{"id":"https://openalex.org/W4415540995","doi":"https://doi.org/10.1145/3746027.3755334","title":"GTHNA: Local-global Graph Transformer with Memory Reconstruction for Holistic Node Anomaly Evaluation","display_name":"GTHNA: Local-global Graph Transformer with Memory Reconstruction for Holistic Node Anomaly Evaluation","publication_year":2025,"publication_date":"2025-10-25","ids":{"openalex":"https://openalex.org/W4415540995","doi":"https://doi.org/10.1145/3746027.3755334"},"language":null,"primary_location":{"id":"doi:10.1145/3746027.3755334","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3746027.3755334","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 33rd ACM International Conference on Multimedia","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/A5016499067","display_name":"Mingkang Li","orcid":"https://orcid.org/0009-0005-1099-1914"},"institutions":[{"id":"https://openalex.org/I37461747","display_name":"Wuhan University","ror":"https://ror.org/033vjfk17","country_code":"CN","type":"education","lineage":["https://openalex.org/I37461747"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Mingkang Li","raw_affiliation_strings":["School of Computer Science, Wuhan University, Wuhan, Hubei, China"],"raw_orcid":"https://orcid.org/0009-0005-1099-1914","affiliations":[{"raw_affiliation_string":"School of Computer Science, Wuhan University, Wuhan, Hubei, China","institution_ids":["https://openalex.org/I37461747"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5017536603","display_name":"Xuexiong Luo","orcid":"https://orcid.org/0000-0003-3400-4061"},"institutions":[{"id":"https://openalex.org/I99043593","display_name":"Macquarie University","ror":"https://ror.org/01sf06y89","country_code":"AU","type":"education","lineage":["https://openalex.org/I99043593"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Xuexiong Luo","raw_affiliation_strings":["School of Computing, Macquarie University, Sydney, NSW, Australia"],"raw_orcid":"https://orcid.org/0000-0003-3400-4061","affiliations":[{"raw_affiliation_string":"School of Computing, Macquarie University, Sydney, NSW, Australia","institution_ids":["https://openalex.org/I99043593"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5088052182","display_name":"Y. Z. Zhang","orcid":"https://orcid.org/0009-0008-6101-260X"},"institutions":[{"id":"https://openalex.org/I37461747","display_name":"Wuhan University","ror":"https://ror.org/033vjfk17","country_code":"CN","type":"education","lineage":["https://openalex.org/I37461747"]},{"id":"https://openalex.org/I4210111616","display_name":"Wuhan Business University","ror":"https://ror.org/0282ggx30","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210111616"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yue Zhang","raw_affiliation_strings":["School of Cyber Science and Engineering, Wuhan University, WuHan, Hubei, China"],"raw_orcid":"https://orcid.org/0009-0008-6101-260X","affiliations":[{"raw_affiliation_string":"School of Cyber Science and Engineering, Wuhan University, WuHan, Hubei, China","institution_ids":["https://openalex.org/I37461747","https://openalex.org/I4210111616"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5088191346","display_name":"Y.H. Li","orcid":null},"institutions":[{"id":"https://openalex.org/I37461747","display_name":"Wuhan University","ror":"https://ror.org/033vjfk17","country_code":"CN","type":"education","lineage":["https://openalex.org/I37461747"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yaoyang Li","raw_affiliation_strings":["Wuhan University, Wuhan, Hubei, China"],"raw_orcid":"https://orcid.org/0009-0009-0535-4928","affiliations":[{"raw_affiliation_string":"Wuhan University, Wuhan, Hubei, China","institution_ids":["https://openalex.org/I37461747"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5102858012","display_name":"Fu Lin","orcid":"https://orcid.org/0009-0002-3749-4043"},"institutions":[{"id":"https://openalex.org/I37461747","display_name":"Wuhan University","ror":"https://ror.org/033vjfk17","country_code":"CN","type":"education","lineage":["https://openalex.org/I37461747"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Fu Lin","raw_affiliation_strings":["School of Computer Science, Wuhan University, Wuhan, Hubei, China"],"raw_orcid":"https://orcid.org/0009-0002-3749-4043","affiliations":[{"raw_affiliation_string":"School of Computer Science, Wuhan University, Wuhan, Hubei, China","institution_ids":["https://openalex.org/I37461747"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"6540","last_page":"6548"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11273","display_name":"Advanced Graph Neural Networks","score":0.9997000098228455,"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":0.9997000098228455,"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":0.9957000017166138,"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":0.9923999905586243,"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/anomaly-detection","display_name":"Anomaly detection","score":0.7494000196456909},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.536300003528595},{"id":"https://openalex.org/keywords/transformer","display_name":"Transformer","score":0.4729999899864197},{"id":"https://openalex.org/keywords/anomaly","display_name":"Anomaly (physics)","score":0.4494999945163727},{"id":"https://openalex.org/keywords/matching","display_name":"Matching (statistics)","score":0.43299999833106995},{"id":"https://openalex.org/keywords/node","display_name":"Node (physics)","score":0.42340001463890076},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.4047999978065491},{"id":"https://openalex.org/keywords/outlier","display_name":"Outlier","score":0.38179999589920044}],"concepts":[{"id":"https://openalex.org/C739882","wikidata":"https://www.wikidata.org/wiki/Q3560506","display_name":"Anomaly detection","level":2,"score":0.7494000196456909},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7177000045776367},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.536300003528595},{"id":"https://openalex.org/C66322947","wikidata":"https://www.wikidata.org/wiki/Q11658","display_name":"Transformer","level":3,"score":0.4729999899864197},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.46230000257492065},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4593000113964081},{"id":"https://openalex.org/C12997251","wikidata":"https://www.wikidata.org/wiki/Q567560","display_name":"Anomaly (physics)","level":2,"score":0.4494999945163727},{"id":"https://openalex.org/C165064840","wikidata":"https://www.wikidata.org/wiki/Q1321061","display_name":"Matching (statistics)","level":2,"score":0.43299999833106995},{"id":"https://openalex.org/C62611344","wikidata":"https://www.wikidata.org/wiki/Q1062658","display_name":"Node (physics)","level":2,"score":0.42340001463890076},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.4047999978065491},{"id":"https://openalex.org/C79337645","wikidata":"https://www.wikidata.org/wiki/Q779824","display_name":"Outlier","level":2,"score":0.38179999589920044},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.34209999442100525},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.3294999897480011},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.32690000534057617},{"id":"https://openalex.org/C116834253","wikidata":"https://www.wikidata.org/wiki/Q2039217","display_name":"Identification (biology)","level":2,"score":0.3197000026702881},{"id":"https://openalex.org/C116409475","wikidata":"https://www.wikidata.org/wiki/Q1385056","display_name":"External Data Representation","level":2,"score":0.2978000044822693},{"id":"https://openalex.org/C106937863","wikidata":"https://www.wikidata.org/wiki/Q7236518","display_name":"Power graph analysis","level":3,"score":0.2971000075340271},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.2971000075340271},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.28450000286102295},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.26919999718666077},{"id":"https://openalex.org/C68859911","wikidata":"https://www.wikidata.org/wiki/Q1503724","display_name":"Pattern matching","level":2,"score":0.2572999894618988},{"id":"https://openalex.org/C160920958","wikidata":"https://www.wikidata.org/wiki/Q7662746","display_name":"Synthetic data","level":2,"score":0.2556000053882599}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3746027.3755334","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3746027.3755334","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 33rd ACM International Conference on Multimedia","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":19,"referenced_works":["https://openalex.org/W2022322548","https://openalex.org/W2046253692","https://openalex.org/W2064058256","https://openalex.org/W2153959628","https://openalex.org/W2782836818","https://openalex.org/W2965683718","https://openalex.org/W2998496395","https://openalex.org/W3035240825","https://openalex.org/W3093649180","https://openalex.org/W3133518153","https://openalex.org/W3195107722","https://openalex.org/W3206326321","https://openalex.org/W3206604724","https://openalex.org/W4221095033","https://openalex.org/W4282980030","https://openalex.org/W4319762935","https://openalex.org/W4382239148","https://openalex.org/W4393159601","https://openalex.org/W4403582646"],"related_works":[],"abstract_inverted_index":{"Anomaly":[0,135],"detection":[1,175],"in":[2,23,148],"graph-structured":[3],"data":[4],"is":[5],"an":[6],"inherently":[7],"challenging":[8],"problem,":[9],"as":[10,33],"it":[11],"requires":[12],"the":[13,21,47,65,110,122],"identification":[14],"of":[15,124,133],"rare":[16],"nodes":[17],"that":[18,85,160],"deviate":[19],"from":[20,43,130],"majority":[22],"both":[24,115],"their":[25],"structural":[26,119],"and":[27,80,98,117,127,143,170],"behavioral":[28],"characteristics.":[29],"Existing":[30],"methods,":[31],"such":[32],"those":[34],"based":[35],"on":[36,155],"graph":[37,55,178],"convolutional":[38],"networks":[39],"(GCNs),":[40],"often":[41],"suffer":[42],"over-smoothing,":[44],"which":[45],"causes":[46],"learned":[48],"node":[49,62],"representations":[50],"to":[51,60,69,108,113],"become":[52],"indistinguishable.":[53],"Furthermore,":[54],"reconstruction-based":[56],"approaches":[57],"are":[58,137],"vulnerable":[59],"anomalous":[61,125],"interference":[63],"during":[64],"reconstruction":[66,96,141],"process,":[67],"leading":[68],"inaccurate":[70],"anomaly":[71,82,174],"detection.":[72],"In":[73],"this":[74],"work,":[75],"we":[76],"propose":[77],"a":[78,90,94,99,149,168],"novel":[79],"holistic":[81],"evaluation":[83],"framework":[84],"integrates":[86],"three":[87],"key":[88],"components:":[89],"local-global":[91],"Transformer":[92],"encoder,":[93],"memory-guided":[95],"mechanism":[97],"multi-scale":[100],"representation":[101],"matching":[102,145],"strategy.":[103],"These":[104],"components":[105],"work":[106],"synergistically":[107],"enhance":[109],"model's":[111],"ability":[112],"capture":[114],"local":[116],"global":[118],"dependencies,":[120],"suppress":[121],"influence":[123],"nodes,":[126],"assess":[128],"anomalies":[129],"multiple":[131],"levels":[132],"granularity.":[134],"scores":[136],"computed":[138],"by":[139],"combining":[140],"errors":[142],"memory":[144],"signals,":[146],"resulting":[147],"more":[150],"robust":[151],"evaluation.":[152],"Extensive":[153],"experiments":[154],"seven":[156],"benchmark":[157],"datasets":[158],"demonstrate":[159],"our":[161],"method":[162],"outperforms":[163],"existing":[164],"state-of-the-art":[165],"approaches,":[166],"offering":[167],"comprehensive":[169],"generalizable":[171],"solution":[172],"for":[173],"across":[176],"various":[177],"domains.":[179]},"counts_by_year":[],"updated_date":"2026-07-22T07:51:19.307946","created_date":"2025-10-25T00:00:00"}