{"id":"https://openalex.org/W7152525430","doi":"https://doi.org/10.1145/3774904.3792340","title":"PAGE: Progressive Anomaly Generation Network for Semi-supervised Graph Anomaly Detection","display_name":"PAGE: Progressive Anomaly Generation Network for Semi-supervised Graph Anomaly Detection","publication_year":2026,"publication_date":"2026-04-09","ids":{"openalex":"https://openalex.org/W7152525430","doi":"https://doi.org/10.1145/3774904.3792340"},"language":null,"primary_location":{"id":"doi:10.1145/3774904.3792340","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3774904.3792340","pdf_url":null,"source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the ACM Web Conference 2026","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.1145/3774904.3792340","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5028670963","display_name":"Ting Guo","orcid":"https://orcid.org/0000-0002-5264-4882"},"institutions":[{"id":"https://openalex.org/I135714990","display_name":"North University of China","ror":"https://ror.org/047bp1713","country_code":"CN","type":"education","lineage":["https://openalex.org/I135714990"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Ting Guo","raw_affiliation_strings":["North University of China, Taiyuan, China"],"raw_orcid":"https://orcid.org/0000-0002-5264-4882","affiliations":[{"raw_affiliation_string":"North University of China, Taiyuan, China","institution_ids":["https://openalex.org/I135714990"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5007767351","display_name":"Dongyu Pei","orcid":null},"institutions":[{"id":"https://openalex.org/I135714990","display_name":"North University of China","ror":"https://ror.org/047bp1713","country_code":"CN","type":"education","lineage":["https://openalex.org/I135714990"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Dongyu Pei","raw_affiliation_strings":["North University of China, Taiyuan, China"],"raw_orcid":"https://orcid.org/0009-0000-0544-2578","affiliations":[{"raw_affiliation_string":"North University of China, Taiyuan, China","institution_ids":["https://openalex.org/I135714990"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5068709005","display_name":"Gangzhu Qiao","orcid":"https://orcid.org/0000-0003-3747-8393"},"institutions":[{"id":"https://openalex.org/I135714990","display_name":"North University of China","ror":"https://ror.org/047bp1713","country_code":"CN","type":"education","lineage":["https://openalex.org/I135714990"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Gangzhu Qiao","raw_affiliation_strings":["North University of China, Taiyuan, China"],"raw_orcid":"https://orcid.org/0000-0003-3747-8393","affiliations":[{"raw_affiliation_string":"North University of China, Taiyuan, China","institution_ids":["https://openalex.org/I135714990"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5085483879","display_name":"Kaixuan Yao","orcid":"https://orcid.org/0000-0001-8468-8532"},"institutions":[{"id":"https://openalex.org/I181877577","display_name":"Shanxi University","ror":"https://ror.org/03y3e3s17","country_code":"CN","type":"education","lineage":["https://openalex.org/I181877577"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Kaixuan Yao","raw_affiliation_strings":["Shanxi  University, Taiyuan, China"],"raw_orcid":"https://orcid.org/0000-0001-8468-8532","affiliations":[{"raw_affiliation_string":"Shanxi  University, Taiyuan, China","institution_ids":["https://openalex.org/I181877577"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5075206668","display_name":"Da Zhong Wang","orcid":null},"institutions":[{"id":"https://openalex.org/I181877577","display_name":"Shanxi University","ror":"https://ror.org/03y3e3s17","country_code":"CN","type":"education","lineage":["https://openalex.org/I181877577"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Da Wang","raw_affiliation_strings":["Shanxi  University, Taiyuan, China"],"raw_orcid":"https://orcid.org/0000-0002-6009-3187","affiliations":[{"raw_affiliation_string":"Shanxi  University, Taiyuan, China","institution_ids":["https://openalex.org/I181877577"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":21.2894,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.98348229,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":94,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"959","last_page":"967"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.7060999870300293,"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/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.7060999870300293,"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/T11273","display_name":"Advanced Graph Neural Networks","score":0.0632999986410141,"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/T12127","display_name":"Software System Performance and Reliability","score":0.039500001817941666,"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":0.5313000082969666},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.42989999055862427},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.36309999227523804},{"id":"https://openalex.org/keywords/anomaly","display_name":"Anomaly (physics)","score":0.3628000020980835}],"concepts":[{"id":"https://openalex.org/C739882","wikidata":"https://www.wikidata.org/wiki/Q3560506","display_name":"Anomaly detection","level":2,"score":0.5313000082969666},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5073000192642212},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.42989999055862427},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.42480000853538513},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.36309999227523804},{"id":"https://openalex.org/C12997251","wikidata":"https://www.wikidata.org/wiki/Q567560","display_name":"Anomaly (physics)","level":2,"score":0.3628000020980835},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3156000077724457},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3147999942302704},{"id":"https://openalex.org/C88230418","wikidata":"https://www.wikidata.org/wiki/Q131476","display_name":"Graph theory","level":2,"score":0.2535000145435333},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.23890000581741333}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3774904.3792340","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3774904.3792340","pdf_url":null,"source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the ACM Web Conference 2026","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.1145/3774904.3792340","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3774904.3792340","pdf_url":null,"source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the ACM Web Conference 2026","raw_type":"proceedings-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":26,"referenced_works":["https://openalex.org/W2027731328","https://openalex.org/W2089554624","https://openalex.org/W2944250323","https://openalex.org/W2952534380","https://openalex.org/W2965683718","https://openalex.org/W3015799890","https://openalex.org/W3068123808","https://openalex.org/W3093664513","https://openalex.org/W3153858161","https://openalex.org/W3183787799","https://openalex.org/W3206326321","https://openalex.org/W3217753387","https://openalex.org/W4224310669","https://openalex.org/W4224319742","https://openalex.org/W4318811779","https://openalex.org/W4321485229","https://openalex.org/W4367047347","https://openalex.org/W4379927591","https://openalex.org/W4382239148","https://openalex.org/W4382318469","https://openalex.org/W4393159601","https://openalex.org/W4401864185","https://openalex.org/W4403582646","https://openalex.org/W4409365127","https://openalex.org/W4411726016","https://openalex.org/W4414360665"],"related_works":[],"abstract_inverted_index":{"Semi-supervised":[0],"graph":[1,17,175],"anomaly":[2,42,68,79,120,140,176],"detection":[3,57,80],"confronts":[4],"the":[5,39,56,110,118,138],"fundamental":[6],"challenge":[7],"of":[8,29,41,58,132],"identifying":[9],"anomalous":[10],"nodes":[11,93],"that":[12,108,125,165],"exhibit":[13,128],"deviations":[14],"from":[15,112],"normal":[16,31,92],"patterns":[18],"in":[19,37,48],"node":[20,154],"attributes":[21],"or":[22],"structure":[23,102],"connectivity,":[24],"using":[25],"a":[26,49,66,129],"small":[27],"set":[28],"labeled":[30],"nodes.":[32,97],"Existing":[33],"methods":[34],"fall":[35],"short":[36],"modeling":[38],"spectrum":[40],"severity,":[43],"as":[44],"they":[45],"generate":[46,95],"anomalies":[47,107,127],"single,":[50],"undifferentiated":[51],"step.":[52],"This":[53],"oversight":[54],"restricts":[55],"complex":[59,78,115],"anomalies.":[60],"In":[61],"this":[62,74],"paper,":[63],"we":[64],"propose":[65],"progressive":[67,82,101,121],"generation":[69],"network":[70],"(PAGE)":[71],"to":[72,94,104,114,152],"overcome":[73],"limitation.":[75],"PAGE":[76,143,166],"enhances":[77],"through":[81],"attribute-structure":[83],"perturbation:":[84],"It":[85],"first":[86],"injects":[87],"attribute":[88],"noise":[89],"into":[90],"selected":[91],"pseudo-anomalous":[96],"Then":[98],"it":[99],"applies":[100],"perturbation":[103],"create":[105],"hybrid":[106,126],"simulate":[109],"evolution":[111],"mild":[113],"perturbations.":[116],"Importantly,":[117],"proposed":[119],"constraint":[122,147],"loss":[123,148,151],"enforces":[124],"higher":[130],"degree":[131],"abnormality":[133],"than":[134],"primary":[135],"anomalies,":[136],"enhancing":[137],"model's":[139],"quantification":[141],"capability.":[142],"further":[144],"integrates":[145],"deviation":[146],"and":[149,156],"classification":[150],"optimize":[153],"representations":[155],"improve":[157],"prediction.":[158],"Extensive":[159],"experiments":[160],"on":[161],"benchmark":[162],"datasets":[163],"demonstrate":[164],"significantly":[167],"outperforms":[168],"state-of-the-art":[169],"methods,":[170],"demonstrating":[171],"its":[172],"effectiveness":[173],"for":[174],"detection.":[177]},"counts_by_year":[{"year":2026,"cited_by_count":1}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2026-04-10T00:00:00"}