{"id":"https://openalex.org/W4415933720","doi":"https://doi.org/10.1109/tnse.2025.3629084","title":"Adaptive Graph Filtering Neural Network for Graph Anomaly Detection","display_name":"Adaptive Graph Filtering Neural Network for Graph Anomaly Detection","publication_year":2025,"publication_date":"2025-11-05","ids":{"openalex":"https://openalex.org/W4415933720","doi":"https://doi.org/10.1109/tnse.2025.3629084"},"language":null,"primary_location":{"id":"doi:10.1109/tnse.2025.3629084","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tnse.2025.3629084","pdf_url":null,"source":{"id":"https://openalex.org/S2484352698","display_name":"IEEE Transactions on Network Science and Engineering","issn_l":"2327-4697","issn":["2327-4697","2334-329X"],"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 Network Science and Engineering","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/A5078959651","display_name":"Zhizhe Liu","orcid":"https://orcid.org/0000-0001-7571-4161"},"institutions":[{"id":"https://openalex.org/I78675632","display_name":"Beijing Information Science & Technology University","ror":"https://ror.org/04xnqep60","country_code":"CN","type":"education","lineage":["https://openalex.org/I78675632"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhizhe Liu","raw_affiliation_strings":["Center for Target Cognition Information Processing Science and Technology, Beijing Information Science and Technology University, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0001-7571-4161","affiliations":[{"raw_affiliation_string":"Center for Target Cognition Information Processing Science and Technology, Beijing Information Science and Technology University, Beijing, China","institution_ids":["https://openalex.org/I78675632"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Shuai Zheng","orcid":"https://orcid.org/0000-0001-8560-8135"},"institutions":[{"id":"https://openalex.org/I21193070","display_name":"Beijing Jiaotong University","ror":"https://ror.org/01yj56c84","country_code":"CN","type":"education","lineage":["https://openalex.org/I21193070"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Shuai Zheng","raw_affiliation_strings":["Institute of Information Science, Beijing Jiaotong University, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0001-8560-8135","affiliations":[{"raw_affiliation_string":"Institute of Information Science, Beijing Jiaotong University, Beijing, China","institution_ids":["https://openalex.org/I21193070"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5017288103","display_name":"Yeyu Yan","orcid":"https://orcid.org/0000-0002-6288-453X"},"institutions":[{"id":"https://openalex.org/I21193070","display_name":"Beijing Jiaotong University","ror":"https://ror.org/01yj56c84","country_code":"CN","type":"education","lineage":["https://openalex.org/I21193070"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yeyu Yan","raw_affiliation_strings":["Institute of Information Science, Beijing Jiaotong University, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Institute of Information Science, Beijing Jiaotong University, Beijing, China","institution_ids":["https://openalex.org/I21193070"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101867605","display_name":"Zhenfeng Zhu","orcid":"https://orcid.org/0000-0001-7315-3276"},"institutions":[{"id":"https://openalex.org/I21193070","display_name":"Beijing Jiaotong University","ror":"https://ror.org/01yj56c84","country_code":"CN","type":"education","lineage":["https://openalex.org/I21193070"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhenfeng Zhu","raw_affiliation_strings":["Institute of Information Science, Beijing Jiaotong University, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0001-7315-3276","affiliations":[{"raw_affiliation_string":"Institute of Information Science, Beijing Jiaotong University, Beijing, China","institution_ids":["https://openalex.org/I21193070"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100362745","display_name":"Yao Zhao","orcid":"https://orcid.org/0000-0002-8581-9554"},"institutions":[{"id":"https://openalex.org/I21193070","display_name":"Beijing Jiaotong University","ror":"https://ror.org/01yj56c84","country_code":"CN","type":"education","lineage":["https://openalex.org/I21193070"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yao Zhao","raw_affiliation_strings":["Institute of Information Science, Beijing Jiaotong University, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0002-8581-9554","affiliations":[{"raw_affiliation_string":"Institute of Information Science, Beijing Jiaotong University, Beijing, China","institution_ids":["https://openalex.org/I21193070"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":2.792,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.91996147,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":97,"max":98},"biblio":{"volume":"13","issue":null,"first_page":"3274","last_page":"3284"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11273","display_name":"Advanced Graph Neural Networks","score":0.9563000202178955,"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":0.9563000202178955,"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/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.007600000128149986,"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/T12026","display_name":"Explainable Artificial Intelligence (XAI)","score":0.0027000000700354576,"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/discriminative-model","display_name":"Discriminative model","score":0.733299970626831},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.6195999979972839},{"id":"https://openalex.org/keywords/filter","display_name":"Filter (signal processing)","score":0.5437999963760376},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5184999704360962},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.5047000050544739},{"id":"https://openalex.org/keywords/adaptive-filter","display_name":"Adaptive filter","score":0.435699999332428},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.43160000443458557},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.41429999470710754}],"concepts":[{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.733299970626831},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7287999987602234},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.6195999979972839},{"id":"https://openalex.org/C106131492","wikidata":"https://www.wikidata.org/wiki/Q3072260","display_name":"Filter (signal processing)","level":2,"score":0.5437999963760376},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5388000011444092},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5184999704360962},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5047000050544739},{"id":"https://openalex.org/C102248274","wikidata":"https://www.wikidata.org/wiki/Q168388","display_name":"Adaptive filter","level":2,"score":0.435699999332428},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.43160000443458557},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.41429999470710754},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.40869998931884766},{"id":"https://openalex.org/C739882","wikidata":"https://www.wikidata.org/wiki/Q3560506","display_name":"Anomaly detection","level":2,"score":0.4032999873161316},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3675999939441681},{"id":"https://openalex.org/C62611344","wikidata":"https://www.wikidata.org/wiki/Q1062658","display_name":"Node (physics)","level":2,"score":0.3328000009059906},{"id":"https://openalex.org/C204323151","wikidata":"https://www.wikidata.org/wiki/Q905424","display_name":"Range (aeronautics)","level":2,"score":0.289900004863739},{"id":"https://openalex.org/C83665646","wikidata":"https://www.wikidata.org/wiki/Q42139305","display_name":"Feature vector","level":2,"score":0.28870001435279846},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.28690001368522644},{"id":"https://openalex.org/C2776760102","wikidata":"https://www.wikidata.org/wiki/Q5139990","display_name":"Code (set theory)","level":3,"score":0.2799000144004822},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.26570001244544983},{"id":"https://openalex.org/C52970973","wikidata":"https://www.wikidata.org/wiki/Q2497134","display_name":"Adaptive system","level":2,"score":0.2556000053882599},{"id":"https://openalex.org/C48903430","wikidata":"https://www.wikidata.org/wiki/Q491370","display_name":"Graph partition","level":3,"score":0.25529998540878296}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tnse.2025.3629084","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tnse.2025.3629084","pdf_url":null,"source":{"id":"https://openalex.org/S2484352698","display_name":"IEEE Transactions on Network Science and Engineering","issn_l":"2327-4697","issn":["2327-4697","2334-329X"],"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 Network Science and Engineering","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G3442257106","display_name":null,"funder_award_id":"2024XKRC088","funder_id":"https://openalex.org/F4320335787","funder_display_name":"Fundamental Research Funds for the Central Universities"},{"id":"https://openalex.org/G4129294218","display_name":null,"funder_award_id":"4254085","funder_id":"https://openalex.org/F4320322919","funder_display_name":"Natural Science Foundation of Beijing Municipality"},{"id":"https://openalex.org/G602264051","display_name":null,"funder_award_id":"202504841007","funder_id":"https://openalex.org/F4320334978","funder_display_name":"Beijing Nova Program"},{"id":"https://openalex.org/G6640703287","display_name":null,"funder_award_id":"62476022","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G7413645937","display_name":null,"funder_award_id":"L242047","funder_id":"https://openalex.org/F4320322919","funder_display_name":"Natural Science Foundation of Beijing Municipality"},{"id":"https://openalex.org/G7716759923","display_name":null,"funder_award_id":"62506029","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G8829959983","display_name":null,"funder_award_id":"K24XKRC00030","funder_id":"https://openalex.org/F4320335787","funder_display_name":"Fundamental Research Funds for the Central Universities"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320322919","display_name":"Natural Science Foundation of Beijing Municipality","ror":null},{"id":"https://openalex.org/F4320334978","display_name":"Beijing Nova Program","ror":"https://ror.org/034k14f91"},{"id":"https://openalex.org/F4320335787","display_name":"Fundamental Research Funds for the Central Universities","ror":null}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":53,"referenced_works":["https://openalex.org/W1492581097","https://openalex.org/W2012496675","https://openalex.org/W2064058256","https://openalex.org/W2073330885","https://openalex.org/W2101491865","https://openalex.org/W2108898793","https://openalex.org/W2136891251","https://openalex.org/W2321990803","https://openalex.org/W2348679751","https://openalex.org/W2907492528","https://openalex.org/W2944250323","https://openalex.org/W2965683718","https://openalex.org/W2970127247","https://openalex.org/W2984840400","https://openalex.org/W3009901425","https://openalex.org/W3022945404","https://openalex.org/W3034531178","https://openalex.org/W3068123808","https://openalex.org/W3093649180","https://openalex.org/W3128443161","https://openalex.org/W3133518153","https://openalex.org/W3153858161","https://openalex.org/W3169637772","https://openalex.org/W3203701711","https://openalex.org/W3206326321","https://openalex.org/W3211258672","https://openalex.org/W4221095033","https://openalex.org/W4224310669","https://openalex.org/W4226212070","https://openalex.org/W4280545892","https://openalex.org/W4293704747","https://openalex.org/W4312957143","https://openalex.org/W4375928989","https://openalex.org/W4379927591","https://openalex.org/W4382239148","https://openalex.org/W4386918771","https://openalex.org/W4387681995","https://openalex.org/W4387968290","https://openalex.org/W4393153310","https://openalex.org/W4393159601","https://openalex.org/W4402655210","https://openalex.org/W4403577818","https://openalex.org/W4403582577","https://openalex.org/W4405179773","https://openalex.org/W4405179813","https://openalex.org/W4405488412","https://openalex.org/W4406598481","https://openalex.org/W4409218275","https://openalex.org/W4409365221","https://openalex.org/W4409581319","https://openalex.org/W4410774772","https://openalex.org/W4410852754","https://openalex.org/W4414043722"],"related_works":[],"abstract_inverted_index":{"Graph":[0],"anomaly":[1],"detection":[2],"(GAD)":[3],"plays":[4],"an":[5],"important":[6],"role":[7],"in":[8,23,45,116],"improving":[9,82],"public":[10],"safety":[11],"and":[12,15,50,78,89,125],"product":[13],"quality":[14],"has":[16,32],"attracted":[17],"a":[18,27,64,110,117,165],"great":[19],"deal":[20],"of":[21,30,98,131],"interest":[22],"recent":[24],"years.":[25],"Although":[26],"wide":[28],"range":[29],"progress":[31],"been":[33],"achieved":[34],"recently,":[35],"the":[36,46,87,95,99,129,142,146,181],"following":[37],"challenges":[38],"still":[39],"remain:":[40],"(1)":[41],"abnormal":[42],"nodes":[43],"mixed":[44,75],"normal":[47],"node":[48,88,100],"subgraph":[49],"(2)":[51],"global-consistency":[52],"filtering":[53,68,105,124],"to":[54,72,93,108,139,163],"different":[55,132],"features.":[56],"To":[57],"overcome":[58],"these":[59],"challenges,":[60],"we":[61],"propose":[62],"AGFNN,":[63],"novel":[65],"adaptive":[66,104],"graph":[67,175],"neural":[69],"network":[70],"designed":[71],"handle":[73],"diverse":[74],"local":[76,144],"patterns":[77],"feature":[79,90,103,115],"variations,":[80],"thereby":[81],"model":[83],"fitting":[84],"from":[85],"both":[86],"perspectives.":[91],"Specifically,":[92],"enhance":[94],"discriminative":[96],"capacity":[97],"representation,":[101],"channel-wise":[102],"is":[106,160],"proposed":[107],"learn":[109],"specific":[111,166],"filter":[112,167],"for":[113,135,149,168],"each":[114,136,169],"progressive":[118],"way,":[119],"which":[120,159],"first":[121],"performs":[122],"multi-frequency":[123,150],"then":[126],"adaptively":[127],"captures":[128],"importance":[130],"frequency":[133],"components":[134],"feature.":[137],"Meanwhile,":[138],"better":[140],"fit":[141],"complex":[143],"subgraphs,":[145],"node's":[147],"preference":[148],"information":[151],"can":[152],"be":[153,187],"self-adjusted":[154],"by":[155],"learning":[156,164],"node-aware":[157],"bias,":[158],"also":[161],"equal":[162],"node.":[170],"Extensive":[171],"experiments":[172],"on":[173],"real-world":[174],"datasets":[176],"demonstrate":[177],"that":[178],"AGFNN":[179],"outperforms":[180],"state-of-the-art":[182],"methods.":[183],"The":[184],"code":[185],"will":[186],"available":[188],"at":[189],"<uri":[190],"xmlns:mml=\"http://www.w3.org/1998/Math/MathML\"":[191],"xmlns:xlink=\"http://www.w3.org/1999/xlink\">https://github.com/TFboys-lzz/AGFNN</uri>.":[192]},"counts_by_year":[{"year":2026,"cited_by_count":2}],"updated_date":"2025-12-12T23:16:27.785689","created_date":"2025-11-05T00:00:00"}