{"id":"https://openalex.org/W4406460926","doi":"https://doi.org/10.1109/bigdata62323.2024.10825598","title":"VQ-VGAE: Vector Quantized Variational Graph Auto-Encoder for Unsupervised Anomaly Detection","display_name":"VQ-VGAE: Vector Quantized Variational Graph Auto-Encoder for Unsupervised Anomaly Detection","publication_year":2024,"publication_date":"2024-12-15","ids":{"openalex":"https://openalex.org/W4406460926","doi":"https://doi.org/10.1109/bigdata62323.2024.10825598"},"language":"en","primary_location":{"id":"doi:10.1109/bigdata62323.2024.10825598","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata62323.2024.10825598","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 IEEE International Conference on Big Data (BigData)","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/A5104543858","display_name":"Tarek Seghair","orcid":null},"institutions":[{"id":"https://openalex.org/I179097149","display_name":"University of Carthage","ror":"https://ror.org/057x6za15","country_code":"TN","type":"education","lineage":["https://openalex.org/I179097149"]}],"countries":["TN"],"is_corresponding":true,"raw_author_name":"Tarek Seghair","raw_affiliation_strings":["University of Carthage,SERCOM Laboratory EPT,Marsa,Tunisia"],"affiliations":[{"raw_affiliation_string":"University of Carthage,SERCOM Laboratory EPT,Marsa,Tunisia","institution_ids":["https://openalex.org/I179097149"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5065168755","display_name":"Olfa Besbes","orcid":"https://orcid.org/0000-0003-3831-9036"},"institutions":[{"id":"https://openalex.org/I8636806","display_name":"University of Sousse","ror":"https://ror.org/00dmpgj58","country_code":"TN","type":"education","lineage":["https://openalex.org/I8636806"]}],"countries":["TN"],"is_corresponding":false,"raw_author_name":"Olfa Besbes","raw_affiliation_strings":["Sousse University,SERCOM Laboratory ISITCOM,Sousse,Tunisia"],"affiliations":[{"raw_affiliation_string":"Sousse University,SERCOM Laboratory ISITCOM,Sousse,Tunisia","institution_ids":["https://openalex.org/I8636806"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5033717859","display_name":"Takoua Abdellatif","orcid":"https://orcid.org/0000-0002-7669-7268"},"institutions":[{"id":"https://openalex.org/I8636806","display_name":"University of Sousse","ror":"https://ror.org/00dmpgj58","country_code":"TN","type":"education","lineage":["https://openalex.org/I8636806"]}],"countries":["TN"],"is_corresponding":false,"raw_author_name":"Takoua Abdellatif","raw_affiliation_strings":["Sousse University,SERCOM Laboratory ENISO,Sousse,Tunisia"],"affiliations":[{"raw_affiliation_string":"Sousse University,SERCOM Laboratory ENISO,Sousse,Tunisia","institution_ids":["https://openalex.org/I8636806"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5115905073","display_name":"Sami Bihiri","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Sami Bihiri","raw_affiliation_strings":["Monastir University,Proxym Group ISIM,Paris,France"],"affiliations":[{"raw_affiliation_string":"Monastir University,Proxym Group ISIM,Paris,France","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5104543858"],"corresponding_institution_ids":["https://openalex.org/I179097149"],"apc_list":null,"apc_paid":null,"fwci":0.3862,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.71151111,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":91,"max":95},"biblio":{"volume":null,"issue":null,"first_page":"2370","last_page":"2375"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11273","display_name":"Advanced Graph Neural Networks","score":0.9991999864578247,"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"}},"topics":[{"id":"https://openalex.org/T11273","display_name":"Advanced Graph Neural Networks","score":0.9991999864578247,"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/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9944999814033508,"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/T10064","display_name":"Complex Network Analysis Techniques","score":0.9926000237464905,"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/anomaly-detection","display_name":"Anomaly detection","score":0.7022828459739685},{"id":"https://openalex.org/keywords/autoencoder","display_name":"Autoencoder","score":0.6520254611968994},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5675380229949951},{"id":"https://openalex.org/keywords/encoder","display_name":"Encoder","score":0.5613841414451599},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5309960842132568},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5031012892723083},{"id":"https://openalex.org/keywords/vector-quantization","display_name":"Vector quantization","score":0.48862749338150024},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.4381505250930786},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.3506109118461609},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.3414383828639984},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.1571328043937683},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.1204698383808136}],"concepts":[{"id":"https://openalex.org/C739882","wikidata":"https://www.wikidata.org/wiki/Q3560506","display_name":"Anomaly detection","level":2,"score":0.7022828459739685},{"id":"https://openalex.org/C101738243","wikidata":"https://www.wikidata.org/wiki/Q786435","display_name":"Autoencoder","level":3,"score":0.6520254611968994},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5675380229949951},{"id":"https://openalex.org/C118505674","wikidata":"https://www.wikidata.org/wiki/Q42586063","display_name":"Encoder","level":2,"score":0.5613841414451599},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5309960842132568},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5031012892723083},{"id":"https://openalex.org/C199833920","wikidata":"https://www.wikidata.org/wiki/Q612536","display_name":"Vector quantization","level":2,"score":0.48862749338150024},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.4381505250930786},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3506109118461609},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.3414383828639984},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.1571328043937683},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.1204698383808136},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/bigdata62323.2024.10825598","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata62323.2024.10825598","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 IEEE International Conference on Big Data (BigData)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":17,"referenced_works":["https://openalex.org/W2154851992","https://openalex.org/W2542911717","https://openalex.org/W2900470550","https://openalex.org/W2944250323","https://openalex.org/W2963799213","https://openalex.org/W3104097132","https://openalex.org/W3204763838","https://openalex.org/W3206604724","https://openalex.org/W4231449374","https://openalex.org/W4294170691","https://openalex.org/W4379927591","https://openalex.org/W4394773022","https://openalex.org/W6699364125","https://openalex.org/W6726873649","https://openalex.org/W6730084236","https://openalex.org/W6756561102","https://openalex.org/W6760045743"],"related_works":["https://openalex.org/W3013693939","https://openalex.org/W2566616303","https://openalex.org/W2159052453","https://openalex.org/W3131327266","https://openalex.org/W2734887215","https://openalex.org/W2803255133","https://openalex.org/W3186512740","https://openalex.org/W4363671829","https://openalex.org/W2780476542","https://openalex.org/W2891286602"],"abstract_inverted_index":{"Detecting":[0],"anomalies":[1],"in":[2,118],"graph-structured":[3],"data":[4],"is":[5],"critical":[6],"for":[7,43],"identifying":[8],"unusual":[9],"patterns":[10],"within":[11,144],"complex":[12,159],"systems,":[13],"with":[14,37],"applications":[15],"spanning":[16],"cybersecurity,":[17],"fraud":[18],"detection,":[19],"and":[20,98,112,120,157],"risk":[21],"assessment.":[22],"In":[23],"this":[24],"work,":[25],"we":[26],"present":[27],"VQ-VGAE,":[28],"a":[29,54,128],"novel":[30],"architecture":[31],"that":[32,104],"combines":[33],"Vector":[34],"Quantization":[35],"(VQ)":[36],"the":[38,72,86,140],"Variational":[39,90],"Graph":[40,76,109],"Auto-Encoder":[41,110],"(VGAE)":[42],"unsupervised":[44],"anomaly":[45,134,150],"detection.":[46],"By":[47],"incorporating":[48],"discrete":[49,129],"latent":[50,130],"variables,":[51],"VQ-VGAE":[52,105],"offers":[53],"new":[55],"way":[56],"to":[57,80,149,155],"model":[58],"intricate":[59],"graph":[60,82,101],"anomalies,":[61],"which":[62],"are":[63],"often":[64],"missed":[65],"by":[66],"traditional":[67],"methods.":[68],"The":[69],"framework":[70],"leverages":[71],"expressive":[73],"power":[74],"of":[75,89,142],"Convolutional":[77],"Networks":[78],"(GCNs)":[79],"learn":[81],"structure":[83],"while":[84],"integrating":[85],"probabilistic":[87],"approach":[88,148],"Auto-Encoders":[91],"(VAEs).":[92],"Extensive":[93],"experiments":[94],"on":[95],"both":[96],"transactional":[97],"business":[99],"process":[100],"datasets":[102],"reveal":[103],"consistently":[106],"surpasses":[107],"conventional":[108],"(GAE)":[111],"VGAE":[113],"models,":[114],"showing":[115],"marked":[116],"improvements":[117],"AUC-ROC":[119],"Average":[121],"Precision":[122],"(AP).":[123],"Beyond":[124],"performance":[125],"gains,":[126],"leveraging":[127],"space":[131],"facilitates":[132],"interpretable":[133,147],"representations,":[135],"offering":[136],"deeper":[137],"insights":[138],"into":[139],"nature":[141],"deviations":[143],"graphs.":[145],"This":[146],"detection":[151],"enhances":[152],"our":[153],"ability":[154],"understand":[156],"manage":[158],"networks":[160],"across":[161],"diverse":[162],"domains.":[163]},"counts_by_year":[{"year":2025,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
