{"id":"https://openalex.org/W4414404694","doi":"https://doi.org/10.1109/tnnls.2025.3601449","title":"Contrastive Federated Learning for Graph Anomaly Detection","display_name":"Contrastive Federated Learning for Graph Anomaly Detection","publication_year":2025,"publication_date":"2025-09-22","ids":{"openalex":"https://openalex.org/W4414404694","doi":"https://doi.org/10.1109/tnnls.2025.3601449","pmid":"https://pubmed.ncbi.nlm.nih.gov/40982513"},"language":"en","primary_location":{"id":"doi:10.1109/tnnls.2025.3601449","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tnnls.2025.3601449","pdf_url":null,"source":{"id":"https://openalex.org/S4210175523","display_name":"IEEE Transactions on Neural Networks and Learning Systems","issn_l":"2162-237X","issn":["2162-237X","2162-2388"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Neural Networks and Learning Systems","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","pubmed"],"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/A5059145238","display_name":"Hui Fang","orcid":"https://orcid.org/0000-0001-8263-1372"},"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":"Hui Fang","raw_affiliation_strings":["Zhejiang Key Laboratory of Accessible Perception and Intelligent Systems, Zhejiang University, Hangzhou, China"],"raw_orcid":"https://orcid.org/0000-0001-8263-1372","affiliations":[{"raw_affiliation_string":"Zhejiang Key Laboratory of Accessible Perception and Intelligent Systems, Zhejiang University, Hangzhou, China","institution_ids":["https://openalex.org/I76130692"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5042278585","display_name":"Yang Gao","orcid":"https://orcid.org/0000-0001-9930-137X"},"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":"Yang Gao","raw_affiliation_strings":["Zhejiang Key Laboratory of Accessible Perception and Intelligent Systems, Zhejiang University, Hangzhou, China"],"raw_orcid":"https://orcid.org/0000-0001-9930-137X","affiliations":[{"raw_affiliation_string":"Zhejiang Key Laboratory of Accessible Perception and Intelligent Systems, Zhejiang University, Hangzhou, China","institution_ids":["https://openalex.org/I76130692"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100364041","display_name":"Peng Zhang","orcid":"https://orcid.org/0000-0001-7973-2746"},"institutions":[{"id":"https://openalex.org/I37987034","display_name":"Guangzhou University","ror":"https://ror.org/05ar8rn06","country_code":"CN","type":"education","lineage":["https://openalex.org/I37987034"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Peng Zhang","raw_affiliation_strings":["Cyberspace Institute of Advanced Technology, Guangzhou University, Guangzhou, China"],"raw_orcid":"https://orcid.org/0000-0001-7973-2746","affiliations":[{"raw_affiliation_string":"Cyberspace Institute of Advanced Technology, Guangzhou University, Guangzhou, China","institution_ids":["https://openalex.org/I37987034"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5102754272","display_name":"Sheng Zhou","orcid":"https://orcid.org/0000-0003-3645-1041"},"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":"Sheng Zhou","raw_affiliation_strings":["Zhejiang Key Laboratory of Accessible Perception and Intelligent Systems, Zhejiang University, Hangzhou, China"],"raw_orcid":"https://orcid.org/0000-0003-3645-1041","affiliations":[{"raw_affiliation_string":"Zhejiang Key Laboratory of Accessible Perception and Intelligent Systems, Zhejiang University, Hangzhou, China","institution_ids":["https://openalex.org/I76130692"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5008473103","display_name":"Hongyang Chen","orcid":"https://orcid.org/0000-0002-7626-0162"},"institutions":[{"id":"https://openalex.org/I4210123185","display_name":"Zhejiang Lab","ror":"https://ror.org/02m2h7991","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4210123185"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Hongyang Chen","raw_affiliation_strings":["Zhejiang Laboratory, Hangzhou, China"],"raw_orcid":"https://orcid.org/0000-0002-7626-0162","affiliations":[{"raw_affiliation_string":"Zhejiang Laboratory, Hangzhou, China","institution_ids":["https://openalex.org/I4210123185"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5052757755","display_name":"Jiajun Bu","orcid":"https://orcid.org/0000-0002-1097-2044"},"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":"Jiajun Bu","raw_affiliation_strings":["Zhejiang Key Laboratory of Accessible Perception and Intelligent Systems, Zhejiang University, Hangzhou, China"],"raw_orcid":"https://orcid.org/0000-0002-1097-2044","affiliations":[{"raw_affiliation_string":"Zhejiang Key Laboratory of Accessible Perception and Intelligent Systems, Zhejiang University, Hangzhou, China","institution_ids":["https://openalex.org/I76130692"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5047118636","display_name":"Haishuai Wang","orcid":"https://orcid.org/0000-0003-1617-0920"},"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":"Haishuai Wang","raw_affiliation_strings":["Zhejiang Key Laboratory of Accessible Perception and Intelligent Systems, Zhejiang University, Hangzhou, China"],"raw_orcid":"https://orcid.org/0000-0003-1617-0920","affiliations":[{"raw_affiliation_string":"Zhejiang Key Laboratory of Accessible Perception and Intelligent Systems, Zhejiang University, Hangzhou, China","institution_ids":["https://openalex.org/I76130692"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":1.396,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.86051345,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":97},"biblio":{"volume":"37","issue":"1","first_page":"136","last_page":"147"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11273","display_name":"Advanced Graph Neural Networks","score":0.9987000226974487,"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":0.9987000226974487,"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":0.9886000156402588,"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"}},{"id":"https://openalex.org/T10400","display_name":"Network Security and Intrusion Detection","score":0.9866999983787537,"subfield":{"id":"https://openalex.org/subfields/1705","display_name":"Computer Networks and Communications"},"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/anomaly-detection","display_name":"Anomaly detection","score":0.6962000131607056},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.6455000042915344},{"id":"https://openalex.org/keywords/node","display_name":"Node (physics)","score":0.5375000238418579},{"id":"https://openalex.org/keywords/feature-learning","display_name":"Feature learning","score":0.5095000267028809},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.5006999969482422},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.3995000123977661},{"id":"https://openalex.org/keywords/data-modeling","display_name":"Data modeling","score":0.38199999928474426},{"id":"https://openalex.org/keywords/external-data-representation","display_name":"External Data Representation","score":0.34929999709129333}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7860000133514404},{"id":"https://openalex.org/C739882","wikidata":"https://www.wikidata.org/wiki/Q3560506","display_name":"Anomaly detection","level":2,"score":0.6962000131607056},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.6455000042915344},{"id":"https://openalex.org/C62611344","wikidata":"https://www.wikidata.org/wiki/Q1062658","display_name":"Node (physics)","level":2,"score":0.5375000238418579},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.5095000267028809},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.5006999969482422},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4584999978542328},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.39959999918937683},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.3995000123977661},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.38199999928474426},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.37770000100135803},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.37439998984336853},{"id":"https://openalex.org/C116409475","wikidata":"https://www.wikidata.org/wiki/Q1385056","display_name":"External Data Representation","level":2,"score":0.34929999709129333},{"id":"https://openalex.org/C123201435","wikidata":"https://www.wikidata.org/wiki/Q456632","display_name":"Information privacy","level":2,"score":0.31049999594688416},{"id":"https://openalex.org/C12725497","wikidata":"https://www.wikidata.org/wiki/Q810247","display_name":"Baseline (sea)","level":2,"score":0.29339998960494995},{"id":"https://openalex.org/C12997251","wikidata":"https://www.wikidata.org/wiki/Q567560","display_name":"Anomaly (physics)","level":2,"score":0.28519999980926514},{"id":"https://openalex.org/C70061542","wikidata":"https://www.wikidata.org/wiki/Q989016","display_name":"Distributed database","level":2,"score":0.2809000015258789},{"id":"https://openalex.org/C2992525071","wikidata":"https://www.wikidata.org/wiki/Q50818671","display_name":"Federated learning","level":2,"score":0.2777000069618225},{"id":"https://openalex.org/C8038995","wikidata":"https://www.wikidata.org/wiki/Q1152135","display_name":"Unsupervised learning","level":2,"score":0.27649998664855957},{"id":"https://openalex.org/C66746571","wikidata":"https://www.wikidata.org/wiki/Q1134833","display_name":"ENCODE","level":3,"score":0.2736999988555908},{"id":"https://openalex.org/C2776145971","wikidata":"https://www.wikidata.org/wiki/Q30673951","display_name":"Labeled data","level":2,"score":0.26409998536109924},{"id":"https://openalex.org/C2987255567","wikidata":"https://www.wikidata.org/wiki/Q33002955","display_name":"Knowledge graph","level":2,"score":0.2574999928474426}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/tnnls.2025.3601449","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tnnls.2025.3601449","pdf_url":null,"source":{"id":"https://openalex.org/S4210175523","display_name":"IEEE Transactions on Neural Networks and Learning Systems","issn_l":"2162-237X","issn":["2162-237X","2162-2388"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Neural Networks and Learning Systems","raw_type":"journal-article"},{"id":"pmid:40982513","is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/40982513","pdf_url":null,"source":{"id":"https://openalex.org/S4306525036","display_name":"PubMed","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE transactions on neural networks and learning systems","raw_type":null}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G1122440035","display_name":null,"funder_award_id":"62372408","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G4869551441","display_name":null,"funder_award_id":"62202422","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G6120437032","display_name":null,"funder_award_id":"62376064","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"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":34,"referenced_works":["https://openalex.org/W2142889610","https://openalex.org/W2172852798","https://openalex.org/W2741048099","https://openalex.org/W2741114205","https://openalex.org/W2808544127","https://openalex.org/W2886462128","https://openalex.org/W2888026659","https://openalex.org/W2944250323","https://openalex.org/W2982426954","https://openalex.org/W2994688391","https://openalex.org/W3011573535","https://openalex.org/W3044515030","https://openalex.org/W3048072497","https://openalex.org/W3126230197","https://openalex.org/W3132522414","https://openalex.org/W3133518153","https://openalex.org/W3193988822","https://openalex.org/W3206604724","https://openalex.org/W3210350882","https://openalex.org/W4285066127","https://openalex.org/W4285222884","https://openalex.org/W4285606214","https://openalex.org/W4311080353","https://openalex.org/W4318823481","https://openalex.org/W4319338650","https://openalex.org/W4379927591","https://openalex.org/W4382239148","https://openalex.org/W4401024161","https://openalex.org/W4403791819","https://openalex.org/W4407196210","https://openalex.org/W4409157816","https://openalex.org/W4409364245","https://openalex.org/W4414359147","https://openalex.org/W7084138284"],"related_works":[],"abstract_inverted_index":{"Graph":[0],"anomaly":[1,65],"detection":[2,66],"(GAD)":[3],"refers":[4],"to":[5,81,93,118,143],"identifying":[6],"abnormal":[7],"graph":[8,64,145],"nodes":[9,117],"or":[10],"edges":[11],"that":[12,115,150],"heavily":[13],"deviate":[14],"from":[15,22,124],"normal":[16],"observations.":[17],"Existing":[18],"approaches":[19],"inevitably":[20],"suffer":[21],"the":[23,82,90,95,101,148,167],"influence":[24],"of":[25,48,97,170,179],"imbalanced":[26,157],"data":[27,75,102,158],"and":[28,39,88,139],"privacy":[29],"protection.":[30],"This":[31],"shortcoming":[32],"poses":[33],"challenges":[34],"in":[35,45,147],"optimizing":[36],"node":[37,98,112],"embeddings":[38],"detecting":[40],"multitype":[41],"anomalies":[42,146],"simultaneously,":[43],"resulting":[44],"decreased":[46],"accuracy":[47],"existing":[49],"GAD":[50,181],"models.":[51],"To":[52],"address":[53],"this":[54],"shortcoming,":[55],"we":[56,107,128],"introduce":[57],"a":[58,110,130],"new":[59],"federated":[60],"learning":[61,72,133,141],"model":[62],"for":[63],"(FedGAD).":[67],"FedGAD":[68,86,171],"enables":[69,116],"collaborative":[70],"unsupervised":[71],"among":[73],"decentralized":[74],"centers":[76],"without":[77],"requiring":[78],"direct":[79],"access":[80],"distributed":[83,105],"subgraphs.":[84],"Specifically,":[85],"masks":[87],"reconstructs":[89],"neighborhood":[91],"features":[92],"enhance":[94],"knowledge":[96],"representations.":[99],"Considering":[100],"diversity":[103],"across":[104],"clients,":[106],"also":[108],"design":[109],"cross-clients'":[111],"representation":[113],"module":[114],"reconstruct":[119],"neighbors":[120],"by":[121],"leveraging":[122],"information":[123],"other":[125],"clients.":[126],"Furthermore,":[127],"use":[129],"multiscale":[131],"contrastive":[132],"function,":[134],"which":[135],"includes":[136],"both":[137],"structure-level":[138],"contextual-level":[140],"functions,":[142],"detect":[144],"condition":[149],"subgraphs":[151],"located":[152],"at":[153],"different":[154],"clients":[155],"show":[156],"distributions.":[159],"Experimental":[160],"results":[161],"on":[162],"seven":[163],"benchmark":[164],"datasets":[165],"demonstrate":[166],"superior":[168],"performance":[169],"compared":[172],"with":[173],"baseline":[174],"methods,":[175],"verifying":[176],"its":[177],"capability":[178],"improving":[180],"performance.":[182]},"counts_by_year":[{"year":2026,"cited_by_count":1}],"updated_date":"2026-03-27T05:58:40.876381","created_date":"2025-10-10T00:00:00"}
