{"id":"https://openalex.org/W7147339340","doi":"https://doi.org/10.1109/cnml68938.2026.11452378","title":"CSSA: A Cross-modal Semantic-Structural Alignment Framework via LLMs and Graph Contrastive Learning for Fraud Detection of Online Payment","display_name":"CSSA: A Cross-modal Semantic-Structural Alignment Framework via LLMs and Graph Contrastive Learning for Fraud Detection of Online Payment","publication_year":2026,"publication_date":"2026-01-30","ids":{"openalex":"https://openalex.org/W7147339340","doi":"https://doi.org/10.1109/cnml68938.2026.11452378"},"language":null,"primary_location":{"id":"doi:10.1109/cnml68938.2026.11452378","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cnml68938.2026.11452378","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2026 International Conference on Communication Networks and Machine Learning (CNML)","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/A5125403844","display_name":"Zirui Zhao","orcid":null},"institutions":[{"id":"https://openalex.org/I21741975","display_name":"Suzhou University of Technology","ror":"https://ror.org/05g6ben79","country_code":"CN","type":"education","lineage":["https://openalex.org/I21741975"]},{"id":"https://openalex.org/I4210144214","display_name":"Jiangsu University of Technology","ror":"https://ror.org/04jabhf80","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210144214"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Zirui Zhao","raw_affiliation_strings":["Suzhou University of Technology, Changshu,Jiangsu,P.R. China"],"affiliations":[{"raw_affiliation_string":"Suzhou University of Technology, Changshu,Jiangsu,P.R. China","institution_ids":["https://openalex.org/I21741975","https://openalex.org/I4210144214"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5132598599","display_name":"Keyu Yuan","orcid":null},"institutions":[{"id":"https://openalex.org/I57206974","display_name":"New York University","ror":"https://ror.org/0190ak572","country_code":"US","type":"education","lineage":["https://openalex.org/I57206974"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Keyu Yuan","raw_affiliation_strings":["New York University,NY,U.S.A,11201"],"affiliations":[{"raw_affiliation_string":"New York University,NY,U.S.A,11201","institution_ids":["https://openalex.org/I57206974"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101850408","display_name":"Ziyue Wang","orcid":"https://orcid.org/0000-0002-6080-1040"},"institutions":[{"id":"https://openalex.org/I4210164339","display_name":"Oldham Council","ror":"https://ror.org/05cxzzq07","country_code":"GB","type":"government","lineage":["https://openalex.org/I4210164339"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Ziyue Wang","raw_affiliation_strings":["Independent Researcher,New York,NY,U.S.A"],"affiliations":[{"raw_affiliation_string":"Independent Researcher,New York,NY,U.S.A","institution_ids":["https://openalex.org/I4210164339"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5111149702","display_name":"Jiaqing Shen","orcid":null},"institutions":[{"id":"https://openalex.org/I155173764","display_name":"Rochester Institute of Technology","ror":"https://ror.org/00v4yb702","country_code":"US","type":"education","lineage":["https://openalex.org/I155173764"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Jiaqing Shen","raw_affiliation_strings":["Rochester Institute of Technology,Rochester,NY,U.S.A.,14623"],"affiliations":[{"raw_affiliation_string":"Rochester Institute of Technology,Rochester,NY,U.S.A.,14623","institution_ids":["https://openalex.org/I155173764"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5101879954","display_name":"Yuting Huang","orcid":"https://orcid.org/0000-0002-7949-5872"},"institutions":[{"id":"https://openalex.org/I1174212","display_name":"University of Southern California","ror":"https://ror.org/03taz7m60","country_code":"US","type":"education","lineage":["https://openalex.org/I1174212"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Yirui Huang","raw_affiliation_strings":["University of Southern California,Los Angeles,CA,United States,90089"],"affiliations":[{"raw_affiliation_string":"University of Southern California,Los Angeles,CA,United States,90089","institution_ids":["https://openalex.org/I1174212"]}]}],"institutions":[],"countries_distinct_count":3,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5125403844"],"corresponding_institution_ids":["https://openalex.org/I21741975","https://openalex.org/I4210144214"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.94309406,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"289","last_page":"292"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11273","display_name":"Advanced Graph Neural Networks","score":0.9020000100135803,"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":0.9020000100135803,"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":0.015799999237060547,"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/T13702","display_name":"Machine Learning in Healthcare","score":0.010700000450015068,"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/graph","display_name":"Graph","score":0.4699000120162964},{"id":"https://openalex.org/keywords/database-transaction","display_name":"Database transaction","score":0.45339998602867126},{"id":"https://openalex.org/keywords/class","display_name":"Class (philosophy)","score":0.4399000108242035},{"id":"https://openalex.org/keywords/adversarial-system","display_name":"Adversarial system","score":0.4221999943256378},{"id":"https://openalex.org/keywords/closeness","display_name":"Closeness","score":0.421099990606308},{"id":"https://openalex.org/keywords/semantics","display_name":"Semantics (computer science)","score":0.3763999938964844},{"id":"https://openalex.org/keywords/feature-learning","display_name":"Feature learning","score":0.33820000290870667},{"id":"https://openalex.org/keywords/node","display_name":"Node (physics)","score":0.3222000002861023}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7429999709129333},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4927000105381012},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.4699000120162964},{"id":"https://openalex.org/C75949130","wikidata":"https://www.wikidata.org/wiki/Q848010","display_name":"Database transaction","level":2,"score":0.45339998602867126},{"id":"https://openalex.org/C2777212361","wikidata":"https://www.wikidata.org/wiki/Q5127848","display_name":"Class (philosophy)","level":2,"score":0.4399000108242035},{"id":"https://openalex.org/C37736160","wikidata":"https://www.wikidata.org/wiki/Q1801315","display_name":"Adversarial system","level":2,"score":0.4221999943256378},{"id":"https://openalex.org/C2779545769","wikidata":"https://www.wikidata.org/wiki/Q5135364","display_name":"Closeness","level":2,"score":0.421099990606308},{"id":"https://openalex.org/C184337299","wikidata":"https://www.wikidata.org/wiki/Q1437428","display_name":"Semantics (computer science)","level":2,"score":0.3763999938964844},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.35749998688697815},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.33820000290870667},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.32269999384880066},{"id":"https://openalex.org/C62611344","wikidata":"https://www.wikidata.org/wiki/Q1062658","display_name":"Node (physics)","level":2,"score":0.3222000002861023},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.31690001487731934},{"id":"https://openalex.org/C145097563","wikidata":"https://www.wikidata.org/wiki/Q1148747","display_name":"Payment","level":2,"score":0.31610000133514404},{"id":"https://openalex.org/C2779338814","wikidata":"https://www.wikidata.org/wiki/Q5179285","display_name":"Covert","level":2,"score":0.29840001463890076},{"id":"https://openalex.org/C161301231","wikidata":"https://www.wikidata.org/wiki/Q3478658","display_name":"Knowledge representation and reasoning","level":2,"score":0.28999999165534973},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.28349998593330383},{"id":"https://openalex.org/C25810664","wikidata":"https://www.wikidata.org/wiki/Q44325","display_name":"Ontology","level":2,"score":0.2824000120162964},{"id":"https://openalex.org/C110903229","wikidata":"https://www.wikidata.org/wiki/Q7449064","display_name":"Semantic integration","level":4,"score":0.28040000796318054},{"id":"https://openalex.org/C2779227376","wikidata":"https://www.wikidata.org/wiki/Q6505497","display_name":"Layer (electronics)","level":2,"score":0.2752000093460083},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.2703999876976013},{"id":"https://openalex.org/C85407183","wikidata":"https://www.wikidata.org/wiki/Q1045785","display_name":"Semantic network","level":2,"score":0.2689000070095062},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.26269999146461487},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.25760000944137573}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/cnml68938.2026.11452378","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cnml68938.2026.11452378","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2026 International Conference on Communication Networks and Machine Learning (CNML)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Peace, Justice and strong institutions","id":"https://metadata.un.org/sdg/16","score":0.8300930261611938}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":14,"referenced_works":["https://openalex.org/W4401953687","https://openalex.org/W4406459656","https://openalex.org/W4410110346","https://openalex.org/W4410701935","https://openalex.org/W4411322922","https://openalex.org/W4412479275","https://openalex.org/W4414499224","https://openalex.org/W4417358068","https://openalex.org/W7119974740","https://openalex.org/W7124759612","https://openalex.org/W7125826534","https://openalex.org/W7127291052","https://openalex.org/W7131293926","https://openalex.org/W7138031522"],"related_works":[],"abstract_inverted_index":{"Graph":[0],"Neural":[1],"Networks":[2],"(GNNs)":[3],"have":[4],"demonstrated":[5],"exceptional":[6],"performance":[7],"in":[8,16,40,51,153],"modeling":[9],"structural":[10,20,122],"dependencies":[11],"within":[12],"networked":[13],"data.":[14],"However,":[15],"complex":[17],"decision-making":[18],"environments,":[19],"information":[21],"alone":[22],"often":[23],"fails":[24],"to":[25,92,113,149],"capture":[26],"the":[27,66,72,102,115],"latent":[28],"semantic":[29,41,88,119],"logic":[30],"and":[31,121,146,160],"domain-specific":[32],"heuristics.":[33],"While":[34],"Large":[35],"Language":[36],"Models":[37],"(LLMs)":[38],"excel":[39],"reasoning,":[42],"their":[43],"integration":[44],"with":[45,71],"graph-structured":[46],"data":[47],"remains":[48],"loosely":[49],"coupled":[50],"existing":[52],"literature.":[53],"This":[54],"paper":[55],"proposes":[56],"CSSA,":[57],"a":[58,78,98,128,143],"novel":[59],"Cross-modal":[60],"Semantic-Structural":[61],"Alignment":[62],"framework":[63],"that":[64,140],"synergizes":[65],"zero-shot":[67],"reasoning":[68],"of":[69,75,131],"LLMs":[70,91],"topological":[73],"aggregation":[74],"GNNs":[76],"through":[77],"contrastive":[79],"learning":[80],"objective.":[81],"Specifically,":[82],"we":[83],"treat":[84],"node":[85],"attributes":[86],"as":[87],"prompts":[89],"for":[90],"distill":[93],"high-level":[94],"\"risk":[95],"indicators,\"":[96],"while":[97],"GNN":[99],"branch":[100],"encodes":[101],"local":[103],"neighborhood":[104],"topology.":[105],"A":[106],"cross-modal":[107],"alignment":[108],"layer":[109],"is":[110],"then":[111],"introduced":[112],"minimize":[114],"representational":[116],"gap":[117],"between":[118],"intent":[120],"behavior.":[123],"We":[124],"evaluate":[125],"CSSA":[126,141],"on":[127],"massive":[129],"dataset":[130],"2.84":[132],"million":[133],"online":[134],"transaction":[135],"records.":[136],"Experimental":[137],"results":[138],"demonstrate":[139],"achieves":[142],"superior":[144],"F1-score":[145],"AUC":[147],"compared":[148],"state-of-the-art":[150],"GNNs,":[151],"particularly":[152],"scenarios":[154],"characterized":[155],"by":[156],"extreme":[157],"class":[158],"imbalance":[159],"covert":[161],"adversarial":[162],"patterns.":[163]},"counts_by_year":[],"updated_date":"2026-04-02T13:53:19.096889","created_date":"2026-04-02T00:00:00"}