{"id":"https://openalex.org/W7134986744","doi":"https://doi.org/10.1109/icdmw69685.2025.00192","title":"A Multi-Snapshot Sketching Framework for Anomaly Detection in Streaming Graphs","display_name":"A Multi-Snapshot Sketching Framework for Anomaly Detection in Streaming Graphs","publication_year":2025,"publication_date":"2025-11-12","ids":{"openalex":"https://openalex.org/W7134986744","doi":"https://doi.org/10.1109/icdmw69685.2025.00192"},"language":null,"primary_location":{"id":"doi:10.1109/icdmw69685.2025.00192","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icdmw69685.2025.00192","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE International Conference on Data Mining Workshops (ICDMW)","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/A5121026370","display_name":"Luis Olmos","orcid":null},"institutions":[{"id":"https://openalex.org/I157638225","display_name":"California State University, Northridge","ror":"https://ror.org/005f5hv41","country_code":"US","type":"education","lineage":["https://openalex.org/I157638225"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Luis Olmos","raw_affiliation_strings":["California State University,Department of Computer Science,Northridge,California,USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"California State University,Department of Computer Science,Northridge,California,USA","institution_ids":["https://openalex.org/I157638225"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5091820414","display_name":"Rashida Hasan","orcid":"https://orcid.org/0000-0002-6231-8116"},"institutions":[{"id":"https://openalex.org/I157638225","display_name":"California State University, Northridge","ror":"https://ror.org/005f5hv41","country_code":"US","type":"education","lineage":["https://openalex.org/I157638225"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Rashida Hasan","raw_affiliation_strings":["California State University,Department of Computer Science,Northridge,California,USA"],"raw_orcid":"https://orcid.org/0000-0002-6231-8116","affiliations":[{"raw_affiliation_string":"California State University,Department of Computer Science,Northridge,California,USA","institution_ids":["https://openalex.org/I157638225"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5121026370"],"corresponding_institution_ids":["https://openalex.org/I157638225"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.85417768,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1596","last_page":"1605"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.29989999532699585,"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":0.29989999532699585,"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/T12761","display_name":"Data Stream Mining Techniques","score":0.12999999523162842,"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/T12292","display_name":"Graph Theory and Algorithms","score":0.06589999794960022,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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":0.47600001096725464},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.28450000286102295},{"id":"https://openalex.org/keywords/field","display_name":"Field (mathematics)","score":0.2533000111579895},{"id":"https://openalex.org/keywords/identification","display_name":"Identification (biology)","score":0.24979999661445618}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5809000134468079},{"id":"https://openalex.org/C739882","wikidata":"https://www.wikidata.org/wiki/Q3560506","display_name":"Anomaly detection","level":2,"score":0.47600001096725464},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3513000011444092},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.32829999923706055},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.3158000111579895},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.28450000286102295},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.2720000147819519},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.2678999900817871},{"id":"https://openalex.org/C9652623","wikidata":"https://www.wikidata.org/wiki/Q190109","display_name":"Field (mathematics)","level":2,"score":0.2533000111579895},{"id":"https://openalex.org/C116834253","wikidata":"https://www.wikidata.org/wiki/Q2039217","display_name":"Identification (biology)","level":2,"score":0.24979999661445618}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icdmw69685.2025.00192","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icdmw69685.2025.00192","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE International Conference on Data Mining Workshops (ICDMW)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":17,"referenced_works":["https://openalex.org/W1492581097","https://openalex.org/W1493892051","https://openalex.org/W2625814963","https://openalex.org/W2906694449","https://openalex.org/W2951086007","https://openalex.org/W2998647325","https://openalex.org/W3037594688","https://openalex.org/W3040611190","https://openalex.org/W3114378557","https://openalex.org/W4205329374","https://openalex.org/W4281384964","https://openalex.org/W4318771342","https://openalex.org/W4385565420","https://openalex.org/W4398234562","https://openalex.org/W4400348418","https://openalex.org/W4408524120","https://openalex.org/W4408615285"],"related_works":[],"abstract_inverted_index":{"Anomaly":[0],"detection":[1,91,180,199],"in":[2,9,68,178,200],"dynamic":[3,201],"graphs":[4],"is":[5,107],"a":[6,23,55,84,115],"vital":[7],"task":[8],"domains":[10],"such":[11],"as":[12],"cybersecurity,":[13],"fraud":[14],"detection,":[15],"and":[16,65,97,106,128,161,182,193],"network":[17],"monitoring.":[18],"Traditional":[19],"approaches":[20],"often":[21],"employ":[22],"single":[24],"decay":[25,87,126,151],"factor":[26],"to":[27,35,123],"model":[28],"temporal":[29,86],"dynamics,":[30],"which":[31],"limits":[32],"their":[33],"ability":[34],"capture":[36],"diverse":[37],"patterns":[38],"of":[39,190],"anomalies,":[40],"particularly":[41],"those":[42],"that":[43,60,143,171],"are":[44],"either":[45],"short-lived":[46],"or":[47,149],"gradually":[48],"emerging.":[49],"In":[50],"this":[51],"work,":[52],"we":[53,113],"introduce":[54],"novel":[56],"multi-snapshot":[57],"sketching":[58],"framework":[59,173],"efficiently":[61],"identifies":[62],"both":[63,179],"edge-level":[64],"structural":[66],"anomalies":[67],"high-velocity":[69],"graph":[70,202],"streams.":[71],"Our":[72,185],"method":[73],"leverages":[74],"multiple":[75,93],"concurrent":[76],"Count":[77],"Sketch":[78],"data":[79],"structures,":[80],"each":[81],"operating":[82],"under":[83],"distinct":[85],"schedule,":[88],"enabling":[89],"robust":[90],"across":[92,134],"timescales.":[94],"This":[95],"lightweight":[96],"memory-efficient":[98],"design":[99],"ensures":[100],"constant":[101],"time":[102],"complexity":[103],"per":[104],"update":[105],"well-suited":[108],"for":[109,196],"real-time":[110],"applications.":[111],"Additionally,":[112],"propose":[114],"principled":[116],"unsupervised":[117],"optimization":[118],"strategy":[119],"using":[120],"Bayesian":[121],"Optimization":[122],"automatically":[124],"tune":[125],"factors":[127],"combination":[129],"weights,":[130],"thereby":[131],"enhancing":[132],"adaptability":[133],"different":[135],"datasets":[136,169],"without":[137],"manual":[138],"tuning.":[139],"Unlike":[140],"prior":[141],"methods":[142,177],"rely":[144],"heavily":[145],"on":[146,166],"deep":[147],"models":[148],"fixed":[150],"assumptions,":[152],"our":[153,172],"approach":[154],"combines":[155],"theoretical":[156],"robustness,":[157],"low":[158],"computational":[159],"overhead,":[160],"practical":[162],"scalability.":[163],"Extensive":[164],"experiments":[165],"the":[167,188],"benchmark":[168],"demonstrate":[170],"consistently":[174],"outperforms":[175],"state-of-the-art":[176],"accuracy":[181],"runtime":[183],"efficiency.":[184],"results":[186],"underscore":[187],"effectiveness":[189],"multitimescale":[191],"modeling":[192],"sketch-based":[194],"approximations":[195],"streaming":[197],"anomaly":[198],"environments.":[203]},"counts_by_year":[],"updated_date":"2026-03-13T14:20:09.374765","created_date":"2026-03-12T00:00:00"}
