{"id":"https://openalex.org/W2964211020","doi":"https://doi.org/10.1186/s40537-017-0073-8","title":"Outlier edge detection using random graph generation models and applications","display_name":"Outlier edge detection using random graph generation models and applications","publication_year":2017,"publication_date":"2017-04-26","ids":{"openalex":"https://openalex.org/W2964211020","doi":"https://doi.org/10.1186/s40537-017-0073-8","mag":"2964211020"},"language":"en","primary_location":{"id":"doi:10.1186/s40537-017-0073-8","is_oa":true,"landing_page_url":"https://doi.org/10.1186/s40537-017-0073-8","pdf_url":"https://link.springer.com/content/pdf/10.1186/s40537-017-0073-8.pdf","source":{"id":"https://openalex.org/S2737955091","display_name":"Journal Of Big Data","issn_l":"2196-1115","issn":["2196-1115"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319900","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Science+Business Media","Springer Nature"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Journal of Big Data","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://link.springer.com/content/pdf/10.1186/s40537-017-0073-8.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5100671939","display_name":"Honglei Zhang","orcid":"https://orcid.org/0000-0002-8229-852X"},"institutions":[{"id":"https://openalex.org/I4210133110","display_name":"Tampere University","ror":null,"country_code":"FI","type":null,"lineage":["https://openalex.org/I4210133110"]}],"countries":["FI"],"is_corresponding":true,"raw_author_name":"Honglei Zhang","raw_affiliation_strings":["Department of Signal Processing, Tampere University of Technology, Finland, Korkeakoulunkatu 1, FI-33101, Tampere, Finland"],"raw_orcid":"https://orcid.org/0000-0002-8229-852X","affiliations":[{"raw_affiliation_string":"Department of Signal Processing, Tampere University of Technology, Finland, Korkeakoulunkatu 1, FI-33101, Tampere, Finland","institution_ids":["https://openalex.org/I4210133110"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5047979812","display_name":"Serkan K\u0131ranyaz","orcid":"https://orcid.org/0000-0003-1551-3397"},"institutions":[{"id":"https://openalex.org/I60342839","display_name":"Qatar University","ror":"https://ror.org/00yhnba62","country_code":"QA","type":"education","lineage":["https://openalex.org/I60342839"]}],"countries":["QA"],"is_corresponding":false,"raw_author_name":"Serkan Kiranyaz","raw_affiliation_strings":["Electrical Engineering Department, College of Engineering, Qatar University, Qatar, 2713, Al Hala St, Doha, Qatar"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Electrical Engineering Department, College of Engineering, Qatar University, Qatar, 2713, Al Hala St, Doha, Qatar","institution_ids":["https://openalex.org/I60342839"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5007583477","display_name":"Moncef Gabbouj","orcid":"https://orcid.org/0000-0002-9788-2323"},"institutions":[{"id":"https://openalex.org/I4210133110","display_name":"Tampere University","ror":null,"country_code":"FI","type":null,"lineage":["https://openalex.org/I4210133110"]}],"countries":["FI"],"is_corresponding":false,"raw_author_name":"Moncef Gabbouj","raw_affiliation_strings":["Department of Signal Processing, Tampere University of Technology, Finland, Korkeakoulunkatu 1, FI-33101, Tampere, Finland"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Signal Processing, Tampere University of Technology, Finland, Korkeakoulunkatu 1, FI-33101, Tampere, Finland","institution_ids":["https://openalex.org/I4210133110"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5100671939"],"corresponding_institution_ids":["https://openalex.org/I4210133110"],"apc_list":{"value":1060,"currency":"GBP","value_usd":1300},"apc_paid":{"value":1060,"currency":"GBP","value_usd":1300},"fwci":0.8368,"has_fulltext":true,"cited_by_count":10,"citation_normalized_percentile":{"value":0.73270219,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":97},"biblio":{"volume":"4","issue":"1","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":0.9995999932289124,"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":0.9995999932289124,"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/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9984999895095825,"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/T10400","display_name":"Network Security and Intrusion Detection","score":0.9958999752998352,"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/computer-science","display_name":"Computer science","score":0.7327233552932739},{"id":"https://openalex.org/keywords/outlier","display_name":"Outlier","score":0.6717574596405029},{"id":"https://openalex.org/keywords/anomaly-detection","display_name":"Anomaly detection","score":0.6130377054214478},{"id":"https://openalex.org/keywords/cluster-analysis","display_name":"Cluster analysis","score":0.5649321675300598},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.521722674369812},{"id":"https://openalex.org/keywords/preprocessor","display_name":"Preprocessor","score":0.5196393728256226},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.5013482570648193},{"id":"https://openalex.org/keywords/enhanced-data-rates-for-gsm-evolution","display_name":"Enhanced Data Rates for GSM Evolution","score":0.44851943850517273},{"id":"https://openalex.org/keywords/power-graph-analysis","display_name":"Power graph analysis","score":0.43177348375320435},{"id":"https://openalex.org/keywords/clustering-coefficient","display_name":"Clustering coefficient","score":0.42552316188812256},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.37203341722488403},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.33954697847366333},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3358005881309509},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.2700611650943756}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7327233552932739},{"id":"https://openalex.org/C79337645","wikidata":"https://www.wikidata.org/wiki/Q779824","display_name":"Outlier","level":2,"score":0.6717574596405029},{"id":"https://openalex.org/C739882","wikidata":"https://www.wikidata.org/wiki/Q3560506","display_name":"Anomaly detection","level":2,"score":0.6130377054214478},{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.5649321675300598},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.521722674369812},{"id":"https://openalex.org/C34736171","wikidata":"https://www.wikidata.org/wiki/Q918333","display_name":"Preprocessor","level":2,"score":0.5196393728256226},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.5013482570648193},{"id":"https://openalex.org/C162307627","wikidata":"https://www.wikidata.org/wiki/Q204833","display_name":"Enhanced Data Rates for GSM Evolution","level":2,"score":0.44851943850517273},{"id":"https://openalex.org/C106937863","wikidata":"https://www.wikidata.org/wiki/Q7236518","display_name":"Power graph analysis","level":3,"score":0.43177348375320435},{"id":"https://openalex.org/C22047676","wikidata":"https://www.wikidata.org/wiki/Q898680","display_name":"Clustering coefficient","level":3,"score":0.42552316188812256},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.37203341722488403},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.33954697847366333},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3358005881309509},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.2700611650943756}],"mesh":[],"locations_count":4,"locations":[{"id":"doi:10.1186/s40537-017-0073-8","is_oa":true,"landing_page_url":"https://doi.org/10.1186/s40537-017-0073-8","pdf_url":"https://link.springer.com/content/pdf/10.1186/s40537-017-0073-8.pdf","source":{"id":"https://openalex.org/S2737955091","display_name":"Journal Of Big Data","issn_l":"2196-1115","issn":["2196-1115"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319900","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Science+Business Media","Springer Nature"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Journal of Big Data","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:ff3fb6f6f0254504a946bdd201ee37a8","is_oa":false,"landing_page_url":"https://doaj.org/article/ff3fb6f6f0254504a946bdd201ee37a8","pdf_url":null,"source":{"id":"https://openalex.org/S4306401280","display_name":"DOAJ (DOAJ: Directory of Open Access Journals)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Journal of Big Data, Vol 4, Iss 1, Pp 1-25 (2017)","raw_type":"article"},{"id":"pmh:oai:qspace.qu.edu.qa:10576/5545","is_oa":true,"landing_page_url":"http://hdl.handle.net/10576/5545","pdf_url":null,"source":{"id":"https://openalex.org/S4306400014","display_name":"Qatar University QSpace (Qatar University)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I60342839","host_organization_name":"Qatar University","host_organization_lineage":["https://openalex.org/I60342839"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Article"},{"id":"pmh:oai:trepo.tuni.fi:10024/213745","is_oa":true,"landing_page_url":"https://trepo.tuni.fi/handle/10024/213745","pdf_url":null,"source":{"id":"https://openalex.org/S7407055260","display_name":"Trepo - Institutional Repository of Tampere University","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"publishedVersion"}],"best_oa_location":{"id":"doi:10.1186/s40537-017-0073-8","is_oa":true,"landing_page_url":"https://doi.org/10.1186/s40537-017-0073-8","pdf_url":"https://link.springer.com/content/pdf/10.1186/s40537-017-0073-8.pdf","source":{"id":"https://openalex.org/S2737955091","display_name":"Journal Of Big Data","issn_l":"2196-1115","issn":["2196-1115"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319900","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Science+Business Media","Springer Nature"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Journal of Big Data","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W2964211020.pdf","grobid_xml":"https://content.openalex.org/works/W2964211020.grobid-xml"},"referenced_works_count":63,"referenced_works":["https://openalex.org/W19838944","https://openalex.org/W49973572","https://openalex.org/W131619556","https://openalex.org/W143174683","https://openalex.org/W379337736","https://openalex.org/W390146837","https://openalex.org/W934261879","https://openalex.org/W1492581097","https://openalex.org/W1538524459","https://openalex.org/W1566772816","https://openalex.org/W1596502281","https://openalex.org/W1875112053","https://openalex.org/W1973673208","https://openalex.org/W1979104937","https://openalex.org/W1983285030","https://openalex.org/W1984689597","https://openalex.org/W1992419399","https://openalex.org/W2022704179","https://openalex.org/W2023655578","https://openalex.org/W2029073757","https://openalex.org/W2032280284","https://openalex.org/W2041472519","https://openalex.org/W2057685268","https://openalex.org/W2067825627","https://openalex.org/W2080161383","https://openalex.org/W2089458547","https://openalex.org/W2089554624","https://openalex.org/W2095293504","https://openalex.org/W2097922870","https://openalex.org/W2101447063","https://openalex.org/W2103704311","https://openalex.org/W2108614537","https://openalex.org/W2110953678","https://openalex.org/W2112090702","https://openalex.org/W2120043163","https://openalex.org/W2121564430","https://openalex.org/W2121947440","https://openalex.org/W2122515732","https://openalex.org/W2127048411","https://openalex.org/W2131681506","https://openalex.org/W2131717044","https://openalex.org/W2133591726","https://openalex.org/W2134008243","https://openalex.org/W2135223753","https://openalex.org/W2137130182","https://openalex.org/W2141585940","https://openalex.org/W2147620601","https://openalex.org/W2150593711","https://openalex.org/W2151090060","https://openalex.org/W2151936673","https://openalex.org/W2153204928","https://openalex.org/W2157527521","https://openalex.org/W2164998314","https://openalex.org/W2224609888","https://openalex.org/W2532629220","https://openalex.org/W2912372357","https://openalex.org/W2963270580","https://openalex.org/W3099768174","https://openalex.org/W3102641634","https://openalex.org/W3123041312","https://openalex.org/W3142588439","https://openalex.org/W3195983053","https://openalex.org/W4238452917"],"related_works":["https://openalex.org/W2901947522","https://openalex.org/W1595351371","https://openalex.org/W3116762327","https://openalex.org/W2230433129","https://openalex.org/W2606848831","https://openalex.org/W2390515779","https://openalex.org/W2029968811","https://openalex.org/W2998615029","https://openalex.org/W3183283580","https://openalex.org/W4250175685"],"abstract_inverted_index":{"Outliers":[0],"are":[1,4,26,82,88,175,190],"samples":[2],"that":[3,25,87,96,130,205,215,263],"generated":[5],"by":[6,28,34,84,118,164,268],"different":[7,203],"mechanisms":[8],"from":[9,207],"other":[10],"normal":[11,35],"data":[12,45,234],"samples.":[13],"Graphs,":[14],"in":[15,53,91,171,193,259],"particular":[16],"social":[17,92],"network":[18],"graphs,":[19],"may":[20],"contain":[21],"nodes":[22,39],"and":[23,40,47,72,181,229],"edges":[24,41,71,121,170,258],"made":[27],"scammers,":[29],"malicious":[30],"programs":[31],"or":[32],"mistakenly":[33],"users.":[36],"Detecting":[37],"outlier":[38,61,75,120,137,197,225,246],"is":[42],"important":[43],"for":[44,106],"mining":[46],"graph":[48,98,125,160,220,260],"analytics.":[49],"However,":[50],"previous":[51],"research":[52],"the":[54,68,97,108,111,115,131,141,145,158,166,169,187,194,208,217,240,244,253,257],"field":[55],"has":[56,264],"merely":[57],"focused":[58],"on":[59,144],"detecting":[60],"nodes.":[62],"In":[63,139],"this":[64],"article,":[65],"we":[66,174],"study":[67],"properties":[69,182],"of":[70,110,157,168,183,196,219,243,255],"propose":[73],"effective":[74],"edge":[76,102,198,247],"detection":[77,227,248],"algorithm.":[78,236],"The":[79],"proposed":[80,116,132,188,209,245],"algorithms":[81,117,133,189],"inspired":[83],"community":[85],"structures":[86],"very":[89],"common":[90],"networks.":[93],"We":[94,113,200],"found":[95],"structure":[99,180],"around":[100],"an":[101,224],"holds":[103],"critical":[104],"information":[105],"determining":[107],"authenticity":[109,167],"edge.":[112],"evaluated":[114],"injecting":[119],"into":[122],"some":[123],"real-world":[124],"data.":[126,161],"Experiment":[127],"results":[128],"show":[129,239],"can":[134],"effectively":[135],"detect":[136],"edges.":[138],"particular,":[140],"algorithm":[142],"based":[143],"Preferential":[146],"Attachment":[147],"Random":[148],"Graph":[149],"Generation":[150],"model":[151],"consistently":[152],"gives":[153],"good":[154],"performance":[155,218],"regardless":[156],"test":[159],"More":[162],"important,":[163],"analyzing":[165,256],"a":[172,184,212,231],"graph,":[173],"able":[176],"to":[177],"reveal":[178],"underlying":[179],"graph.":[185],"Thus,":[186],"not":[191],"limited":[192],"area":[195],"detection.":[199],"demonstrate":[201],"three":[202],"applications":[204,238],"benefit":[206],"algorithms:":[210],"(1)":[211],"preprocessing":[213],"tool":[214],"improves":[216],"clustering":[221,235],"algorithms;":[222],"(2)":[223],"node":[226],"algorithm;":[228],"(3)":[230],"novel":[232],"noisy":[233],"These":[237],"great":[241],"potential":[242],"techniques.":[249],"They":[250],"also":[251],"address":[252],"importance":[254],"mining\u2014a":[261],"topic":[262],"been":[265],"mostly":[266],"neglected":[267],"researchers.":[269]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2023,"cited_by_count":3},{"year":2022,"cited_by_count":1},{"year":2020,"cited_by_count":4},{"year":2018,"cited_by_count":1}],"updated_date":"2026-07-02T09:51:11.867554","created_date":"2025-10-10T00:00:00"}