{"id":"https://openalex.org/W7155937676","doi":"https://doi.org/10.1145/3774904.3792442","title":"CIFAD: Causal-Invariant Subspace Learning for Few-Shot Anomaly Detection on Dynamic Relational Graphs","display_name":"CIFAD: Causal-Invariant Subspace Learning for Few-Shot Anomaly Detection on Dynamic Relational Graphs","publication_year":2026,"publication_date":"2026-04-12","ids":{"openalex":"https://openalex.org/W7155937676","doi":"https://doi.org/10.1145/3774904.3792442"},"language":null,"primary_location":{"id":"doi:10.1145/3774904.3792442","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3774904.3792442","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/10.1145/3774904.3792442","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5134737680","display_name":"Yu Xiao","orcid":"https://orcid.org/0009-0003-7603-1364"},"institutions":[{"id":"https://openalex.org/I200845125","display_name":"Nanjing University of Information Science and Technology","ror":"https://ror.org/02y0rxk19","country_code":"CN","type":"education","lineage":["https://openalex.org/I200845125"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yu Xiao","raw_affiliation_strings":["Nanjing University of Information Science and Technology, Nanjing, Jiangsu, China and Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, China"],"raw_orcid":"https://orcid.org/0009-0003-7603-1364","affiliations":[{"raw_affiliation_string":"Nanjing University of Information Science and Technology, Nanjing, Jiangsu, China and Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, China","institution_ids":["https://openalex.org/I200845125"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5134724658","display_name":"Haolong Xiang","orcid":"https://orcid.org/0009-0002-6690-0020"},"institutions":[{"id":"https://openalex.org/I200845125","display_name":"Nanjing University of Information Science and Technology","ror":"https://ror.org/02y0rxk19","country_code":"CN","type":"education","lineage":["https://openalex.org/I200845125"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Haolong Xiang","raw_affiliation_strings":["Nanjing University of Information Science and Technology, Nanjing, China and Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, China"],"raw_orcid":"https://orcid.org/0009-0002-6690-0020","affiliations":[{"raw_affiliation_string":"Nanjing University of Information Science and Technology, Nanjing, China and Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, China","institution_ids":["https://openalex.org/I200845125"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5134748193","display_name":"Xiaolong Xu","orcid":"https://orcid.org/0000-0003-4879-9803"},"institutions":[{"id":"https://openalex.org/I200845125","display_name":"Nanjing University of Information Science and Technology","ror":"https://ror.org/02y0rxk19","country_code":"CN","type":"education","lineage":["https://openalex.org/I200845125"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiaolong Xu","raw_affiliation_strings":["Nanjing University of Information Science and Technology, Nanjing, China"],"raw_orcid":"https://orcid.org/0000-0003-4879-9803","affiliations":[{"raw_affiliation_string":"Nanjing University of Information Science and Technology, Nanjing, China","institution_ids":["https://openalex.org/I200845125"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5006514077","display_name":"Lianyong Qi","orcid":"https://orcid.org/0000-0001-9875-9856"},"institutions":[{"id":"https://openalex.org/I4210162190","display_name":"China University of Petroleum, East China","ror":"https://ror.org/05gbn2817","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210162190"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Lianyong Qi","raw_affiliation_strings":["China University of Petroleum (East China), Qingdao, China"],"raw_orcid":"https://orcid.org/0000-0001-9875-9856","affiliations":[{"raw_affiliation_string":"China University of Petroleum (East China), Qingdao, China","institution_ids":["https://openalex.org/I4210162190"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5076120553","display_name":"Xuyun Zhang","orcid":"https://orcid.org/0000-0001-7353-4159"},"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":"Xuyun Zhang","raw_affiliation_strings":["Macquarie University, Sydney, Australia"],"raw_orcid":"https://orcid.org/0000-0001-7353-4159","affiliations":[{"raw_affiliation_string":"Macquarie University, Sydney, Australia","institution_ids":["https://openalex.org/I99043593"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5129675426","display_name":"Wei Fan","orcid":null},"institutions":[{"id":"https://openalex.org/I154130895","display_name":"University of Auckland","ror":"https://ror.org/03b94tp07","country_code":"NZ","type":"education","lineage":["https://openalex.org/I154130895"]}],"countries":["NZ"],"is_corresponding":false,"raw_author_name":"Wei Fan","raw_affiliation_strings":["University of Auckland, Auckland, New Zealand"],"raw_orcid":"https://orcid.org/0000-0001-7656-445X","affiliations":[{"raw_affiliation_string":"University of Auckland, Auckland, New Zealand","institution_ids":["https://openalex.org/I154130895"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5133307124","display_name":"Wanchun Dou","orcid":"https://orcid.org/0000-0003-4833-2023"},"institutions":[{"id":"https://openalex.org/I881766915","display_name":"Nanjing University","ror":"https://ror.org/01rxvg760","country_code":"CN","type":"education","lineage":["https://openalex.org/I881766915"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wanchun Dou","raw_affiliation_strings":["Nanjing University, Nanjing, China"],"raw_orcid":"https://orcid.org/0000-0003-4833-2023","affiliations":[{"raw_affiliation_string":"Nanjing University, Nanjing, China","institution_ids":["https://openalex.org/I881766915"]}]}],"institutions":[],"countries_distinct_count":3,"institutions_distinct_count":5,"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":"4680","last_page":"4688"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11273","display_name":"Advanced Graph Neural Networks","score":0.7603999972343445,"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":0.7603999972343445,"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":0.05849999934434891,"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":0.03460000082850456,"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":0.7630000114440918},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.6428999900817871},{"id":"https://openalex.org/keywords/subspace-topology","display_name":"Subspace topology","score":0.6256999969482422},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.4417000114917755},{"id":"https://openalex.org/keywords/annotation","display_name":"Annotation","score":0.4221000075340271},{"id":"https://openalex.org/keywords/tensor-decomposition","display_name":"Tensor decomposition","score":0.3400999903678894},{"id":"https://openalex.org/keywords/data-modeling","display_name":"Data modeling","score":0.3231000006198883},{"id":"https://openalex.org/keywords/social-network","display_name":"Social network (sociolinguistics)","score":0.3188000023365021},{"id":"https://openalex.org/keywords/network-security","display_name":"Network security","score":0.30790001153945923}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7825000286102295},{"id":"https://openalex.org/C739882","wikidata":"https://www.wikidata.org/wiki/Q3560506","display_name":"Anomaly detection","level":2,"score":0.7630000114440918},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.6428999900817871},{"id":"https://openalex.org/C32834561","wikidata":"https://www.wikidata.org/wiki/Q660730","display_name":"Subspace topology","level":2,"score":0.6256999969482422},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.5508999824523926},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.520799994468689},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.47110000252723694},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.4417000114917755},{"id":"https://openalex.org/C2776321320","wikidata":"https://www.wikidata.org/wiki/Q857525","display_name":"Annotation","level":2,"score":0.4221000075340271},{"id":"https://openalex.org/C2986737658","wikidata":"https://www.wikidata.org/wiki/Q30103009","display_name":"Tensor decomposition","level":3,"score":0.3400999903678894},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.3231000006198883},{"id":"https://openalex.org/C4727928","wikidata":"https://www.wikidata.org/wiki/Q17164759","display_name":"Social network (sociolinguistics)","level":3,"score":0.3188000023365021},{"id":"https://openalex.org/C182590292","wikidata":"https://www.wikidata.org/wiki/Q989632","display_name":"Network security","level":2,"score":0.30790001153945923},{"id":"https://openalex.org/C33762810","wikidata":"https://www.wikidata.org/wiki/Q461671","display_name":"Data integrity","level":2,"score":0.3061000108718872},{"id":"https://openalex.org/C5655090","wikidata":"https://www.wikidata.org/wiki/Q192588","display_name":"Relational database","level":2,"score":0.29989999532699585},{"id":"https://openalex.org/C110893760","wikidata":"https://www.wikidata.org/wiki/Q3115590","display_name":"Named graph","level":5,"score":0.29899999499320984},{"id":"https://openalex.org/C42355184","wikidata":"https://www.wikidata.org/wiki/Q1361088","display_name":"Matrix decomposition","level":3,"score":0.2879999876022339},{"id":"https://openalex.org/C118930307","wikidata":"https://www.wikidata.org/wiki/Q600590","display_name":"Tuple","level":2,"score":0.28619998693466187},{"id":"https://openalex.org/C114713312","wikidata":"https://www.wikidata.org/wiki/Q7551269","display_name":"Social network analysis","level":3,"score":0.28529998660087585},{"id":"https://openalex.org/C13540734","wikidata":"https://www.wikidata.org/wiki/Q5318996","display_name":"Dynamic network analysis","level":2,"score":0.27300000190734863},{"id":"https://openalex.org/C12725497","wikidata":"https://www.wikidata.org/wiki/Q810247","display_name":"Baseline (sea)","level":2,"score":0.26269999146461487},{"id":"https://openalex.org/C197298091","wikidata":"https://www.wikidata.org/wiki/Q5318963","display_name":"Dynamic data","level":2,"score":0.26190000772476196},{"id":"https://openalex.org/C2780735816","wikidata":"https://www.wikidata.org/wiki/Q28324931","display_name":"Incremental learning","level":2,"score":0.26080000400543213},{"id":"https://openalex.org/C35525427","wikidata":"https://www.wikidata.org/wiki/Q745881","display_name":"Intrusion detection system","level":2,"score":0.25679999589920044},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.2565999925136566},{"id":"https://openalex.org/C136389625","wikidata":"https://www.wikidata.org/wiki/Q334384","display_name":"Supervised learning","level":3,"score":0.25600001215934753},{"id":"https://openalex.org/C29265498","wikidata":"https://www.wikidata.org/wiki/Q7047719","display_name":"Noise measurement","level":3,"score":0.25589999556541443}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3774904.3792442","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3774904.3792442","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:10.1145/3774904.3792442","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3774904.3792442","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":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":33,"referenced_works":["https://openalex.org/W4321485229","https://openalex.org/W4368232640","https://openalex.org/W4385764457","https://openalex.org/W4385768068","https://openalex.org/W4388107294","https://openalex.org/W4390872785","https://openalex.org/W4392693636","https://openalex.org/W4399661606","https://openalex.org/W4399804785","https://openalex.org/W4402716375","https://openalex.org/W4403996974","https://openalex.org/W4406354592","https://openalex.org/W4406598481","https://openalex.org/W4407056396","https://openalex.org/W4407832144","https://openalex.org/W4408345909","https://openalex.org/W4408354090","https://openalex.org/W4409363174","https://openalex.org/W4409363394","https://openalex.org/W4409364187","https://openalex.org/W4409365059","https://openalex.org/W4409365221","https://openalex.org/W4409366876","https://openalex.org/W4409367042","https://openalex.org/W4409657216","https://openalex.org/W4409657439","https://openalex.org/W4409657560","https://openalex.org/W4410852754","https://openalex.org/W4413144266","https://openalex.org/W4413349568","https://openalex.org/W4413856775","https://openalex.org/W4414360002","https://openalex.org/W4414360452"],"related_works":[],"abstract_inverted_index":{"Abnormal":[0],"user":[1,44,55,196],"detection":[2,110,197],"has":[3],"been":[4],"a":[5,100,120,170],"critical":[6],"and":[7,25,41,63,83,145,159],"widely":[8],"studied":[9],"research":[10],"problem":[11],"in":[12,50,90,169,194],"social":[13,38,91,178,199],"networks":[14],"since":[15],"these":[16,75,95],"users":[17],"can":[18],"create":[19],"significant":[20,48],"risks":[21],"to":[22,47,67,85,125,136,162],"platform":[23],"security":[24],"privacy":[26],"leakage.":[27],"Currently,":[28],"graph-based":[29,53],"models":[30],"are":[31,61,77],"commonly":[32],"used":[33],"for":[34,198],"exploring":[35],"the":[36,65,164],"structured":[37],"network":[39,179],"data":[40],"temporally":[42],"dynamic":[43,51,142],"interactions,":[45],"leading":[46],"advances":[49],"heterogeneous":[52],"abnormal":[54,195],"detection.":[56],"However,":[57],"most":[58],"existing":[59],"approaches":[60],"correlation-driven":[62],"lack":[64],"ability":[66],"separate":[68],"stable":[69,138],"patterns":[70],"from":[71,141],"transient":[72],"noise.":[73],"Furthermore,":[74,130],"methods":[76],"highly":[78],"dependent":[79],"on":[80,156,175],"inherent":[81],"labels":[82,168],"fail":[84],"detect":[86],"common":[87],"few-shot":[88,108],"anomalies":[89],"networks.":[92,200],"To":[93],"address":[94],"issues,":[96],"we":[97],"propose":[98],"CIFAD,":[99],"Causal-Invariant":[101],"Few-shot":[102],"Anomaly":[103],"Detection":[104],"method":[105,184],"that":[106,182],"improves":[107],"anomaly":[109],"with":[111],"an":[112,151],"active":[113,152],"annotation":[114,153],"strategy.":[115],"Specifically,":[116],"CIFAD":[117],"first":[118],"integrates":[119],"sparse":[121],"lagged":[122],"attention":[123],"encoder":[124],"model":[126],"multi-relational":[127],"temporal":[128],"interactions.":[129],"it":[131,149],"introduces":[132],"causal-invariant":[133],"subspace":[134],"decomposition":[135],"disentangle":[137],"causal":[139],"signals":[140],"environmental":[143],"noise":[144],"improve":[146],"generalization.":[147],"Finally,":[148],"designs":[150],"strategy":[154],"based":[155],"influence":[157],"functions":[158],"coverage":[160],"optimization":[161],"maximize":[163],"utility":[165],"of":[166],"limited":[167],"closed-loop":[171],"process.":[172],"Extensive":[173],"experiments":[174],"multiple":[176],"real-world":[177],"datasets":[180],"demonstrate":[181],"our":[183],"achieves":[185],"higher":[186],"accuracy":[187],"than":[188],"state-of-the-art":[189],"methods,":[190],"validating":[191],"its":[192],"robustness":[193]},"counts_by_year":[],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2026-04-28T00:00:00"}