{"id":"https://openalex.org/W4412877030","doi":"https://doi.org/10.1145/3711896.3736843","title":"AnomalyGFM: Graph Foundation Model for Zero/Few-shot Anomaly Detection","display_name":"AnomalyGFM: Graph Foundation Model for Zero/Few-shot Anomaly Detection","publication_year":2025,"publication_date":"2025-08-03","ids":{"openalex":"https://openalex.org/W4412877030","doi":"https://doi.org/10.1145/3711896.3736843"},"language":"en","primary_location":{"id":"doi:10.1145/3711896.3736843","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3711896.3736843","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3711896.3736843","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 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining V.2","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://dl.acm.org/doi/pdf/10.1145/3711896.3736843","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5001991004","display_name":"Hezhe Qiao","orcid":"https://orcid.org/0000-0003-3511-0528"},"institutions":[{"id":"https://openalex.org/I79891267","display_name":"Singapore Management University","ror":"https://ror.org/050qmg959","country_code":"SG","type":"education","lineage":["https://openalex.org/I79891267"]}],"countries":["SG"],"is_corresponding":false,"raw_author_name":"Hezhe Qiao","raw_affiliation_strings":["School of Computing and Information Systems, Singapore Management University, Singapore, Singapore"],"raw_orcid":"https://orcid.org/0000-0003-3511-0528","affiliations":[{"raw_affiliation_string":"School of Computing and Information Systems, Singapore Management University, Singapore, Singapore","institution_ids":["https://openalex.org/I79891267"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5075093679","display_name":"Chaoxi Niu","orcid":"https://orcid.org/0000-0003-4529-8560"},"institutions":[{"id":"https://openalex.org/I114017466","display_name":"University of Technology Sydney","ror":"https://ror.org/03f0f6041","country_code":"AU","type":"education","lineage":["https://openalex.org/I114017466"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Chaoxi Niu","raw_affiliation_strings":["Australian Artificial Intelligence Institute, University of Technology Sydney, Sydney, NSW, Australia"],"raw_orcid":"https://orcid.org/0000-0003-4529-8560","affiliations":[{"raw_affiliation_string":"Australian Artificial Intelligence Institute, University of Technology Sydney, Sydney, NSW, Australia","institution_ids":["https://openalex.org/I114017466"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5069988750","display_name":"Ling Chen","orcid":"https://orcid.org/0000-0002-6468-5729"},"institutions":[{"id":"https://openalex.org/I114017466","display_name":"University of Technology Sydney","ror":"https://ror.org/03f0f6041","country_code":"AU","type":"education","lineage":["https://openalex.org/I114017466"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Ling Chen","raw_affiliation_strings":["Australian Artificial Intelligence Institute, University of Technology Sydney, Sydney, NSW, Australia"],"raw_orcid":"https://orcid.org/0000-0002-6468-5729","affiliations":[{"raw_affiliation_string":"Australian Artificial Intelligence Institute, University of Technology Sydney, Sydney, NSW, Australia","institution_ids":["https://openalex.org/I114017466"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5039104219","display_name":"Guansong Pang","orcid":"https://orcid.org/0000-0002-9877-2716"},"institutions":[{"id":"https://openalex.org/I79891267","display_name":"Singapore Management University","ror":"https://ror.org/050qmg959","country_code":"SG","type":"education","lineage":["https://openalex.org/I79891267"]}],"countries":["SG"],"is_corresponding":false,"raw_author_name":"Guansong Pang","raw_affiliation_strings":["School of Computing and Information Systems, Singapore Management University, Singapore, Singapore"],"raw_orcid":"https://orcid.org/0000-0002-9877-2716","affiliations":[{"raw_affiliation_string":"School of Computing and Information Systems, Singapore Management University, Singapore, Singapore","institution_ids":["https://openalex.org/I79891267"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":true,"cited_by_count":10,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"2326","last_page":"2337"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9998999834060669,"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":0.9998999834060669,"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/T10400","display_name":"Network Security and Intrusion Detection","score":0.9821000099182129,"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"}},{"id":"https://openalex.org/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.9641000032424927,"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/anomaly-detection","display_name":"Anomaly detection","score":0.544685959815979},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5394995212554932},{"id":"https://openalex.org/keywords/zero","display_name":"Zero (linguistics)","score":0.5234025120735168},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.4860439896583557},{"id":"https://openalex.org/keywords/foundation","display_name":"Foundation (evidence)","score":0.46135756373405457},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3965117335319519},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.20967206358909607},{"id":"https://openalex.org/keywords/geography","display_name":"Geography","score":0.06252911686897278},{"id":"https://openalex.org/keywords/philosophy","display_name":"Philosophy","score":0.055887430906295776}],"concepts":[{"id":"https://openalex.org/C739882","wikidata":"https://www.wikidata.org/wiki/Q3560506","display_name":"Anomaly detection","level":2,"score":0.544685959815979},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5394995212554932},{"id":"https://openalex.org/C2780813799","wikidata":"https://www.wikidata.org/wiki/Q3274237","display_name":"Zero (linguistics)","level":2,"score":0.5234025120735168},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.4860439896583557},{"id":"https://openalex.org/C2780966255","wikidata":"https://www.wikidata.org/wiki/Q5474306","display_name":"Foundation (evidence)","level":2,"score":0.46135756373405457},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3965117335319519},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.20967206358909607},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.06252911686897278},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.055887430906295776},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C166957645","wikidata":"https://www.wikidata.org/wiki/Q23498","display_name":"Archaeology","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3711896.3736843","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3711896.3736843","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3711896.3736843","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 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining V.2","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.1145/3711896.3736843","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3711896.3736843","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3711896.3736843","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 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining V.2","raw_type":"proceedings-article"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G2291743649","display_name":null,"funder_award_id":"DP210101347","funder_id":"https://openalex.org/F4320323817","funder_display_name":"Universitas Brawijaya"}],"funders":[{"id":"https://openalex.org/F4320320751","display_name":"Ministry of Education - Singapore","ror":"https://ror.org/01kcva023"},{"id":"https://openalex.org/F4320323817","display_name":"Universitas Brawijaya","ror":"https://ror.org/01wk3d929"}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4412877030.pdf","grobid_xml":"https://content.openalex.org/works/W4412877030.grobid-xml"},"referenced_works_count":25,"referenced_works":["https://openalex.org/W2062769337","https://openalex.org/W2089554624","https://openalex.org/W2944250323","https://openalex.org/W2965683718","https://openalex.org/W2990138404","https://openalex.org/W3015799890","https://openalex.org/W3068123808","https://openalex.org/W3102969158","https://openalex.org/W3133518153","https://openalex.org/W3153858161","https://openalex.org/W3195065899","https://openalex.org/W3206604724","https://openalex.org/W3209441117","https://openalex.org/W4285066127","https://openalex.org/W4290877635","https://openalex.org/W4367046771","https://openalex.org/W4367047347","https://openalex.org/W4381714210","https://openalex.org/W4383468961","https://openalex.org/W4386081020","https://openalex.org/W4391901119","https://openalex.org/W4401856690","https://openalex.org/W4401857377","https://openalex.org/W4402733578","https://openalex.org/W6800467824"],"related_works":["https://openalex.org/W2381393187","https://openalex.org/W2332779545","https://openalex.org/W2358060160","https://openalex.org/W2035483685","https://openalex.org/W1969764885","https://openalex.org/W596947562","https://openalex.org/W2793937822","https://openalex.org/W2790817834","https://openalex.org/W2777605427","https://openalex.org/W2501983714"],"abstract_inverted_index":{"Graph":[0],"anomaly":[1],"detection":[2],"(GAD)":[3],"aims":[4],"to":[5,42,44,110,123,194,222],"identify":[6],"abnormal":[7,106,129,188],"nodes":[8,16,165,210,225],"that":[9,83,100,247],"differ":[10],"from":[11,50,69,142,166],"the":[12,15,45,59,149,162,178,185,213],"majority":[13],"of":[14,139,164,180],"in":[17,26,36,53,67,93,169,212],"a":[18,78,140,153,170,174],"graph,":[19],"which":[20,190],"has":[21],"been":[22],"attracting":[23],"significant":[24],"attention":[25],"recent":[27],"years.Existing":[28],"generalist":[29],"graph":[30,38,80,95],"models":[31],"have":[32],"achieved":[33],"remarkable":[34],"success":[35],"different":[37,70,116,167],"tasks":[39],"but":[40],"struggle":[41],"generalize":[43],"GAD":[46,92,114,197,233,258],"task.This":[47],"limitation":[48],"arises":[49],"their":[51],"difficulty":[52],"learning":[54,179],"generalized":[55],"knowledge":[56],"for":[57,91,103,177,184,226],"capturing":[58],"inherently":[60],"infrequent,":[61],"irregular":[62],"and":[63,87,105,128,187,243],"heterogeneous":[64],"abnormality":[65,163],"patterns":[66],"graphs":[68,168],"domains.To":[71],"address":[72],"this":[73],"challenge,":[74],"we":[75,158],"propose":[76],"AnomalyGFM,":[77],"GAD-oriented":[79],"foundation":[81],"model":[82],"supports":[84],"zero-shot":[85,196],"inference":[86],"few-shot":[88,207,257],"prompt":[89,220],"tuning":[90,221],"diverse":[94],"datasets.One":[96],"key":[97],"insight":[98],"is":[99,121],"graph-agnostic":[101],"representations":[102],"normal":[104,127,186,209],"classes":[107],"are":[108,206],"required":[109],"support":[111,219],"effective":[112],"zero/few-shot":[113],"across":[115],"graphs.Motivated":[117],"by":[118],"this,":[119],"AnomalyGFM":[120,216,248],"pre-trained":[122],"align":[124],"data-independent,":[125],"learnable":[126],"class":[130],"prototypes":[131,183],"with":[132,235],"node":[133,141,150],"representation":[134,137],"residuals":[135],"(i.e.,":[136],"deviation":[138],"its":[143],"neighbors).The":[144],"residual":[145],"features":[146],"essentially":[147],"project":[148],"information":[151],"into":[152],"unified":[154],"feature":[155],"space":[156],"where":[157],"can":[159,191,217],"effectively":[160],"measure":[161],"consistent":[171],"way.This":[172],"provides":[173],"driving":[175],"force":[176],"graph-agnostic,":[181],"discriminative":[182],"classes,":[189],"be":[192],"used":[193],"enable":[195],"on":[198,230],"new":[199,214],"graphs,":[200,215],"including":[201],"very":[202],"large-scale":[203],"graphs.If":[204],"there":[205],"labeled":[208],"available":[211],"further":[218],"leverage":[223],"these":[224],"better":[227],"adaptation.Comprehensive":[228],"experiments":[229],"11":[231],"widely-used":[232],"datasets":[234],"real":[236],"anomalies,":[237],"covering":[238],"social":[239],"networks,":[240,242,245],"finance":[241],"co-review":[244],"demonstrate":[246],"significantly":[249],"outperforms":[250],"state-of-the-art":[251],"competing":[252],"methods":[253],"under":[254],"both":[255],"zero-and":[256],"settings.":[259]},"counts_by_year":[{"year":2026,"cited_by_count":7},{"year":2025,"cited_by_count":3}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}
