{"id":"https://openalex.org/W4416251016","doi":"https://doi.org/10.1109/ijcnn64981.2025.11229249","title":"Dual-channel Heterophilic Message Passing for Graph Fraud Detection","display_name":"Dual-channel Heterophilic Message Passing for Graph Fraud Detection","publication_year":2025,"publication_date":"2025-06-30","ids":{"openalex":"https://openalex.org/W4416251016","doi":"https://doi.org/10.1109/ijcnn64981.2025.11229249"},"language":null,"primary_location":{"id":"doi:10.1109/ijcnn64981.2025.11229249","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn64981.2025.11229249","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 International Joint Conference on Neural Networks (IJCNN)","raw_type":"proceedings-article"},"type":"conference-paper","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/A5036114061","display_name":"Wenxin Zhang","orcid":"https://orcid.org/0009-0000-8916-6944"},"institutions":[{"id":"https://openalex.org/I4210165038","display_name":"University of Chinese Academy of Sciences","ror":"https://ror.org/05qbk4x57","country_code":"CN","type":"education","lineage":["https://openalex.org/I19820366","https://openalex.org/I4210165038"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wenxin Zhang","raw_affiliation_strings":["University of Chinese Academy of Science,Beijing,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Chinese Academy of Science,Beijing,China","institution_ids":["https://openalex.org/I4210165038"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5111284254","display_name":"Jingxing Zhong","orcid":"https://orcid.org/0009-0006-8190-6112"},"institutions":[{"id":"https://openalex.org/I80947539","display_name":"Fuzhou University","ror":"https://ror.org/011xvna82","country_code":"CN","type":"education","lineage":["https://openalex.org/I80947539"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jingxing Zhong","raw_affiliation_strings":["Fuzhou University,Fuzhou,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Fuzhou University,Fuzhou,China","institution_ids":["https://openalex.org/I80947539"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5065454900","display_name":"Guangzhen Yao","orcid":"https://orcid.org/0009-0002-1323-6998"},"institutions":[{"id":"https://openalex.org/I184983240","display_name":"Northeast Normal University","ror":"https://ror.org/02rkvz144","country_code":"CN","type":"education","lineage":["https://openalex.org/I184983240"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Guangzhen Yao","raw_affiliation_strings":["Northeast Normal University,Changchun,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Northeast Normal University,Changchun,China","institution_ids":["https://openalex.org/I184983240"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5116585415","display_name":"Renda Han","orcid":"https://orcid.org/0009-0009-8568-2285"},"institutions":[{"id":"https://openalex.org/I20942203","display_name":"Hainan University","ror":"https://ror.org/03q648j11","country_code":"CN","type":"education","lineage":["https://openalex.org/I20942203"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Renda Han","raw_affiliation_strings":["Hainan University,Haikou,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Hainan University,Haikou,China","institution_ids":["https://openalex.org/I20942203"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100542390","display_name":"Xiaojian Lin","orcid":null},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiaojian Lin","raw_affiliation_strings":["Tsinghua University,Beijing,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Tsinghua University,Beijing,China","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5114859279","display_name":"Lei Jiang","orcid":"https://orcid.org/0000-0001-6526-6430"},"institutions":[{"id":"https://openalex.org/I4210165038","display_name":"University of Chinese Academy of Sciences","ror":"https://ror.org/05qbk4x57","country_code":"CN","type":"education","lineage":["https://openalex.org/I19820366","https://openalex.org/I4210165038"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Lei Jiang","raw_affiliation_strings":["University of Chinese Academy of Science,Beijing,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Chinese Academy of Science,Beijing,China","institution_ids":["https://openalex.org/I4210165038"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100358738","display_name":"Zeyu Zhang","orcid":"https://orcid.org/0000-0002-7583-1867"},"institutions":[{"id":"https://openalex.org/I118347636","display_name":"Australian National University","ror":"https://ror.org/019wvm592","country_code":"AU","type":"education","lineage":["https://openalex.org/I118347636"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Zeyu Zhang","raw_affiliation_strings":["The Australian National University,Canberra,Australia"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"The Australian National University,Canberra,Australia","institution_ids":["https://openalex.org/I118347636"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5055992274","display_name":"Cuicui Luo","orcid":"https://orcid.org/0000-0002-4570-5990"},"institutions":[{"id":"https://openalex.org/I4210165038","display_name":"University of Chinese Academy of Sciences","ror":"https://ror.org/05qbk4x57","country_code":"CN","type":"education","lineage":["https://openalex.org/I19820366","https://openalex.org/I4210165038"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Cuicui Luo","raw_affiliation_strings":["University of Chinese Academy of Science,Beijing,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Chinese Academy of Science,Beijing,China","institution_ids":["https://openalex.org/I4210165038"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":6,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":5.019,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":{"value":0.96030177,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":98,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"8"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11273","display_name":"Advanced Graph Neural Networks","score":0.9871000051498413,"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":0.9871000051498413,"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/T10028","display_name":"Topic Modeling","score":0.0012000000569969416,"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/T10064","display_name":"Complex Network Analysis Techniques","score":0.0010999999940395355,"subfield":{"id":"https://openalex.org/subfields/3109","display_name":"Statistical and Nonlinear Physics"},"field":{"id":"https://openalex.org/fields/31","display_name":"Physics and Astronomy"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/message-passing","display_name":"Message passing","score":0.6891000270843506},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.6431999802589417},{"id":"https://openalex.org/keywords/call-graph","display_name":"Call graph","score":0.3666999936103821},{"id":"https://openalex.org/keywords/inductive-bias","display_name":"Inductive bias","score":0.32600000500679016},{"id":"https://openalex.org/keywords/network-topology","display_name":"Network topology","score":0.32519999146461487},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.3086000084877014}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7785999774932861},{"id":"https://openalex.org/C854659","wikidata":"https://www.wikidata.org/wiki/Q1859284","display_name":"Message passing","level":2,"score":0.6891000270843506},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.6431999802589417},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.4878999888896942},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.40299999713897705},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.37279999256134033},{"id":"https://openalex.org/C102379954","wikidata":"https://www.wikidata.org/wiki/Q2589940","display_name":"Call graph","level":2,"score":0.3666999936103821},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3619000017642975},{"id":"https://openalex.org/C197352929","wikidata":"https://www.wikidata.org/wiki/Q1074074","display_name":"Inductive bias","level":4,"score":0.32600000500679016},{"id":"https://openalex.org/C199845137","wikidata":"https://www.wikidata.org/wiki/Q145490","display_name":"Network topology","level":2,"score":0.32519999146461487},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.3086000084877014},{"id":"https://openalex.org/C3018234147","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Undirected graph","level":3,"score":0.29339998960494995},{"id":"https://openalex.org/C88230418","wikidata":"https://www.wikidata.org/wiki/Q131476","display_name":"Graph theory","level":2,"score":0.2863999903202057},{"id":"https://openalex.org/C2993807640","wikidata":"https://www.wikidata.org/wiki/Q103709453","display_name":"Attention network","level":2,"score":0.2856999933719635},{"id":"https://openalex.org/C36464697","wikidata":"https://www.wikidata.org/wiki/Q451553","display_name":"Visualization","level":2,"score":0.2547999918460846},{"id":"https://openalex.org/C43126263","wikidata":"https://www.wikidata.org/wiki/Q128751","display_name":"Source code","level":2,"score":0.2517000138759613}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ijcnn64981.2025.11229249","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn64981.2025.11229249","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 International Joint Conference on Neural Networks (IJCNN)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":14,"referenced_works":["https://openalex.org/W2295598076","https://openalex.org/W3068123808","https://openalex.org/W3128443161","https://openalex.org/W4213043044","https://openalex.org/W4224310669","https://openalex.org/W4231449374","https://openalex.org/W4376868835","https://openalex.org/W4387846516","https://openalex.org/W4389633671","https://openalex.org/W4389687725","https://openalex.org/W4391549729","https://openalex.org/W4393159755","https://openalex.org/W4401857656","https://openalex.org/W4402352530"],"related_works":[],"abstract_inverted_index":{"Fraudulent":[0],"activities":[1],"have":[2,28],"significantly":[3],"increased":[4],"across":[5],"various":[6,152],"domains,":[7],"such":[8],"as":[9],"e-commerce,":[10],"online":[11],"review":[12],"platforms,":[13],"and":[14,77,112,135],"social":[15],"networks,":[16],"making":[17],"fraud":[18,33,98,181],"detection":[19,34],"a":[20,89,102,137],"critical":[21],"task.":[22],"Spatial":[23],"Graph":[24],"Neural":[25],"Networks":[26],"(GNNs)":[27],"been":[29],"successfully":[30],"applied":[31],"to":[32,37,60,106,128,146],"tasks":[35],"due":[36],"their":[38,156],"strong":[39],"inductive":[40,118],"learning":[41],"capabilities.":[42],"However,":[43],"existing":[44,168],"spatial":[45],"GNN-based":[46],"methods":[47],"often":[48],"enhance":[49],"the":[50,63,73,108,116,149,171],"graph":[51,75,109],"structure":[52],"by":[53],"excluding":[54],"heterophilic":[55,113],"neighbors":[56],"during":[57],"message":[58],"passing":[59],"align":[61],"with":[62,176],"homophilic":[64,111],"bias":[65,119],"of":[66,120,151,173],"GNNs.":[67,122],"Unfortunately,":[68],"this":[69,86],"approach":[70],"can":[71],"disrupt":[72],"original":[74],"topology":[76],"increase":[78],"uncertainty":[79],"in":[80],"predictions.":[81],"To":[82],"address":[83],"these":[84],"limitations,":[85],"paper":[87],"proposes":[88],"novel":[90],"framework,":[91],"Dual-channel":[92],"Heterophilic":[93],"Message":[94],"Passing":[95],"(DHMP),":[96],"for":[97,141,179],"detection.":[99,182],"DHMP":[100,166],"leverages":[101],"heterophily":[103],"separation":[104],"module":[105],"divide":[107],"into":[110],"subgraphs,":[114],"mitigating":[115],"low-pass":[117],"traditional":[121],"It":[123],"then":[124],"applies":[125],"shared":[126],"weights":[127],"capture":[129],"signals":[130,153,175],"at":[131,187],"different":[132,177],"frequencies":[133,178],"independently":[134],"incorporates":[136],"customized":[138],"sampling":[139],"strategy":[140],"training.":[142],"This":[143],"allows":[144],"nodes":[145],"adaptively":[147],"balance":[148],"contributions":[150],"based":[154],"on":[155,160],"labels.":[157],"Extensive":[158],"experiments":[159],"three":[161],"real-world":[162],"datasets":[163],"demonstrate":[164],"that":[165],"outperforms":[167],"methods,":[169],"highlighting":[170],"importance":[172],"separating":[174],"improved":[180],"The":[183],"code":[184],"is":[185],"available":[186],"https://github.com/shaieesss/DHMP.":[188]},"counts_by_year":[{"year":2026,"cited_by_count":3}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-11-14T00:00:00"}
