{"id":"https://openalex.org/W4386768606","doi":"https://doi.org/10.14778/3611540.3611551","title":"MINT: Detecting Fraudulent Behaviors from Time-Series Relational Data","display_name":"MINT: Detecting Fraudulent Behaviors from Time-Series Relational Data","publication_year":2023,"publication_date":"2023-08-01","ids":{"openalex":"https://openalex.org/W4386768606","doi":"https://doi.org/10.14778/3611540.3611551"},"language":"en","primary_location":{"id":"doi:10.14778/3611540.3611551","is_oa":false,"landing_page_url":"https://doi.org/10.14778/3611540.3611551","pdf_url":null,"source":{"id":"https://openalex.org/S4210226185","display_name":"Proceedings of the VLDB Endowment","issn_l":"2150-8097","issn":["2150-8097"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319798","host_organization_name":"Association for Computing Machinery","host_organization_lineage":["https://openalex.org/P4310319798"],"host_organization_lineage_names":["Association for Computing Machinery"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the VLDB Endowment","raw_type":"journal-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/A5071824369","display_name":"Fei Xiao","orcid":"https://orcid.org/0000-0002-3789-6387"},"institutions":[{"id":"https://openalex.org/I165932596","display_name":"National University of Singapore","ror":"https://ror.org/01tgyzw49","country_code":"SG","type":"education","lineage":["https://openalex.org/I165932596"]}],"countries":["SG"],"is_corresponding":true,"raw_author_name":"Fei Xiao","raw_affiliation_strings":["Shopee Singapore, National University of Singapore"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Shopee Singapore, National University of Singapore","institution_ids":["https://openalex.org/I165932596"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101635565","display_name":"Yuncheng Wu","orcid":"https://orcid.org/0000-0002-1436-3916"},"institutions":[{"id":"https://openalex.org/I165932596","display_name":"National University of Singapore","ror":"https://ror.org/01tgyzw49","country_code":"SG","type":"education","lineage":["https://openalex.org/I165932596"]}],"countries":["SG"],"is_corresponding":false,"raw_author_name":"Yuncheng Wu","raw_affiliation_strings":["National University of Singapore"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"National University of Singapore","institution_ids":["https://openalex.org/I165932596"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5032263010","display_name":"Meihui Zhang","orcid":"https://orcid.org/0000-0002-0752-9877"},"institutions":[{"id":"https://openalex.org/I125839683","display_name":"Beijing Institute of Technology","ror":"https://ror.org/01skt4w74","country_code":"CN","type":"education","lineage":["https://openalex.org/I125839683","https://openalex.org/I890469752"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Meihui Zhang","raw_affiliation_strings":["Beijing Institute of Technology"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Beijing Institute of Technology","institution_ids":["https://openalex.org/I125839683"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100389286","display_name":"Gang Chen","orcid":"https://orcid.org/0000-0002-7483-0045"},"institutions":[{"id":"https://openalex.org/I76130692","display_name":"Zhejiang University","ror":"https://ror.org/00a2xv884","country_code":"CN","type":"education","lineage":["https://openalex.org/I76130692"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Gang Chen","raw_affiliation_strings":["Zhejiang University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Zhejiang University","institution_ids":["https://openalex.org/I76130692"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5024892041","display_name":"Beng Chin Ooi","orcid":"https://orcid.org/0000-0003-4446-1100"},"institutions":[{"id":"https://openalex.org/I165932596","display_name":"National University of Singapore","ror":"https://ror.org/01tgyzw49","country_code":"SG","type":"education","lineage":["https://openalex.org/I165932596"]}],"countries":["SG"],"is_corresponding":false,"raw_author_name":"Beng Chin Ooi","raw_affiliation_strings":["National University of Singapore"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"National University of Singapore","institution_ids":["https://openalex.org/I165932596"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5071824369"],"corresponding_institution_ids":["https://openalex.org/I165932596"],"apc_list":null,"apc_paid":null,"fwci":1.8224,"has_fulltext":false,"cited_by_count":11,"citation_normalized_percentile":{"value":0.88245138,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":98},"biblio":{"volume":"16","issue":"12","first_page":"3610","last_page":"3623"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11652","display_name":"Imbalanced Data Classification Techniques","score":0.9968000054359436,"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/T11652","display_name":"Imbalanced Data Classification Techniques","score":0.9968000054359436,"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/T11273","display_name":"Advanced Graph Neural Networks","score":0.9950000047683716,"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":0.8140875697135925},{"id":"https://openalex.org/keywords/scalability","display_name":"Scalability","score":0.7200488448143005},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.5746908187866211},{"id":"https://openalex.org/keywords/smoothing","display_name":"Smoothing","score":0.5278313755989075},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.45564186573028564},{"id":"https://openalex.org/keywords/exponential-smoothing","display_name":"Exponential smoothing","score":0.4515411853790283},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4320516884326935},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.41487014293670654},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.33061155676841736}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8140875697135925},{"id":"https://openalex.org/C48044578","wikidata":"https://www.wikidata.org/wiki/Q727490","display_name":"Scalability","level":2,"score":0.7200488448143005},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.5746908187866211},{"id":"https://openalex.org/C3770464","wikidata":"https://www.wikidata.org/wiki/Q775963","display_name":"Smoothing","level":2,"score":0.5278313755989075},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.45564186573028564},{"id":"https://openalex.org/C133710760","wikidata":"https://www.wikidata.org/wiki/Q775837","display_name":"Exponential smoothing","level":2,"score":0.4515411853790283},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4320516884326935},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.41487014293670654},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.33061155676841736},{"id":"https://openalex.org/C77088390","wikidata":"https://www.wikidata.org/wiki/Q8513","display_name":"Database","level":1,"score":0.0},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.14778/3611540.3611551","is_oa":false,"landing_page_url":"https://doi.org/10.14778/3611540.3611551","pdf_url":null,"source":{"id":"https://openalex.org/S4210226185","display_name":"Proceedings of the VLDB Endowment","issn_l":"2150-8097","issn":["2150-8097"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319798","host_organization_name":"Association for Computing Machinery","host_organization_lineage":["https://openalex.org/P4310319798"],"host_organization_lineage_names":["Association for Computing Machinery"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the VLDB Endowment","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.7200000286102295,"display_name":"Peace, Justice and strong institutions","id":"https://metadata.un.org/sdg/16"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":47,"referenced_works":["https://openalex.org/W2062022900","https://openalex.org/W2131774270","https://openalex.org/W2133591726","https://openalex.org/W2337240321","https://openalex.org/W2548532770","https://openalex.org/W2604314403","https://openalex.org/W2612690371","https://openalex.org/W2739805805","https://openalex.org/W2747329762","https://openalex.org/W2783666221","https://openalex.org/W2887231366","https://openalex.org/W2889320527","https://openalex.org/W2889440651","https://openalex.org/W2890165066","https://openalex.org/W2911239483","https://openalex.org/W2914166707","https://openalex.org/W2924545743","https://openalex.org/W2948405945","https://openalex.org/W2951761234","https://openalex.org/W2962946486","https://openalex.org/W2963555845","https://openalex.org/W2970632477","https://openalex.org/W2971681342","https://openalex.org/W2997130580","https://openalex.org/W3006399446","https://openalex.org/W3012650537","https://openalex.org/W3031898476","https://openalex.org/W3033991488","https://openalex.org/W3035298482","https://openalex.org/W3068123808","https://openalex.org/W3080342357","https://openalex.org/W3093782407","https://openalex.org/W3102969158","https://openalex.org/W3103177583","https://openalex.org/W3105757720","https://openalex.org/W3106181667","https://openalex.org/W3169637772","https://openalex.org/W3170163874","https://openalex.org/W3173587790","https://openalex.org/W3176287504","https://openalex.org/W3178212073","https://openalex.org/W4205516974","https://openalex.org/W4220721362","https://openalex.org/W4224310669","https://openalex.org/W4317767731","https://openalex.org/W4380433160","https://openalex.org/W6600103761"],"related_works":["https://openalex.org/W3110660585","https://openalex.org/W2068015237","https://openalex.org/W4285210468","https://openalex.org/W4382138423","https://openalex.org/W4385790854","https://openalex.org/W4236520329","https://openalex.org/W2375002273","https://openalex.org/W4206732483","https://openalex.org/W2093101924","https://openalex.org/W4226363941"],"abstract_inverted_index":{"The":[0,87],"e-commerce":[1,229],"platforms,":[2],"such":[3],"as":[4,48,109],"Shopee,":[5],"have":[6],"accumulated":[7],"a":[8,37,49,70,95,154,161,240],"huge":[9],"volume":[10],"of":[11,90,180],"time-series":[12,34,84,102],"relational":[13,103],"data,":[14],"which":[15],"contains":[16],"useful":[17],"information":[18,118,171],"on":[19,226],"differentiating":[20],"fraud":[21,27],"users":[22],"from":[23,83,132,231,243],"benign":[24],"users.":[25],"Existing":[26],"behavior":[28,97,157],"detection":[29],"approaches":[30],"typically":[31],"model":[32],"the":[33,45,54,115,129,150,169,177,181,227],"data":[35,104],"with":[36,105,233],"vanilla":[38,155],"Recurrent":[39],"Neural":[40],"Network":[41],"(RNN)":[42],"or":[43],"combine":[44],"whole":[46],"sequence":[47],"single":[50],"intention":[51],"without":[52],"considering":[53],"temporal":[55,117],"behavioral":[56],"patterns,":[57],"row-level":[58,146],"interactions,":[59],"and":[60,139,148,210,221,239,255,259],"different":[61,122,133],"view":[62],"intentions.":[63,141],"In":[64],"this":[65],"paper,":[66],"we":[67,159],"present":[68],"MINT,":[69],"M":[71],"ultiview":[72],"row-":[73],"IN":[74],"teractive":[75],"T":[76],"ime-aware":[77],"framework":[78],"to":[79,93,119,167,188,205,217,235],"detect":[80],"fraudulent":[81],"behaviors":[82],"structured":[85],"data.":[86],"key":[88],"idea":[89],"MINT":[91,193,247],"is":[92],"build":[94],"time-aware":[96,156],"graph":[98,123,183,201],"for":[99,126],"each":[100,106,173],"user's":[101,116,130],"row":[107],"represented":[108],"an":[110],"action":[111,174],"node.":[112,175],"We":[113],"utilize":[114],"construct":[120],"three":[121,182],"convolutional":[124,184],"matrices":[125],"hierarchically":[127],"learning":[128],"intentions":[131],"views,":[134],"that":[135,246],"is,":[136],"short-term,":[137],"medium-term,":[138],"long-term":[140],"To":[142],"capture":[143,206],"more":[144],"meaningful":[145],"interactions":[147],"alleviate":[149],"over-smoothing":[151],"issue":[152],"in":[153],"graph,":[158],"propose":[160],"novel":[162],"gated":[163],"neighbor":[164],"interaction":[165],"mechanism":[166],"calibrate":[168],"aggregated":[170],"by":[172],"Since":[176],"receptive":[178],"fields":[179],"layers":[185,197],"are":[186],"designed":[187],"grow":[189],"nearly":[190],"exponentially,":[191],"our":[192],"requires":[194],"many":[195],"fewer":[196],"than":[198],"traditional":[199],"deep":[200],"neural":[202],"networks":[203],"(GNNs)":[204],"multi-hop":[207],"neighboring":[208],"information,":[209],"avoids":[211],"recurrent":[212],"feedforward":[213],"propagation,":[214],"thus":[215],"leading":[216],"higher":[218],"training":[219],"efficiency":[220],"scalability.":[222,260],"Our":[223],"extensive":[224],"experiments":[225],"large-scale":[228],"datasets":[230],"Shopee":[232],"up":[234],"4.6":[236],"billion":[237],"records":[238],"public":[241],"dataset":[242],"Amazon":[244],"show":[245],"achieves":[248],"superior":[249],"performance":[250],"over":[251],"10":[252],"state-of-the-art":[253],"models":[254],"provides":[256],"better":[257],"interpretability":[258]},"counts_by_year":[{"year":2025,"cited_by_count":5},{"year":2024,"cited_by_count":5},{"year":2023,"cited_by_count":1}],"updated_date":"2026-05-21T06:26:12.895304","created_date":"2025-10-10T00:00:00"}
