{"id":"https://openalex.org/W4416250796","doi":"https://doi.org/10.1109/ijcnn64981.2025.11227724","title":"Frequency-Spatial Domain Fusion for Graph Anomaly Detection","display_name":"Frequency-Spatial Domain Fusion for Graph Anomaly Detection","publication_year":2025,"publication_date":"2025-06-30","ids":{"openalex":"https://openalex.org/W4416250796","doi":"https://doi.org/10.1109/ijcnn64981.2025.11227724"},"language":null,"primary_location":{"id":"doi:10.1109/ijcnn64981.2025.11227724","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn64981.2025.11227724","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 International Joint Conference on Neural Networks (IJCNN)","raw_type":"proceedings-article"},"type":"article","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/A5020017425","display_name":"Senbao Hou","orcid":null},"institutions":[{"id":"https://openalex.org/I96908189","display_name":"Xinjiang University","ror":"https://ror.org/059gw8r13","country_code":"CN","type":"education","lineage":["https://openalex.org/I96908189"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Senbao Hou","raw_affiliation_strings":["Xinjiang University,College of Software,Urumqi,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Xinjiang University,College of Software,Urumqi,China","institution_ids":["https://openalex.org/I96908189"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5047166646","display_name":"Enguang Zuo","orcid":null},"institutions":[{"id":"https://openalex.org/I96908189","display_name":"Xinjiang University","ror":"https://ror.org/059gw8r13","country_code":"CN","type":"education","lineage":["https://openalex.org/I96908189"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Enguang Zuo","raw_affiliation_strings":["Xinjiang University,School of Intelligent Science and Technology,Urumqi,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Xinjiang University,School of Intelligent Science and Technology,Urumqi,China","institution_ids":["https://openalex.org/I96908189"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5108144939","display_name":"Wenyan Wang","orcid":"https://orcid.org/0009-0005-4284-2008"},"institutions":[{"id":"https://openalex.org/I96908189","display_name":"Xinjiang University","ror":"https://ror.org/059gw8r13","country_code":"CN","type":"education","lineage":["https://openalex.org/I96908189"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wenyan Wang","raw_affiliation_strings":["Xinjiang University,College of Software,Urumqi,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Xinjiang University,College of Software,Urumqi,China","institution_ids":["https://openalex.org/I96908189"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5019066825","display_name":"Ruiting Wang","orcid":null},"institutions":[{"id":"https://openalex.org/I96908189","display_name":"Xinjiang University","ror":"https://ror.org/059gw8r13","country_code":"CN","type":"education","lineage":["https://openalex.org/I96908189"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Ruiting Wang","raw_affiliation_strings":["Xinjiang University,College of Software,Urumqi,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Xinjiang University,College of Software,Urumqi,China","institution_ids":["https://openalex.org/I96908189"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100418543","display_name":"Chen Chen","orcid":"https://orcid.org/0000-0003-1406-5721"},"institutions":[{"id":"https://openalex.org/I96908189","display_name":"Xinjiang University","ror":"https://ror.org/059gw8r13","country_code":"CN","type":"education","lineage":["https://openalex.org/I96908189"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiaoyi Lv","raw_affiliation_strings":["Xinjiang University,College of Software,Urumqi,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Xinjiang University,College of Software,Urumqi,China","institution_ids":["https://openalex.org/I96908189"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.16533171,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"8"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11273","display_name":"Advanced Graph Neural Networks","score":0.9337000250816345,"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":0.9337000250816345,"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":0.01549999974668026,"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/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.009499999694526196,"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":0.6844000220298767},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.6588000059127808},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.5745000243186951},{"id":"https://openalex.org/keywords/smoothing","display_name":"Smoothing","score":0.506600022315979},{"id":"https://openalex.org/keywords/biometrics","display_name":"Biometrics","score":0.375900000333786},{"id":"https://openalex.org/keywords/frequency-domain","display_name":"Frequency domain","score":0.3709000051021576},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.362199991941452},{"id":"https://openalex.org/keywords/anomaly","display_name":"Anomaly (physics)","score":0.35920000076293945},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.3400000035762787}],"concepts":[{"id":"https://openalex.org/C739882","wikidata":"https://www.wikidata.org/wiki/Q3560506","display_name":"Anomaly detection","level":2,"score":0.6844000220298767},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.6588000059127808},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6126000285148621},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6068999767303467},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.5745000243186951},{"id":"https://openalex.org/C3770464","wikidata":"https://www.wikidata.org/wiki/Q775963","display_name":"Smoothing","level":2,"score":0.506600022315979},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3887999951839447},{"id":"https://openalex.org/C184297639","wikidata":"https://www.wikidata.org/wiki/Q177765","display_name":"Biometrics","level":2,"score":0.375900000333786},{"id":"https://openalex.org/C19118579","wikidata":"https://www.wikidata.org/wiki/Q786423","display_name":"Frequency domain","level":2,"score":0.3709000051021576},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.362199991941452},{"id":"https://openalex.org/C12997251","wikidata":"https://www.wikidata.org/wiki/Q567560","display_name":"Anomaly (physics)","level":2,"score":0.35920000076293945},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.3400000035762787},{"id":"https://openalex.org/C102519508","wikidata":"https://www.wikidata.org/wiki/Q6520159","display_name":"Fourier transform","level":2,"score":0.3292999863624573},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.3199000060558319},{"id":"https://openalex.org/C83665646","wikidata":"https://www.wikidata.org/wiki/Q42139305","display_name":"Feature vector","level":2,"score":0.3028999865055084},{"id":"https://openalex.org/C36503486","wikidata":"https://www.wikidata.org/wiki/Q11235244","display_name":"Domain (mathematical analysis)","level":2,"score":0.29499998688697815},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.29089999198913574},{"id":"https://openalex.org/C70518039","wikidata":"https://www.wikidata.org/wiki/Q16000077","display_name":"Dimensionality reduction","level":2,"score":0.2809000015258789},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.2793999910354614},{"id":"https://openalex.org/C158525013","wikidata":"https://www.wikidata.org/wiki/Q2593739","display_name":"Fusion","level":2,"score":0.27720001339912415},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.2757999897003174},{"id":"https://openalex.org/C2781067378","wikidata":"https://www.wikidata.org/wiki/Q17027399","display_name":"Interpretability","level":2,"score":0.27570000290870667},{"id":"https://openalex.org/C106937863","wikidata":"https://www.wikidata.org/wiki/Q7236518","display_name":"Power graph analysis","level":3,"score":0.2754000127315521},{"id":"https://openalex.org/C88230418","wikidata":"https://www.wikidata.org/wiki/Q131476","display_name":"Graph theory","level":2,"score":0.26600000262260437},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.25870001316070557},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.25609999895095825},{"id":"https://openalex.org/C111335779","wikidata":"https://www.wikidata.org/wiki/Q3454686","display_name":"Reduction (mathematics)","level":2,"score":0.2533999979496002}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ijcnn64981.2025.11227724","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn64981.2025.11227724","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 International Joint Conference on Neural Networks (IJCNN)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320326153","display_name":"Xinjiang Production and Construction Corps","ror":"https://ror.org/03hcmxw73"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":21,"referenced_works":["https://openalex.org/W1991252559","https://openalex.org/W2022322548","https://openalex.org/W2046253692","https://openalex.org/W2101491865","https://openalex.org/W2133299088","https://openalex.org/W2153959628","https://openalex.org/W2741114205","https://openalex.org/W2808544127","https://openalex.org/W2944250323","https://openalex.org/W2963486145","https://openalex.org/W2965683718","https://openalex.org/W3093649180","https://openalex.org/W3128443161","https://openalex.org/W3133518153","https://openalex.org/W3153858161","https://openalex.org/W3198135059","https://openalex.org/W3206326321","https://openalex.org/W4221152131","https://openalex.org/W4285603647","https://openalex.org/W4379927591","https://openalex.org/W4382239148"],"related_works":[],"abstract_inverted_index":{"Graph":[0,73],"anomaly":[1,63,128],"detection":[2],"(GAD)":[3],"remains":[4],"a":[5,151],"major":[6],"challenge":[7],"in":[8,26],"artificial":[9],"intelligence":[10],"security":[11],"applications.":[12],"Existing":[13],"graph":[14,99],"neural":[15],"networks":[16],"(GNN)":[17],"face":[18],"two":[19],"key":[20],"issues:":[21],"(1)":[22],"the":[23,32,60],"neighborhood":[24,112],"smoothing":[25],"spatial":[27,121],"domain":[28,124],"methods":[29,143],"can":[30],"mask":[31],"distribution":[33,104],"differences":[34],"between":[35],"normal":[36],"and":[37,83,111,115,122,150,159],"abnormal":[38,109],"graphs,":[39],"filtering":[40],"out":[41],"important":[42],"high-frequency":[43],"signals;":[44],"(2)":[45],"their":[46],"excessive":[47],"reliance":[48],"on":[49,134],"local":[50],"structural":[51],"patterns":[52,105],"fails":[53],"to":[54,106],"capture":[55],"global":[56,103],"frequency":[57,123],"responses,":[58],"limiting":[59],"effectiveness":[61],"of":[62,148,155],"detection.":[64,130],"To":[65],"address":[66],"these":[67],"issues,":[68],"we":[69],"propose":[70],"FSGAD":[71,140],"(Frequency-Spatial":[72],"Anomaly":[74],"Detection),":[75],"an":[76,145],"unsupervised":[77],"framework":[78],"that":[79,139],"combines":[80],"spectral":[81,90,96],"analysis":[82],"contrastive":[84,113],"learning.":[85],"Our":[86,131,157],"main":[87],"innovations":[88],"include":[89],"fingerprint":[91],"extraction,":[92],"which":[93,119],"extracts":[94],"node-level":[95],"features":[97],"through":[98],"Fourier":[100],"transforms,":[101],"capturing":[102],"accurately":[107],"detect":[108],"nodes;":[110],"learning":[114],"dual-channel":[116],"feature":[117],"reconstruction,":[118],"use":[120],"information":[125],"for":[126],"precise":[127],"pattern":[129],"experimental":[132],"results":[133],"eight":[135],"benchmark":[136],"datasets":[137],"show":[138],"surpasses":[141],"existing":[142],"with":[144],"AUC":[146],"improvement":[147],"2.9%":[149],"time":[152],"consumption":[153],"reduction":[154],"21%.":[156],"code":[158],"data":[160],"are":[161],"available":[162],"at":[163],"https://github.com/Senbao/FSGAD.":[164]},"counts_by_year":[],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-11-14T00:00:00"}