{"id":"https://openalex.org/W4408345909","doi":"https://doi.org/10.1109/icassp49660.2025.10888224","title":"Graph Anomaly Detection via Multi-Scale Reconstruction of Graph Encoder-Decoder Networks","display_name":"Graph Anomaly Detection via Multi-Scale Reconstruction of Graph Encoder-Decoder Networks","publication_year":2025,"publication_date":"2025-03-12","ids":{"openalex":"https://openalex.org/W4408345909","doi":"https://doi.org/10.1109/icassp49660.2025.10888224"},"language":"en","primary_location":{"id":"doi:10.1109/icassp49660.2025.10888224","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icassp49660.2025.10888224","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ICASSP 2025 - 2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","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/A5115653006","display_name":"Jiaqi Yu","orcid":"https://orcid.org/0000-0002-9953-7873"},"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":"Jiaqi Yu","raw_affiliation_strings":["Guangzhou University,Cyberspace Institute of Advanced Technology,Guangzhou,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Guangzhou University,Cyberspace Institute of Advanced Technology,Guangzhou,China","institution_ids":["https://openalex.org/I37987034"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101950118","display_name":"Hong Yang","orcid":"https://orcid.org/0000-0003-2361-0437"},"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":"Hong Yang","raw_affiliation_strings":["Guangzhou University,Cyberspace Institute of Advanced Technology,Guangzhou,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Guangzhou University,Cyberspace Institute of Advanced Technology,Guangzhou,China","institution_ids":["https://openalex.org/I37987034"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5108116169","display_name":"Peng Zhang","orcid":"https://orcid.org/0009-0004-3607-8560"},"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":["Guangzhou University,Cyberspace Institute of Advanced Technology,Guangzhou,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Guangzhou University,Cyberspace Institute of Advanced Technology,Guangzhou,China","institution_ids":["https://openalex.org/I37987034"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I37987034"],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"5"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11273","display_name":"Advanced Graph Neural Networks","score":0.9866999983787537,"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":0.9866999983787537,"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":0.963100016117096,"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/T11241","display_name":"Advanced Malware Detection Techniques","score":0.9217000007629395,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"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/computer-science","display_name":"Computer science","score":0.7042807340621948},{"id":"https://openalex.org/keywords/anomaly-detection","display_name":"Anomaly detection","score":0.5311086773872375},{"id":"https://openalex.org/keywords/encoder","display_name":"Encoder","score":0.5244441628456116},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.5126098990440369},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.2926369905471802},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.2427574098110199}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7042807340621948},{"id":"https://openalex.org/C739882","wikidata":"https://www.wikidata.org/wiki/Q3560506","display_name":"Anomaly detection","level":2,"score":0.5311086773872375},{"id":"https://openalex.org/C118505674","wikidata":"https://www.wikidata.org/wiki/Q42586063","display_name":"Encoder","level":2,"score":0.5244441628456116},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.5126098990440369},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.2926369905471802},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.2427574098110199},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icassp49660.2025.10888224","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icassp49660.2025.10888224","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ICASSP 2025 - 2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","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":30,"referenced_works":["https://openalex.org/W2022322548","https://openalex.org/W2062769337","https://openalex.org/W2134008243","https://openalex.org/W2153959628","https://openalex.org/W2741114205","https://openalex.org/W2944250323","https://openalex.org/W2962785568","https://openalex.org/W2963893312","https://openalex.org/W2965683718","https://openalex.org/W2998336824","https://openalex.org/W3011667710","https://openalex.org/W3015799890","https://openalex.org/W3093664513","https://openalex.org/W3133518153","https://openalex.org/W3169077988","https://openalex.org/W3206604724","https://openalex.org/W3210350882","https://openalex.org/W4205942400","https://openalex.org/W4281955767","https://openalex.org/W4285066127","https://openalex.org/W4285222884","https://openalex.org/W4319338650","https://openalex.org/W4372260176","https://openalex.org/W4379927591","https://openalex.org/W4382239148","https://openalex.org/W4405304869","https://openalex.org/W6803012065","https://openalex.org/W6846266145","https://openalex.org/W6854026103","https://openalex.org/W6854227290"],"related_works":["https://openalex.org/W4391375266","https://openalex.org/W2899084033","https://openalex.org/W2748952813","https://openalex.org/W2390279801","https://openalex.org/W4391913857","https://openalex.org/W2358668433","https://openalex.org/W4396701345","https://openalex.org/W2376932109","https://openalex.org/W2001405890","https://openalex.org/W4396696052"],"abstract_inverted_index":{"Existing":[0],"unsupervised":[1],"graph":[2,46,91],"anomaly":[3,92,164],"detection":[4,93,165],"(GAD)":[5],"methods":[6,13,23,37],"can":[7],"be":[8],"categorized":[9],"into":[10],"reconstruction":[11,22,32,36,44,75,89,117,141],"based":[12,17,29,52,90],"and":[14,107,126],"contrastive":[15],"learning":[16],"methods.":[18,166],"The":[19],"principle":[20],"of":[21,45,147],"is":[24],"to":[25,40,119,143],"capture":[26,120],"anomalous":[27,80],"nodes":[28],"on":[30,53,155],"data":[31],"errors.":[33],"However,":[34],"existing":[35],"are":[38],"limited":[39],"a":[41,87,100,115,124,138],"single":[42],"scale":[43],"structures,":[47],"typically":[48],"predicting":[49],"edges":[50],"solely":[51],"direct":[54],"connections,":[55],"without":[56],"considering":[57],"the":[58,74,79,121,145],"rich":[59],"contextual":[60],"information":[61],"within":[62],"node":[63,125],"neighborhoods.":[64],"Moreover,":[65],"reconstructing":[66],"original":[67],"low-density":[68],"inputs":[69],"usually":[70],"introduce":[71],"noise,":[72],"where":[73],"model":[76],"may":[77],"overfit":[78],"data.":[81],"To":[82],"this":[83],"end,":[84],"we":[85,113,136],"propose":[86],"new":[88],"method":[94,118,142],"(GADMR":[95],"for":[96],"short).":[97],"GADMR":[98,161],"introduces":[99],"multi-scale":[101],"reconstruction,":[102],"which":[103],"contains":[104],"both":[105],"structure":[106,111],"feature":[108,134,151],"levels.":[109],"From":[110,133],"level,":[112,135],"use":[114],"node-subgraph":[116],"relationship":[122],"between":[123],"their":[127],"neighboring":[128],"subgraphs":[129],"at":[130],"multiple":[131],"scales.":[132],"employ":[137],"multi-layer":[139],"representation":[140],"facilitate":[144],"evaluation":[146],"discrepancies":[148],"in":[149],"high-level":[150],"representations.":[152],"Extensive":[153],"experiments":[154],"six":[156],"benchmark":[157],"datasets":[158],"show":[159],"that":[160],"outperforms":[162],"popular":[163]},"counts_by_year":[{"year":2026,"cited_by_count":2}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}