{"id":"https://openalex.org/W2958123697","doi":"https://doi.org/10.1109/cifer.2019.8759109","title":"Auto-encoder based Graph Convolutional Networks for Online Financial Anti-fraud","display_name":"Auto-encoder based Graph Convolutional Networks for Online Financial Anti-fraud","publication_year":2019,"publication_date":"2019-05-01","ids":{"openalex":"https://openalex.org/W2958123697","doi":"https://doi.org/10.1109/cifer.2019.8759109","mag":"2958123697"},"language":"en","primary_location":{"id":"doi:10.1109/cifer.2019.8759109","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cifer.2019.8759109","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 IEEE Conference on Computational Intelligence for Financial Engineering & Economics (CIFEr)","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/A5100931698","display_name":"Le Lv","orcid":"https://orcid.org/0009-0000-9571-5049"},"institutions":[{"id":"https://openalex.org/I4210103986","display_name":"Jingdong (China)","ror":"https://ror.org/01dkjkq64","country_code":"CN","type":"company","lineage":["https://openalex.org/I4210103986"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Le Lv","raw_affiliation_strings":["JD Digits, Risk Management Center, Beijing, China"],"affiliations":[{"raw_affiliation_string":"JD Digits, Risk Management Center, Beijing, China","institution_ids":["https://openalex.org/I4210103986"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5110272050","display_name":"Jianbo Cheng","orcid":null},"institutions":[{"id":"https://openalex.org/I4210103986","display_name":"Jingdong (China)","ror":"https://ror.org/01dkjkq64","country_code":"CN","type":"company","lineage":["https://openalex.org/I4210103986"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jianbo Cheng","raw_affiliation_strings":["JD Digits, Risk Management Center, Beijing, China"],"affiliations":[{"raw_affiliation_string":"JD Digits, Risk Management Center, Beijing, China","institution_ids":["https://openalex.org/I4210103986"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5108765617","display_name":"Nanbo Peng","orcid":null},"institutions":[{"id":"https://openalex.org/I4210103986","display_name":"Jingdong (China)","ror":"https://ror.org/01dkjkq64","country_code":"CN","type":"company","lineage":["https://openalex.org/I4210103986"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Nanbo Peng","raw_affiliation_strings":["JD Digits, Risk Management Center, Beijing, China"],"affiliations":[{"raw_affiliation_string":"JD Digits, Risk Management Center, Beijing, China","institution_ids":["https://openalex.org/I4210103986"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100740354","display_name":"Fan Min","orcid":"https://orcid.org/0000-0002-3290-1036"},"institutions":[{"id":"https://openalex.org/I4210103986","display_name":"Jingdong (China)","ror":"https://ror.org/01dkjkq64","country_code":"CN","type":"company","lineage":["https://openalex.org/I4210103986"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Min Fan","raw_affiliation_strings":["JD Digits, Risk Management Center, Beijing, China"],"affiliations":[{"raw_affiliation_string":"JD Digits, Risk Management Center, Beijing, China","institution_ids":["https://openalex.org/I4210103986"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100624298","display_name":"Dongbin Zhao","orcid":"https://orcid.org/0000-0001-8218-9633"},"institutions":[{"id":"https://openalex.org/I19820366","display_name":"Chinese Academy of Sciences","ror":"https://ror.org/034t30j35","country_code":"CN","type":"funder","lineage":["https://openalex.org/I19820366"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Dongbin Zhao","raw_affiliation_strings":["The State Key Laboratory of Management and Control for Complex Systems, Chinese Academy of Sciences, Beijing, China"],"affiliations":[{"raw_affiliation_string":"The State Key Laboratory of Management and Control for Complex Systems, Chinese Academy of Sciences, Beijing, China","institution_ids":["https://openalex.org/I19820366"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100662562","display_name":"Jianhong Zhang","orcid":"https://orcid.org/0000-0001-8674-3988"},"institutions":[{"id":"https://openalex.org/I4210166468","display_name":"Beijing Aerospace Flight Control Center","ror":"https://ror.org/007a14354","country_code":"CN","type":"other","lineage":["https://openalex.org/I4210166468"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jianhong Zhang","raw_affiliation_strings":["Beijing Aerospace Automatic Control Institute, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Beijing Aerospace Automatic Control Institute, Beijing, China","institution_ids":["https://openalex.org/I4210166468"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":6,"corresponding_author_ids":["https://openalex.org/A5100931698"],"corresponding_institution_ids":["https://openalex.org/I4210103986"],"apc_list":null,"apc_paid":null,"fwci":0.7001,"has_fulltext":false,"cited_by_count":10,"citation_normalized_percentile":{"value":0.7773954,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":96},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"6"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11273","display_name":"Advanced Graph Neural Networks","score":0.9986000061035156,"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":0.9986000061035156,"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/T11652","display_name":"Imbalanced Data Classification Techniques","score":0.9972000122070312,"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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.996999979019165,"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/computer-science","display_name":"Computer science","score":0.7836203575134277},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.6780686378479004},{"id":"https://openalex.org/keywords/autoencoder","display_name":"Autoencoder","score":0.5930036306381226},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.5671859383583069},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5380051136016846},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5373218655586243},{"id":"https://openalex.org/keywords/convolution","display_name":"Convolution (computer science)","score":0.53134685754776},{"id":"https://openalex.org/keywords/encoder","display_name":"Encoder","score":0.5180390477180481},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.48544174432754517},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.4212934970855713},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.40759795904159546},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.35700732469558716}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7836203575134277},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.6780686378479004},{"id":"https://openalex.org/C101738243","wikidata":"https://www.wikidata.org/wiki/Q786435","display_name":"Autoencoder","level":3,"score":0.5930036306381226},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.5671859383583069},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5380051136016846},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5373218655586243},{"id":"https://openalex.org/C45347329","wikidata":"https://www.wikidata.org/wiki/Q5166604","display_name":"Convolution (computer science)","level":3,"score":0.53134685754776},{"id":"https://openalex.org/C118505674","wikidata":"https://www.wikidata.org/wiki/Q42586063","display_name":"Encoder","level":2,"score":0.5180390477180481},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.48544174432754517},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.4212934970855713},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.40759795904159546},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.35700732469558716},{"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/cifer.2019.8759109","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cifer.2019.8759109","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 IEEE Conference on Computational Intelligence for Financial Engineering & Economics (CIFEr)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.800000011920929,"id":"https://metadata.un.org/sdg/16","display_name":"Peace, Justice and strong institutions"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":29,"referenced_works":["https://openalex.org/W82771173","https://openalex.org/W1995562189","https://openalex.org/W2017257315","https://openalex.org/W2134842679","https://openalex.org/W2153959628","https://openalex.org/W2158787690","https://openalex.org/W2163922914","https://openalex.org/W2165874743","https://openalex.org/W2194775991","https://openalex.org/W2606780347","https://openalex.org/W2788242942","https://openalex.org/W2953267151","https://openalex.org/W2963017945","https://openalex.org/W2963312446","https://openalex.org/W2963858333","https://openalex.org/W2964015378","https://openalex.org/W2964051675","https://openalex.org/W2964321699","https://openalex.org/W3103358463","https://openalex.org/W4297733535","https://openalex.org/W6680067488","https://openalex.org/W6684578312","https://openalex.org/W6699364125","https://openalex.org/W6720006811","https://openalex.org/W6726873649","https://openalex.org/W6736685754","https://openalex.org/W6745537798","https://openalex.org/W6747954111","https://openalex.org/W6748141922"],"related_works":["https://openalex.org/W3013693939","https://openalex.org/W2669956259","https://openalex.org/W4249005693","https://openalex.org/W4392946183","https://openalex.org/W3088732000","https://openalex.org/W4226493464","https://openalex.org/W3133861977","https://openalex.org/W2951211570","https://openalex.org/W2964954556","https://openalex.org/W3103566983"],"abstract_inverted_index":{"Many":[0],"practical":[1],"problems":[2],"can":[3,88,99,116,131,145],"be":[4,89],"formulated":[5],"as":[6,91],"graph-based":[7],"semi-supervised":[8,40],"classification":[9,41,74],"problems.":[10,27],"For":[11],"example,":[12],"online":[13],"finance":[14],"anti-fraud.":[15],"Recently,":[16],"many":[17],"researchers":[18],"attempt":[19],"using":[20],"deep":[21],"learning":[22],"methods":[23],"to":[24,38,81],"solve":[25],"such":[26],"In":[28],"this":[29],"paper,":[30],"we":[31,76],"propose":[32],"a":[33,68],"novel":[34],"neural":[35,63,104],"network":[36,50,64,105],"architecture":[37],"perform":[39],"on":[42,96],"graph-structured":[43],"data.":[44],"We":[45],"improve":[46],"the":[47,54,73,78,83,101,111,118,122,147],"graph":[48,55,84,123],"convolutional":[49],"(GCN)":[51],"by":[52,67],"replacing":[53],"convolution":[56,85,95],"matrix":[57],"with":[58],"auto-encoder":[59,79],"module.":[60],"The":[61,138],"proposed":[62],"is":[65,126],"trained":[66],"multi-task":[69],"objective":[70],"function.":[71],"Except":[72],"task,":[75],"train":[77],"module":[80],"reconstruct":[82],"matrix.":[86],"It":[87,98],"seen":[90],"an":[92],"adaptive":[93],"spectral":[94],"graph.":[97],"increase":[100],"depth":[102],"of":[103,113,149],"without":[106],"causing":[107],"over-smooth":[108],"effect.":[109],"Additionally,":[110],"introduction":[112],"reconstruction":[114],"task":[115],"mitigate":[117],"cold-start":[119],"problem.":[120],"Even":[121],"topological":[124],"structure":[125],"extreme":[127],"sparse,":[128],"our":[129,143],"method":[130,144],"learn":[132],"expressive":[133],"latent":[134],"features":[135],"for":[136],"vertices.":[137],"experimental":[139],"results":[140],"show":[141],"that":[142],"achieve":[146],"state":[148],"art":[150],"performance.":[151]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":2},{"year":2022,"cited_by_count":2},{"year":2021,"cited_by_count":2},{"year":2020,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}