex.org/keywords/random-graph","display_name":"Random graph","score":0.569689929485321},{"id":"https://openalex.org/keywords/statistic","display_name":"Statistic","score":0.5611600875854492},{"id":"https://openalex.org/keywords/test-statistic","display_name":"Test statistic","score":0.5240723490715027},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.48943009972572327},{"id":"https://openalex.org/keywords/subgraph-isomorphism-problem","display_name":"Subgraph isomorphism problem","score":0.47628894448280334},{"id":"https://openalex.org/keywords/graph-factorization","display_name":"Graph factorization","score":0.4374489188194275},{"id":"https://openalex.org/keywords/anomaly-detection","display_name":"Anomaly detection","score":0.42436912655830383},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.40471696853637695},{"id":"https://openalex.org/keywords/discrete-mathematics","display_name":"Discrete mathematics","score":0.3980862498283386},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.3974539041519165},{"id":"https://openalex.org/keywords/statistical-hypothesis-testing","display_name":"Statistical hypothesis testing","score":0.3152013421058655},{"id":"https://openalex.org/keywords/line-graph","display_name":"Line graph","score":0.22511252760887146},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.18503224849700928},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.1499975621700287},{"id":"https://openalex.org/keywords/graph-power","display_name":"Graph power","score":0.13944095373153687},{"id":"https://openalex.org/keywords/voltage-graph","display_name":"Voltage graph","score":0.08027276396751404}],"concepts":[{"id":"https://openalex.org/C191241153","wikidata":"https://www.wikidata.org/wiki/Q6027240","display_name":"Induced subgraph isomorphism problem","level":5,"score":0.741694450378418},{"id":"https://openalex.org/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","level":1,"score":0.5752797722816467},{"id":"https://openalex.org/C158693339","wikidata":"https://www.wikidata.org/wiki/Q190524","display_name":"Eigenvalues and eigenvectors","level":2,"score":0.571919322013855},{"id":"https://openalex.org/C47458327","wikidata":"https://www.wikidata.org/wiki/Q910404","display_name":"Random graph","level":3,"score":0.569689929485321},{"id":"https://openalex.org/C89128539","wikidata":"https://www.wikidata.org/wiki/Q1949963","display_name":"Statistic","level":2,"score":0.5611600875854492},{"id":"https://openalex.org/C169857963","wikidata":"https://www.wikidata.org/wiki/Q1461038","display_name":"Test statistic","level":3,"score":0.5240723490715027},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.48943009972572327},{"id":"https://openalex.org/C131992880","wikidata":"https://www.wikidata.org/wiki/Q2528185","display_name":"Subgraph isomorphism problem","level":3,"score":0.47628894448280334},{"id":"https://openalex.org/C128115575","wikidata":"https://www.wikidata.org/wiki/Q5597083","display_name":"Graph factorization","level":5,"score":0.4374489188194275},{"id":"https://openalex.org/C739882","wikidata":"https://www.wikidata.org/wiki/Q3560506","display_name":"Anomaly detection","level":2,"score":0.42436912655830383},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.40471696853637695},{"id":"https://openalex.org/C118615104","wikidata":"https://www.wikidata.org/wiki/Q121416","display_name":"Discrete mathematics","level":1,"score":0.3980862498283386},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.3974539041519165},{"id":"https://openalex.org/C87007009","wikidata":"https://www.wikidata.org/wiki/Q210832","display_name":"Statistical hypothesis testing","level":2,"score":0.3152013421058655},{"id":"https://openalex.org/C203776342","wikidata":"https://www.wikidata.org/wiki/Q1378376","display_name":"Line graph","level":3,"score":0.22511252760887146},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.18503224849700928},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.1499975621700287},{"id":"https://openalex.org/C149530733","wikidata":"https://www.wikidata.org/wiki/Q5597091","display_name":"Graph power","level":4,"score":0.13944095373153687},{"id":"https://openalex.org/C22149727","wikidata":"https://www.wikidata.org/wiki/Q7940747","display_name":"Voltage graph","level":4,"score":0.08027276396751404},{"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/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/cdc.2016.7798969","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cdc.2016.7798969","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 IEEE 55th Conference on Decision and Control (CDC)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Reduced inequalities","score":0.49000000953674316,"id":"https://metadata.un.org/sdg/10"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":37,"referenced_works":["https://openalex.org/W348531675","https://openalex.org/W653925602","https://openalex.org/W1480650841","https://openalex.org/W1531677314","https://openalex.org/W1568365905","https://openalex.org/W1794022037","https://openalex.org/W1800780444","https://openalex.org/W1964944793","https://openalex.org/W1967184357","https://openalex.org/W1987289599","https://openalex.org/W1987349051","https://openalex.org/W1996399057","https://openalex.org/W2004531067","https://openalex.org/W2056099894","https://openalex.org/W2068466582","https://openalex.org/W2089554624","https://openalex.org/W2093486293","https://openalex.org/W2094438648","https://openalex.org/W2095293504","https://openalex.org/W2099309550","https://openalex.org/W2109848965","https://openalex.org/W2132914434","https://openalex.org/W2151936673","https://openalex.org/W2152420876","https://openalex.org/W2162384258","https://openalex.org/W2166602562","https://openalex.org/W2187786257","https://openalex.org/W2248940257","https://openalex.org/W2610857016","https://openalex.org/W2950018692","https://openalex.org/W3102534500","https://openalex.org/W3104043637","https://openalex.org/W3104227803","https://openalex.org/W3139080059","https://openalex.org/W4300223101","https://openalex.org/W6628714853","https://openalex.org/W6638149108"],"related_works":["https://openalex.org/W2393701947","https://openalex.org/W1512756268","https://openalex.org/W2532922352","https://openalex.org/W2886672068","https://openalex.org/W167435155","https://openalex.org/W2604114816","https://openalex.org/W2953496651","https://openalex.org/W1887488684","https://openalex.org/W1739013558","https://openalex.org/W3208942821"],"abstract_inverted_index":{"We":[0,58,91],"describe":[1],"a":[2,17,32,42,46,51,55,132],"test":[3,79],"statistic":[4,80],"based":[5],"on":[6,84,146],"the":[7,14,22,62,65,75,78,85,94,109,113,136,147,150,153,161],"L":[8],"<sup":[9],"xmlns:mml=\"http://www.w3.org/1998/Math/MathML\"":[10],"xmlns:xlink=\"http://www.w3.org/1999/xlink\">1</sup>":[11],"-norm":[12],"of":[13,16,24,61,64,68,70,77,102,108,149,152],"eigenvectors":[15,69],"modularity":[18],"matrix":[19],"to":[20,73,124,138,159],"detect":[21,160],"presence":[23],"an":[25,129],"embedded":[26,38],"Erd\u00f6s":[27],"R\u00e9nyi":[28],"(ER)":[29],"subgraph":[30,39,86,103,162],"inside":[31],"larger":[33],"ER":[34],"random":[35,71],"graph.":[36],"An":[37],"may":[40,155],"model":[41],"hidden":[43],"community":[44],"in":[45,131],"large":[47],"network":[48,53],"such":[49],"as":[50],"social":[52],"or":[54],"computer":[56],"network.":[57],"make":[59],"use":[60],"properties":[63],"asymptotic":[66],"distribution":[67,76,148],"graphs":[72],"derive":[74,145],"under":[81],"certain":[82],"conditions":[83],"size":[87],"and":[88,105,112],"edge":[89,106],"probabilities.":[90],"show":[92],"that":[93],"distributions":[95],"differ":[96],"sufficiently":[97],"for":[98],"well":[99],"defined":[100],"ranges":[101],"sizes":[104],"probabilities":[107],"background":[110],"graph":[111,134],"subgraph.":[114],"This":[115],"method":[116],"can":[117],"have":[118],"applications":[119],"where":[120],"it":[121],"is":[122,128],"sufficient":[123],"know":[125],"whether":[126],"there":[127],"anomaly":[130],"given":[133],"without":[135],"need":[137],"infer":[139],"its":[140],"location.":[141],"The":[142],"results":[143],"we":[144],"components":[151],"eigenvector":[154],"also":[156],"be":[157],"useful":[158],"nodes.":[163]},"counts_by_year":[{"year":2018,"cited_by_count":1},{"year":2017,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
