{"id":"https://openalex.org/W4409671349","doi":"https://doi.org/10.1145/3696410.3714520","title":"Grad: Guided Relation Diffusion Generation for Graph Augmentation in Graph Fraud Detection","display_name":"Grad: Guided Relation Diffusion Generation for Graph Augmentation in Graph Fraud Detection","publication_year":2025,"publication_date":"2025-04-22","ids":{"openalex":"https://openalex.org/W4409671349","doi":"https://doi.org/10.1145/3696410.3714520"},"language":"en","primary_location":{"id":"doi:10.1145/3696410.3714520","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3696410.3714520","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3696410.3714520","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the ACM on Web Conference 2025","raw_type":"proceedings-article"},"type":"article","indexed_in":["arxiv","crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://dl.acm.org/doi/pdf/10.1145/3696410.3714520","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5101957175","display_name":"Jie Yang","orcid":"https://orcid.org/0009-0009-7857-6863"},"institutions":[{"id":"https://openalex.org/I116953780","display_name":"Tongji University","ror":"https://ror.org/03rc6as71","country_code":"CN","type":"education","lineage":["https://openalex.org/I116953780"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Jie Yang","raw_affiliation_strings":["Tongji University, Shanghai, China"],"raw_orcid":"https://orcid.org/0009-0009-7857-6863","affiliations":[{"raw_affiliation_string":"Tongji University, Shanghai, China","institution_ids":["https://openalex.org/I116953780"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Rui Zhang","orcid":"https://orcid.org/0009-0003-5701-485X"},"institutions":[{"id":"https://openalex.org/I31746571","display_name":"UNSW Sydney","ror":"https://ror.org/03r8z3t63","country_code":"AU","type":"education","lineage":["https://openalex.org/I31746571"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Rui Zhang","raw_affiliation_strings":["The University of New South Wales, Sydney, Australia"],"raw_orcid":"https://orcid.org/0009-0003-5701-485X","affiliations":[{"raw_affiliation_string":"The University of New South Wales, Sydney, Australia","institution_ids":["https://openalex.org/I31746571"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5085833777","display_name":"Ziyang Cheng","orcid":"https://orcid.org/0009-0006-0975-4054"},"institutions":[{"id":"https://openalex.org/I116953780","display_name":"Tongji University","ror":"https://ror.org/03rc6as71","country_code":"CN","type":"education","lineage":["https://openalex.org/I116953780"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Ziyang Cheng","raw_affiliation_strings":["Tongji University, Shanghai, China"],"raw_orcid":"https://orcid.org/0009-0006-0975-4054","affiliations":[{"raw_affiliation_string":"Tongji University, Shanghai, China","institution_ids":["https://openalex.org/I116953780"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5069869295","display_name":"Dawei Cheng","orcid":"https://orcid.org/0000-0002-5877-7387"},"institutions":[{"id":"https://openalex.org/I116953780","display_name":"Tongji University","ror":"https://ror.org/03rc6as71","country_code":"CN","type":"education","lineage":["https://openalex.org/I116953780"]},{"id":"https://openalex.org/I4391012619","display_name":"Shanghai Artificial Intelligence Laboratory","ror":"https://ror.org/03wkvpx79","country_code":null,"type":"facility","lineage":["https://openalex.org/I4391012619"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Dawei Cheng","raw_affiliation_strings":["Tongji University, Shanghai, China and Shanghai Artificial Intelligence Laboratory, Shanghai, China"],"raw_orcid":"https://orcid.org/0000-0002-5877-7387","affiliations":[{"raw_affiliation_string":"Tongji University, Shanghai, China and Shanghai Artificial Intelligence Laboratory, Shanghai, China","institution_ids":["https://openalex.org/I116953780","https://openalex.org/I4391012619"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5110104631","display_name":"Guang Yang","orcid":"https://orcid.org/0009-0002-9593-7850"},"institutions":[{"id":"https://openalex.org/I2250653659","display_name":"Tencent (China)","ror":"https://ror.org/00hhjss72","country_code":"CN","type":"company","lineage":["https://openalex.org/I2250653659"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Guang Yang","raw_affiliation_strings":["Wechat Pay, Tencent Inc., Shenzhen, China"],"raw_orcid":"https://orcid.org/0009-0002-9593-7850","affiliations":[{"raw_affiliation_string":"Wechat Pay, Tencent Inc., Shenzhen, China","institution_ids":["https://openalex.org/I2250653659"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5092492808","display_name":"Bo Wang","orcid":"https://orcid.org/0009-0004-9426-8741"},"institutions":[{"id":"https://openalex.org/I2250653659","display_name":"Tencent (China)","ror":"https://ror.org/00hhjss72","country_code":"CN","type":"company","lineage":["https://openalex.org/I2250653659"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Bo Wang","raw_affiliation_strings":["Wechat Pay, Tencent Inc., Shenzhen, China"],"raw_orcid":"https://orcid.org/0009-0004-9426-8741","affiliations":[{"raw_affiliation_string":"Wechat Pay, Tencent Inc., Shenzhen, China","institution_ids":["https://openalex.org/I2250653659"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":6,"corresponding_author_ids":["https://openalex.org/A5101957175"],"corresponding_institution_ids":["https://openalex.org/I116953780"],"apc_list":null,"apc_paid":null,"fwci":13.0395,"has_fulltext":false,"cited_by_count":6,"citation_normalized_percentile":{"value":0.9836092,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":96,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"5308","last_page":"5319"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11273","display_name":"Advanced Graph Neural Networks","score":0.9990000128746033,"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":0.9990000128746033,"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":0.9977999925613403,"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/T11644","display_name":"Spam and Phishing Detection","score":0.9937000274658203,"subfield":{"id":"https://openalex.org/subfields/1710","display_name":"Information Systems"},"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":0.5989833474159241},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.5428398251533508},{"id":"https://openalex.org/keywords/relation","display_name":"Relation (database)","score":0.47093096375465393},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.3247716426849365},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.2168622612953186}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5989833474159241},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.5428398251533508},{"id":"https://openalex.org/C25343380","wikidata":"https://www.wikidata.org/wiki/Q277521","display_name":"Relation (database)","level":2,"score":0.47093096375465393},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.3247716426849365},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.2168622612953186}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1145/3696410.3714520","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3696410.3714520","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3696410.3714520","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the ACM on Web Conference 2025","raw_type":"proceedings-article"},{"id":"pmh:oai:arXiv.org:2512.18133","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2512.18133","pdf_url":"https://arxiv.org/pdf/2512.18133","source":{"id":"https://openalex.org/S4393918464","display_name":"ArXiv.org","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"}],"best_oa_location":{"id":"doi:10.1145/3696410.3714520","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3696410.3714520","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3696410.3714520","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the ACM on Web Conference 2025","raw_type":"proceedings-article"},"sustainable_development_goals":[{"display_name":"Peace, Justice and strong institutions","score":0.5099999904632568,"id":"https://metadata.un.org/sdg/16"}],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":false},"content_urls":{"pdf":"https://content.openalex.org/works/W4409671349.pdf"},"referenced_works_count":46,"referenced_works":["https://openalex.org/W2046253692","https://openalex.org/W2064058256","https://openalex.org/W2087347434","https://openalex.org/W2136891251","https://openalex.org/W2889158967","https://openalex.org/W2963523189","https://openalex.org/W2990138404","https://openalex.org/W3006520502","https://openalex.org/W3009901425","https://openalex.org/W3040213512","https://openalex.org/W3068123808","https://openalex.org/W3080922200","https://openalex.org/W3096831136","https://openalex.org/W3099064659","https://openalex.org/W3102969158","https://openalex.org/W3104425534","https://openalex.org/W3153858161","https://openalex.org/W3169450514","https://openalex.org/W3171044370","https://openalex.org/W4220759569","https://openalex.org/W4224882649","https://openalex.org/W4280545892","https://openalex.org/W4281706128","https://openalex.org/W4312126067","https://openalex.org/W4365398195","https://openalex.org/W4367047347","https://openalex.org/W4367721843","https://openalex.org/W4379927591","https://openalex.org/W4384652641","https://openalex.org/W4385562644","https://openalex.org/W4385568076","https://openalex.org/W4385763879","https://openalex.org/W4387841511","https://openalex.org/W4387846348","https://openalex.org/W4388994354","https://openalex.org/W4388994391","https://openalex.org/W4393147361","https://openalex.org/W4393161202","https://openalex.org/W4396758725","https://openalex.org/W4401024670","https://openalex.org/W4401856724","https://openalex.org/W4404390753","https://openalex.org/W4406457650","https://openalex.org/W4409657429","https://openalex.org/W6604801084","https://openalex.org/W7037116381"],"related_works":["https://openalex.org/W4391375266","https://openalex.org/W2899084033","https://openalex.org/W2748952813","https://openalex.org/W4234874385","https://openalex.org/W2390279801","https://openalex.org/W2323648130","https://openalex.org/W4391913857","https://openalex.org/W2358668433","https://openalex.org/W4396701345","https://openalex.org/W2157140558"],"abstract_inverted_index":{"Nowadays,":[0],"Graph":[1],"Fraud":[2],"Detection":[3],"(GFD)":[4],"in":[5,28,76,196,207],"financial":[6],"scenarios":[7],"has":[8],"become":[9],"an":[10],"urgent":[11],"research":[12],"topic":[13],"to":[14,62,118,130,154],"protect":[15],"online":[16,174],"payment":[17,175],"security.":[18],"However,":[19],"as":[20],"organized":[21],"crime":[22],"groups":[23],"are":[24,32,55],"becoming":[25],"more":[26,34],"professional":[27],"real-world":[29,164],"scenarios,":[30,199],"fraudsters":[31,39],"employing":[33],"sophisticated":[35],"camouflage":[36],"strategies.":[37],"Specifically,":[38],"disguise":[40],"themselves":[41],"by":[42,48,167],"mimicking":[43],"the":[44,74,85,120,147,172],"behavioral":[45,77],"data":[46],"collected":[47],"platforms,":[49],"ensuring":[50],"that":[51,188],"their":[52],"key":[53],"characteristics":[54],"consistent":[56],"with":[57,177],"those":[58],"of":[59,171,179],"benign":[60,82],"users":[61,83],"a":[63,101,112,125],"high":[64],"degree,":[65],"which":[66],"we":[67,99],"call":[68],"Adaptive":[69],"Camouflage.":[70],"Consequently,":[71],"this":[72,97],"narrows":[73],"differences":[75],"traits":[78],"between":[79],"them":[80],"and":[81,123,181,204,209],"within":[84],"platform's":[86],"database,":[87],"thereby":[88],"making":[89],"current":[90],"GFD":[91],"models":[92],"lose":[93],"efficiency.":[94],"To":[95],"address":[96],"problem,":[98],"propose":[100],"relation":[102,127],"diffusion-based":[103],"graph":[104,114],"augmentation":[105],"model":[106,191],"Grad.":[107],"In":[108],"detail,":[109],"Grad":[110,192],"leverages":[111],"supervised":[113],"contrastive":[115],"learning":[116],"module":[117],"enhance":[119],"fraud-benign":[121],"difference":[122],"employs":[124],"guided":[126],"diffusion":[128],"generator":[129],"generate":[131],"auxiliary":[132],"homophilic":[133],"relations":[134],"from":[135],"scratch.":[136],"Based":[137],"on":[138,162],"these,":[139],"weak":[140],"fraudulent":[141],"signals":[142],"would":[143],"be":[144,155],"enhanced":[145],"during":[146],"aggregation":[148],"process,":[149],"thus":[150],"being":[151],"obvious":[152],"enough":[153],"captured.":[156],"Extensive":[157],"experiments":[158],"have":[159],"been":[160],"conducted":[161],"two":[163],"datasets":[165],"provided":[166],"WeChat":[168],"Pay,":[169],"one":[170],"largest":[173],"platforms":[176],"billions":[178],"users,":[180],"three":[182],"public":[183],"datasets.":[184],"The":[185],"results":[186],"show":[187],"our":[189],"proposed":[190],"outperforms":[193],"SOTA":[194],"methods":[195],"both":[197],"various":[198],"achieving":[200],"at":[201],"most":[202],"11.10%":[203],"43.95%":[205],"increases":[206],"AUC":[208],"AP,":[210],"respectively.":[211]},"counts_by_year":[{"year":2026,"cited_by_count":3},{"year":2025,"cited_by_count":3}],"updated_date":"2026-03-27T05:58:40.876381","created_date":"2025-10-10T00:00:00"}
