{"id":"https://openalex.org/W4206503836","doi":"https://doi.org/10.1109/tnnls.2021.3136171","title":"Graph Convolutional Adversarial Networks for Spatiotemporal Anomaly Detection","display_name":"Graph Convolutional Adversarial Networks for Spatiotemporal Anomaly Detection","publication_year":2022,"publication_date":"2022-01-04","ids":{"openalex":"https://openalex.org/W4206503836","doi":"https://doi.org/10.1109/tnnls.2021.3136171","pmid":"https://pubmed.ncbi.nlm.nih.gov/34982699"},"language":"en","primary_location":{"id":"doi:10.1109/tnnls.2021.3136171","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tnnls.2021.3136171","pdf_url":null,"source":{"id":"https://openalex.org/S4210175523","display_name":"IEEE Transactions on Neural Networks and Learning Systems","issn_l":"2162-237X","issn":["2162-237X","2162-2388"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Neural Networks and Learning Systems","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","pubmed"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"Leyan Deng","orcid":"https://orcid.org/0000-0003-1723-6450"},"institutions":[{"id":"https://openalex.org/I126520041","display_name":"University of Science and Technology of China","ror":"https://ror.org/04c4dkn09","country_code":"CN","type":"education","lineage":["https://openalex.org/I126520041","https://openalex.org/I19820366"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Leyan Deng","raw_affiliation_strings":["School of Data Science, University of Science and Technology of China, Hefei, China"],"affiliations":[{"raw_affiliation_string":"School of Data Science, University of Science and Technology of China, Hefei, China","institution_ids":["https://openalex.org/I126520041"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Defu Lian","orcid":"https://orcid.org/0000-0002-3507-9607"},"institutions":[{"id":"https://openalex.org/I126520041","display_name":"University of Science and Technology of China","ror":"https://ror.org/04c4dkn09","country_code":"CN","type":"education","lineage":["https://openalex.org/I126520041","https://openalex.org/I19820366"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Defu Lian","raw_affiliation_strings":["School of Data Science, University of Science and Technology of China, Hefei, China"],"affiliations":[{"raw_affiliation_string":"School of Data Science, University of Science and Technology of China, Hefei, China","institution_ids":["https://openalex.org/I126520041"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Zhenya Huang","orcid":"https://orcid.org/0000-0003-1661-0420"},"institutions":[{"id":"https://openalex.org/I126520041","display_name":"University of Science and Technology of China","ror":"https://ror.org/04c4dkn09","country_code":"CN","type":"education","lineage":["https://openalex.org/I126520041","https://openalex.org/I19820366"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhenya Huang","raw_affiliation_strings":["School of Computer Science and Technology, University of Science and Technology of China, Hefei, China"],"affiliations":[{"raw_affiliation_string":"School of Computer Science and Technology, University of Science and Technology of China, Hefei, China","institution_ids":["https://openalex.org/I126520041"]}]},{"author_position":"last","author":{"id":null,"display_name":"Enhong Chen","orcid":"https://orcid.org/0000-0002-4835-4102"},"institutions":[{"id":"https://openalex.org/I126520041","display_name":"University of Science and Technology of China","ror":"https://ror.org/04c4dkn09","country_code":"CN","type":"education","lineage":["https://openalex.org/I126520041","https://openalex.org/I19820366"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Enhong Chen","raw_affiliation_strings":["School of Data Science, University of Science and Technology of China, Hefei, China"],"affiliations":[{"raw_affiliation_string":"School of Data Science, University of Science and Technology of China, Hefei, China","institution_ids":["https://openalex.org/I126520041"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I126520041"],"apc_list":null,"apc_paid":null,"fwci":14.194,"has_fulltext":false,"cited_by_count":106,"citation_normalized_percentile":{"value":0.99129782,"is_in_top_1_percent":true,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":99,"max":100},"biblio":{"volume":"33","issue":"6","first_page":"2416","last_page":"2428"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9097999930381775,"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":0.9097999930381775,"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/T11344","display_name":"Traffic Prediction and Management Techniques","score":0.04179999977350235,"subfield":{"id":"https://openalex.org/subfields/2215","display_name":"Building and Construction"},"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/T12205","display_name":"Time Series Analysis and Forecasting","score":0.004100000020116568,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"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/discriminator","display_name":"Discriminator","score":0.8043000102043152},{"id":"https://openalex.org/keywords/anomaly-detection","display_name":"Anomaly detection","score":0.7448999881744385},{"id":"https://openalex.org/keywords/generator","display_name":"Generator (circuit theory)","score":0.6402999758720398},{"id":"https://openalex.org/keywords/leverage","display_name":"Leverage (statistics)","score":0.616599977016449},{"id":"https://openalex.org/keywords/adversarial-system","display_name":"Adversarial system","score":0.478300005197525},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.46380001306533813},{"id":"https://openalex.org/keywords/concept-drift","display_name":"Concept drift","score":0.35690000653266907}],"concepts":[{"id":"https://openalex.org/C2779803651","wikidata":"https://www.wikidata.org/wiki/Q5282088","display_name":"Discriminator","level":3,"score":0.8043000102043152},{"id":"https://openalex.org/C739882","wikidata":"https://www.wikidata.org/wiki/Q3560506","display_name":"Anomaly detection","level":2,"score":0.7448999881744385},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7271999716758728},{"id":"https://openalex.org/C2780992000","wikidata":"https://www.wikidata.org/wiki/Q17016113","display_name":"Generator (circuit theory)","level":3,"score":0.6402999758720398},{"id":"https://openalex.org/C153083717","wikidata":"https://www.wikidata.org/wiki/Q6535263","display_name":"Leverage (statistics)","level":2,"score":0.616599977016449},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.5200999975204468},{"id":"https://openalex.org/C37736160","wikidata":"https://www.wikidata.org/wiki/Q1801315","display_name":"Adversarial system","level":2,"score":0.478300005197525},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.46380001306533813},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.45820000767707825},{"id":"https://openalex.org/C60777511","wikidata":"https://www.wikidata.org/wiki/Q3045002","display_name":"Concept drift","level":3,"score":0.35690000653266907},{"id":"https://openalex.org/C12997251","wikidata":"https://www.wikidata.org/wiki/Q567560","display_name":"Anomaly (physics)","level":2,"score":0.3197999894618988},{"id":"https://openalex.org/C94915269","wikidata":"https://www.wikidata.org/wiki/Q1834857","display_name":"Detector","level":2,"score":0.3124000132083893},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.2996000051498413},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.29679998755455017},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.29269999265670776},{"id":"https://openalex.org/C2781317605","wikidata":"https://www.wikidata.org/wiki/Q7832483","display_name":"Traffic analysis","level":2,"score":0.29159998893737793},{"id":"https://openalex.org/C79403827","wikidata":"https://www.wikidata.org/wiki/Q3988","display_name":"Real-time computing","level":1,"score":0.2766999900341034},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.26969999074935913},{"id":"https://openalex.org/C2993660032","wikidata":"https://www.wikidata.org/wiki/Q746984","display_name":"Traffic speed","level":2,"score":0.2637999951839447}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/tnnls.2021.3136171","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tnnls.2021.3136171","pdf_url":null,"source":{"id":"https://openalex.org/S4210175523","display_name":"IEEE Transactions on Neural Networks and Learning Systems","issn_l":"2162-237X","issn":["2162-237X","2162-2388"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Neural Networks and Learning Systems","raw_type":"journal-article"},{"id":"pmid:34982699","is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/34982699","pdf_url":null,"source":{"id":"https://openalex.org/S4306525036","display_name":"PubMed","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE transactions on neural networks and learning systems","raw_type":null}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":52,"referenced_works":["https://openalex.org/W1525147170","https://openalex.org/W1980262437","https://openalex.org/W1982300822","https://openalex.org/W1994412661","https://openalex.org/W1999110238","https://openalex.org/W2010097736","https://openalex.org/W2019459021","https://openalex.org/W2022968603","https://openalex.org/W2027047167","https://openalex.org/W2054007841","https://openalex.org/W2058898885","https://openalex.org/W2097241863","https://openalex.org/W2099471712","https://openalex.org/W2101491865","https://openalex.org/W2110413449","https://openalex.org/W2112526520","https://openalex.org/W2112738128","https://openalex.org/W2127735975","https://openalex.org/W2129352331","https://openalex.org/W2164489414","https://openalex.org/W2168903146","https://openalex.org/W2294723619","https://openalex.org/W2296719434","https://openalex.org/W2443352794","https://openalex.org/W2465297350","https://openalex.org/W2492525761","https://openalex.org/W2517640113","https://openalex.org/W2547208782","https://openalex.org/W2565330852","https://openalex.org/W2599354622","https://openalex.org/W2741267865","https://openalex.org/W2783937577","https://openalex.org/W2794774115","https://openalex.org/W2795138333","https://openalex.org/W2903871660","https://openalex.org/W2911200746","https://openalex.org/W2911964244","https://openalex.org/W2942843559","https://openalex.org/W2963045681","https://openalex.org/W2964232409","https://openalex.org/W2964331599","https://openalex.org/W2966587068","https://openalex.org/W2971381425","https://openalex.org/W3023625663","https://openalex.org/W4249224151","https://openalex.org/W4254182148","https://openalex.org/W6631190155","https://openalex.org/W6640212811","https://openalex.org/W6731966471","https://openalex.org/W6746015598","https://openalex.org/W6785773631","https://openalex.org/W6948268659"],"related_works":[],"abstract_inverted_index":{"Traffic":[0],"anomalies,":[1,158],"such":[2],"as":[3,170],"traffic":[4,19,35,48,57,63,99,154,157,179,211,240],"accidents":[5],"and":[6,31,69,101,126,135,147,156,165,182,228,243],"unexpected":[7],"crowd":[8],"gathering,":[9],"may":[10,65],"endanger":[11],"public":[12],"safety":[13],"if":[14],"not":[15],"handled":[16],"timely.":[17],"Detecting":[18],"anomalies":[20,64,241],"in":[21,123],"their":[22],"early":[23],"stage":[24],"can":[25,167],"benefit":[26],"citizens'":[27],"quality":[28],"of":[29,56,62,153,202,209],"life":[30],"city":[32],"planning.":[33],"However,":[34],"anomaly":[36,197,251],"detection":[37,186,256],"faces":[38],"two":[39,203,221],"main":[40],"challenges.":[41,87],"First,":[42],"it":[43],"is":[44,111],"challenging":[45],"to":[46,51,83,95,105,143,213],"model":[47],"dynamics":[49,100,155,180,212],"due":[50],"the":[52,60,97,124,145,150,163,174,177,183,200,207,214,235,245,249,259],"complex":[53],"spatiotemporal":[54,77,93,103,151,190],"characteristics":[55],"data.":[58],"Second,":[59],"criteria":[61,187],"vary":[66],"with":[67,189],"locations":[68],"times.":[70],"In":[71],"this":[72],"article,":[73],"we":[74,90,130],"propose":[75,131],"a":[76,92,102,132,195],"graph":[78,137],"convolutional":[79,138],"adversarial":[80,161],"network":[81],"(STGAN)":[82],"address":[84],"these":[85],"above":[86],"More":[88],"specifically,":[89],"devise":[91],"generator":[94,146,164,175],"predict":[96],"normal":[98,178],"discriminator":[104,148,166,184],"determine":[106],"whether":[107],"an":[108],"input":[109],"sequence":[110],"real":[112],"or":[113],"not.":[114],"There":[115],"are":[116],"high":[117],"correlations":[118],"between":[119],"neighboring":[120],"data":[121],"points":[122],"spatial":[125],"temporal":[127],"dimensions.":[128],"Therefore,":[129],"recent":[133],"module":[134],"leverage":[136],"gated":[139],"recurrent":[140],"unit":[141],"(GCGRU)":[142],"help":[144],"learn":[149],"features":[152],"respectively.":[159],"After":[160],"training,":[162],"be":[168],"used":[169],"detectors":[171],"independently,":[172],"where":[173],"models":[176],"patterns":[181],"provides":[185],"varying":[188],"features.":[191],"We":[192,216],"then":[193],"design":[194],"novel":[196],"score":[198,252],"combining":[199],"abilities":[201],"detectors,":[204],"which":[205],"considers":[206],"misleading":[208],"unpredictable":[210],"discriminator.":[215],"evaluate":[217],"our":[218],"method":[219,237],"on":[220],"real-world":[222],"datasets":[223],"from":[224],"New":[225],"York":[226],"City":[227],"California.":[229],"The":[230],"experimental":[231],"results":[232],"show":[233],"that":[234],"proposed":[236],"detects":[238],"various":[239],"effectively":[242],"outperforms":[244],"state-of-the-art":[246],"methods.":[247],"Furthermore,":[248],"devised":[250],"achieves":[253],"more":[254],"robust":[255],"performances":[257],"than":[258],"general":[260],"score.":[261]},"counts_by_year":[{"year":2026,"cited_by_count":3},{"year":2025,"cited_by_count":37},{"year":2024,"cited_by_count":29},{"year":2023,"cited_by_count":25},{"year":2022,"cited_by_count":12}],"updated_date":"2026-03-18T14:38:29.013473","created_date":"2022-01-25T00:00:00"}