{"id":"https://openalex.org/W4416250124","doi":"https://doi.org/10.1109/ijcnn64981.2025.11228687","title":"Unsupervised Dual-discriminative Graph Neural Network for Anomaly Detection","display_name":"Unsupervised Dual-discriminative Graph Neural Network for Anomaly Detection","publication_year":2025,"publication_date":"2025-06-30","ids":{"openalex":"https://openalex.org/W4416250124","doi":"https://doi.org/10.1109/ijcnn64981.2025.11228687"},"language":null,"primary_location":{"id":"doi:10.1109/ijcnn64981.2025.11228687","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn64981.2025.11228687","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 International Joint Conference on Neural Networks (IJCNN)","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/A5100747159","display_name":"Yiran Liu","orcid":"https://orcid.org/0000-0002-2760-2461"},"institutions":[{"id":"https://openalex.org/I96908189","display_name":"Xinjiang University","ror":"https://ror.org/059gw8r13","country_code":"CN","type":"education","lineage":["https://openalex.org/I96908189"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Yiran Liu","raw_affiliation_strings":["Xinjiang University,School of Computer Science and Technology,Urumqi,China"],"affiliations":[{"raw_affiliation_string":"Xinjiang University,School of Computer Science and Technology,Urumqi,China","institution_ids":["https://openalex.org/I96908189"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5020873493","display_name":"Jiong Yu","orcid":"https://orcid.org/0000-0002-9181-6720"},"institutions":[{"id":"https://openalex.org/I96908189","display_name":"Xinjiang University","ror":"https://ror.org/059gw8r13","country_code":"CN","type":"education","lineage":["https://openalex.org/I96908189"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jiong Yu","raw_affiliation_strings":["Xinjiang University,School of Computer Science and Technology,Urumqi,China"],"affiliations":[{"raw_affiliation_string":"Xinjiang University,School of Computer Science and Technology,Urumqi,China","institution_ids":["https://openalex.org/I96908189"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5102658005","display_name":"Chu Zhuang","orcid":null},"institutions":[{"id":"https://openalex.org/I96908189","display_name":"Xinjiang University","ror":"https://ror.org/059gw8r13","country_code":"CN","type":"education","lineage":["https://openalex.org/I96908189"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhuang Chu","raw_affiliation_strings":["Xinjiang University,School of Software,Urumqi,China"],"affiliations":[{"raw_affiliation_string":"Xinjiang University,School of Software,Urumqi,China","institution_ids":["https://openalex.org/I96908189"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100601850","display_name":"Li Shu","orcid":"https://orcid.org/0000-0002-7445-7455"},"institutions":[{"id":"https://openalex.org/I96908189","display_name":"Xinjiang University","ror":"https://ror.org/059gw8r13","country_code":"CN","type":"education","lineage":["https://openalex.org/I96908189"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Shu Li","raw_affiliation_strings":["Xinjiang University,School of Computer Science and Technology,Urumqi,China"],"affiliations":[{"raw_affiliation_string":"Xinjiang University,School of Computer Science and Technology,Urumqi,China","institution_ids":["https://openalex.org/I96908189"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5069710434","display_name":"Xusheng Du","orcid":"https://orcid.org/0000-0002-3586-6926"},"institutions":[{"id":"https://openalex.org/I4210126646","display_name":"Vanke (China)","ror":"https://ror.org/0329dzd04","country_code":"CN","type":"company","lineage":["https://openalex.org/I4210126646"]},{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xusheng Du","raw_affiliation_strings":["Tsinghua University,Vanke School of Public Health,Beijing,China"],"affiliations":[{"raw_affiliation_string":"Tsinghua University,Vanke School of Public Health,Beijing,China","institution_ids":["https://openalex.org/I99065089","https://openalex.org/I4210126646"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5100747159"],"corresponding_institution_ids":["https://openalex.org/I96908189"],"apc_list":null,"apc_paid":null,"fwci":2.3568,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.91845129,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":96,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"9"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9846000075340271,"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/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9846000075340271,"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/T10917","display_name":"Smart Grid Security and Resilience","score":0.0017999999690800905,"subfield":{"id":"https://openalex.org/subfields/2207","display_name":"Control and Systems Engineering"},"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/T12535","display_name":"Machine Learning and Data Classification","score":0.0017999999690800905,"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/anomaly-detection","display_name":"Anomaly detection","score":0.7946000099182129},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5824000239372253},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.5486000180244446},{"id":"https://openalex.org/keywords/outlier","display_name":"Outlier","score":0.4729999899864197},{"id":"https://openalex.org/keywords/unsupervised-learning","display_name":"Unsupervised learning","score":0.42239999771118164},{"id":"https://openalex.org/keywords/euclidean-distance","display_name":"Euclidean distance","score":0.4147000014781952},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.3961000144481659},{"id":"https://openalex.org/keywords/focus","display_name":"Focus (optics)","score":0.3862999975681305}],"concepts":[{"id":"https://openalex.org/C739882","wikidata":"https://www.wikidata.org/wiki/Q3560506","display_name":"Anomaly detection","level":2,"score":0.7946000099182129},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6365000009536743},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5878000259399414},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5824000239372253},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.5486000180244446},{"id":"https://openalex.org/C79337645","wikidata":"https://www.wikidata.org/wiki/Q779824","display_name":"Outlier","level":2,"score":0.4729999899864197},{"id":"https://openalex.org/C8038995","wikidata":"https://www.wikidata.org/wiki/Q1152135","display_name":"Unsupervised learning","level":2,"score":0.42239999771118164},{"id":"https://openalex.org/C120174047","wikidata":"https://www.wikidata.org/wiki/Q847073","display_name":"Euclidean distance","level":2,"score":0.4147000014781952},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.3961000144481659},{"id":"https://openalex.org/C192209626","wikidata":"https://www.wikidata.org/wiki/Q190909","display_name":"Focus (optics)","level":2,"score":0.3862999975681305},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.374099999666214},{"id":"https://openalex.org/C21080849","wikidata":"https://www.wikidata.org/wiki/Q13611879","display_name":"Data point","level":2,"score":0.37040001153945923},{"id":"https://openalex.org/C12997251","wikidata":"https://www.wikidata.org/wiki/Q567560","display_name":"Anomaly (physics)","level":2,"score":0.3370000123977661},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.33160001039505005},{"id":"https://openalex.org/C2776145971","wikidata":"https://www.wikidata.org/wiki/Q30673951","display_name":"Labeled data","level":2,"score":0.328900009393692},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.31949999928474426},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.3003000020980835},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.28380000591278076},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.28290000557899475},{"id":"https://openalex.org/C129782007","wikidata":"https://www.wikidata.org/wiki/Q162886","display_name":"Euclidean geometry","level":2,"score":0.2705000042915344},{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.2703999876976013},{"id":"https://openalex.org/C34872919","wikidata":"https://www.wikidata.org/wiki/Q7092302","display_name":"One-class classification","level":3,"score":0.2671999931335449},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.2572999894618988},{"id":"https://openalex.org/C160920958","wikidata":"https://www.wikidata.org/wiki/Q7662746","display_name":"Synthetic data","level":2,"score":0.2547999918460846}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ijcnn64981.2025.11228687","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn64981.2025.11228687","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 International Joint Conference on Neural Networks (IJCNN)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":29,"referenced_works":["https://openalex.org/W1740377143","https://openalex.org/W2018815836","https://openalex.org/W2061240327","https://openalex.org/W2064029323","https://openalex.org/W2096179302","https://openalex.org/W2102999520","https://openalex.org/W2296719434","https://openalex.org/W3038625188","https://openalex.org/W3040266635","https://openalex.org/W3091873932","https://openalex.org/W3124243418","https://openalex.org/W4254182148","https://openalex.org/W4282980030","https://openalex.org/W4283810514","https://openalex.org/W4285066127","https://openalex.org/W4297828509","https://openalex.org/W4310465416","https://openalex.org/W4315798510","https://openalex.org/W4318811779","https://openalex.org/W4385284566","https://openalex.org/W4385781987","https://openalex.org/W4386918771","https://openalex.org/W4388099759","https://openalex.org/W4388602791","https://openalex.org/W4390956528","https://openalex.org/W4392693771","https://openalex.org/W4394779406","https://openalex.org/W4396757510","https://openalex.org/W4402227122"],"related_works":[],"abstract_inverted_index":{"Anomaly":[0],"detection":[1,21,88,193],"plays":[2],"a":[3,102,119],"crucial":[4],"role":[5],"in":[6,113],"various":[7],"fields":[8],"such":[9],"as":[10,147],"industrial":[11],"production,":[12],"medical":[13],"diagnostics,":[14],"and":[15,51,68,77,125,139,173],"cybersecurity.":[16],"However,":[17],"existing":[18],"unsupervised":[19,92],"outlier":[20],"methods":[22],"often":[23],"assume":[24],"that":[25,188],"data":[26,49,60,111,148,175,179],"instances":[27,50],"are":[28,145],"independent":[29],"of":[30,58,142,157],"each":[31],"other.":[32],"They":[33],"typically":[34],"focus":[35],"only":[36],"on":[37,72,91,183],"extracting":[38],"features":[39,71],"from":[40],"individual":[41],"instances,":[42],"while":[43],"overlooking":[44],"the":[45,56,107,133,140,153,161,166,178],"potential":[46],"correlations":[47],"between":[48,171],"their":[52],"features.":[53,159],"Due":[54],"to":[55,65,105,128,151,169,196],"lack":[57],"effective":[59],"augmentation":[61,149],"strategies,":[62],"models":[63],"struggle":[64],"adequately":[66],"extract":[67],"learn":[69,152],"abnormal":[70,174],"datasets":[73,186],"with":[74],"imbalanced":[75],"classes":[76],"few":[78],"anomalies.":[79],"To":[80],"address":[81],"these":[82],"challenges,":[83],"we":[84],"propose":[85],"an":[86],"anomaly":[87,130,136,158,192],"method":[89],"based":[90],"dual-discriminative":[93],"graph":[94,104,126,162],"neural":[95],"networks,":[96],"called":[97],"UDG-AD.":[98],"Specifically,":[99],"UDG-AD":[100,189],"constructs":[101],"feature":[103],"capture":[106],"latent":[108],"relationships":[109],"among":[110],"points":[112],"Euclidean":[114],"space.":[115],"It":[116],"then":[117],"utilizes":[118],"dual-task":[120],"learning":[121],"framework,":[122],"combining":[123],"classification":[124,134],"reconstruction,":[127],"evaluate":[129],"scores.":[131],"In":[132],"task,":[135],"prior":[137],"knowledge":[138],"permutation":[141],"node":[143],"embeddings":[144],"used":[146],"strategies":[150],"most":[154],"relevant":[155],"representations":[156],"Meanwhile,":[160],"reconstruction":[163],"module":[164],"enhances":[165],"model\u2019s":[167],"ability":[168],"distinguish":[170],"normal":[172],"by":[176],"reconstructing":[177],"distribution.":[180],"Extensive":[181],"experiments":[182],"12":[184],"real-world":[185],"demonstrate":[187],"significantly":[190],"improves":[191],"accuracy":[194],"compared":[195],"other":[197],"state-of-the-art":[198],"methods.":[199]},"counts_by_year":[{"year":2026,"cited_by_count":1}],"updated_date":"2026-04-09T08:11:56.329763","created_date":"2025-11-14T00:00:00"}
