{"id":"https://openalex.org/W7138077422","doi":"https://doi.org/10.1609/aaai.v40i17.38510","title":"BAG: Benchmarking Anomaly Detection on Dynamic Graphs","display_name":"BAG: Benchmarking Anomaly Detection on Dynamic Graphs","publication_year":2026,"publication_date":"2026-03-14","ids":{"openalex":"https://openalex.org/W7138077422","doi":"https://doi.org/10.1609/aaai.v40i17.38510"},"language":null,"primary_location":{"id":"doi:10.1609/aaai.v40i17.38510","is_oa":true,"landing_page_url":"https://doi.org/10.1609/aaai.v40i17.38510","pdf_url":null,"source":{"id":"https://openalex.org/S4210191458","display_name":"Proceedings of the AAAI Conference on Artificial Intelligence","issn_l":"2159-5399","issn":["2159-5399","2374-3468"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/P4310320058","host_organization_name":"Association for the Advancement of Artificial Intelligence","host_organization_lineage":["https://openalex.org/P4310320058"],"host_organization_lineage_names":["Association for the Advancement of Artificial Intelligence"],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the AAAI Conference on Artificial Intelligence","raw_type":"journal-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"diamond","oa_url":"https://doi.org/10.1609/aaai.v40i17.38510","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5088224908","display_name":"Fengrui Hua","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Fengrui Hua","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5129743072","display_name":"Yiyan Qi","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yiyan Qi","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5129705946","display_name":"Zikai Wei","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zikai Wei","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5111065404","display_name":"Yuxing Tian","orcid":"https://orcid.org/0009-0008-0313-5089"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yuxing Tian","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5129652937","display_name":"Chengjin Xu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chengjin Xu","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5129743337","display_name":"Xiaojun Wu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Xiaojun Wu","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5129670449","display_name":"Jia Li","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Jia Li","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5129684673","display_name":"Jian Guo","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Jian Guo","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":8,"corresponding_author_ids":["https://openalex.org/A5088224908"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.29855538,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"40","issue":"17","first_page":"14892","last_page":"14900"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11273","display_name":"Advanced Graph Neural Networks","score":0.6412000060081482,"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":0.6412000060081482,"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":0.07150000333786011,"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":0.03550000116229057,"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/benchmarking","display_name":"Benchmarking","score":0.8600000143051147},{"id":"https://openalex.org/keywords/anomaly-detection","display_name":"Anomaly detection","score":0.7224000096321106},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.5837000012397766},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.5672000050544739},{"id":"https://openalex.org/keywords/metric","display_name":"Metric (unit)","score":0.4684999883174896},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.376800000667572},{"id":"https://openalex.org/keywords/dynamic-network-analysis","display_name":"Dynamic network analysis","score":0.3492000102996826},{"id":"https://openalex.org/keywords/field","display_name":"Field (mathematics)","score":0.3402000069618225}],"concepts":[{"id":"https://openalex.org/C86251818","wikidata":"https://www.wikidata.org/wiki/Q816754","display_name":"Benchmarking","level":2,"score":0.8600000143051147},{"id":"https://openalex.org/C739882","wikidata":"https://www.wikidata.org/wiki/Q3560506","display_name":"Anomaly detection","level":2,"score":0.7224000096321106},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7160999774932861},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.5837000012397766},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.5672000050544739},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.5194000005722046},{"id":"https://openalex.org/C176217482","wikidata":"https://www.wikidata.org/wiki/Q860554","display_name":"Metric (unit)","level":2,"score":0.4684999883174896},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.45910000801086426},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.382999986410141},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.376800000667572},{"id":"https://openalex.org/C13540734","wikidata":"https://www.wikidata.org/wiki/Q5318996","display_name":"Dynamic network analysis","level":2,"score":0.3492000102996826},{"id":"https://openalex.org/C9652623","wikidata":"https://www.wikidata.org/wiki/Q190109","display_name":"Field (mathematics)","level":2,"score":0.3402000069618225},{"id":"https://openalex.org/C62611344","wikidata":"https://www.wikidata.org/wiki/Q1062658","display_name":"Node (physics)","level":2,"score":0.30869999527931213},{"id":"https://openalex.org/C106937863","wikidata":"https://www.wikidata.org/wiki/Q7236518","display_name":"Power graph analysis","level":3,"score":0.3077000081539154},{"id":"https://openalex.org/C88230418","wikidata":"https://www.wikidata.org/wiki/Q131476","display_name":"Graph theory","level":2,"score":0.29580000042915344},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.29409998655319214},{"id":"https://openalex.org/C37404715","wikidata":"https://www.wikidata.org/wiki/Q380679","display_name":"Dynamic programming","level":2,"score":0.2924000024795532},{"id":"https://openalex.org/C12997251","wikidata":"https://www.wikidata.org/wiki/Q567560","display_name":"Anomaly (physics)","level":2,"score":0.28610000014305115},{"id":"https://openalex.org/C197298091","wikidata":"https://www.wikidata.org/wiki/Q5318963","display_name":"Dynamic data","level":2,"score":0.2653999924659729},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.25769999623298645},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.25360000133514404},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.2535000145435333}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1609/aaai.v40i17.38510","is_oa":true,"landing_page_url":"https://doi.org/10.1609/aaai.v40i17.38510","pdf_url":null,"source":{"id":"https://openalex.org/S4210191458","display_name":"Proceedings of the AAAI Conference on Artificial Intelligence","issn_l":"2159-5399","issn":["2159-5399","2374-3468"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/P4310320058","host_organization_name":"Association for the Advancement of Artificial Intelligence","host_organization_lineage":["https://openalex.org/P4310320058"],"host_organization_lineage_names":["Association for the Advancement of Artificial Intelligence"],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the AAAI Conference on Artificial Intelligence","raw_type":"journal-article"}],"best_oa_location":{"id":"doi:10.1609/aaai.v40i17.38510","is_oa":true,"landing_page_url":"https://doi.org/10.1609/aaai.v40i17.38510","pdf_url":null,"source":{"id":"https://openalex.org/S4210191458","display_name":"Proceedings of the AAAI Conference on Artificial Intelligence","issn_l":"2159-5399","issn":["2159-5399","2374-3468"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/P4310320058","host_organization_name":"Association for the Advancement of Artificial Intelligence","host_organization_lineage":["https://openalex.org/P4310320058"],"host_organization_lineage_names":["Association for the Advancement of Artificial Intelligence"],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the AAAI Conference on Artificial Intelligence","raw_type":"journal-article"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/9","display_name":"Industry, innovation and infrastructure","score":0.40365180373191833}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Anomaly":[0,45],"detection":[1,66],"in":[2,29,137,140,204],"dynamic":[3,68,141],"graphs":[4],"is":[5,125],"a":[6,38,115],"critical":[7],"area":[8],"of":[9,74,170,185],"research":[10],"that":[11,21,95,126,155],"focuses":[12],"on":[13,67],"identifying":[14],"abnormal":[15],"components":[16],"within":[17],"evolving":[18],"graph":[19,142],"structures":[20],"deviate":[22],"significantly":[23,181],"from":[24],"typical":[25],"patterns.":[26],"Despite":[27],"advancements":[28],"traditional":[30],"temporal":[31,171],"pattern":[32],"mining":[33],"and":[34,83,99,111,135,173,194,201,212],"deep":[35],"learning":[36],"techniques,":[37],"comprehensive":[39,60],"benchmarking":[40],"framework":[41,104],"for":[42,64],"Dynamic":[43,85,128,147],"Graph":[44,86,129,148],"Detection":[46],"(DyGAD)":[47],"has":[48],"been":[49],"lacking.":[50],"To":[51],"address":[52],"this":[53,205],"gap,":[54],"we":[55],"introduce":[56],"BAG,":[57],"the":[58,109,152,168,183],"first":[59],"benchmark":[61,180],"specifically":[62],"designed":[63],"anomaly":[65],"graphs.":[69],"BAG":[70,179,198],"enables":[71],"extensive":[72],"evaluation":[73,184,213],"25":[75],"leading":[76],"DyGAD":[77,120,186],"models,":[78,210],"covering":[79],"both":[80,97,108],"classical":[81],"approaches":[82],"advanced":[84],"Neural":[87],"Networks":[88],"(DGNNs),":[89],"across":[90],"10":[91],"diverse":[92],"real-world":[93],"datasets":[94],"include":[96],"synthetic":[98],"naturally":[100],"occurring":[101],"anomalies.":[102],"The":[103,177],"supports":[105,199],"evaluations":[106],"at":[107,161],"edge":[110],"node":[112],"levels,":[113],"offering":[114],"robust":[116],"tool":[117],"to":[118,145,167],"advance":[119],"research.":[121],"Our":[122],"main":[123],"finding":[124],"Continuous-time":[127],"(CTDG)":[130],"models":[131],"demonstrate":[132],"superior":[133],"performance":[134],"potential":[136],"detecting":[138,162],"anomalies":[139,172],"edges,":[143],"compared":[144],"Discrete-time":[146],"(DTDG)":[149],"models.":[150],"Furthermore,":[151],"results":[153],"reveal":[154],"existing":[156],"methods":[157,187],"are":[158],"less":[159],"effective":[160],"organic":[163],"anomalies,":[164],"primarily":[165],"due":[166],"presence":[169],"highly":[174],"imbalanced":[175],"samples.":[176],"proposed":[178],"enhances":[182],"by":[188,207],"improving":[189],"dataset":[190],"selection,":[191],"metric":[192],"application,":[193],"model":[195],"training.":[196],"Moreover,":[197],"reproducibility":[200],"further":[202],"exploration":[203],"field":[206],"integrating":[208],"all":[209],"datasets,":[211],"protocols":[214],"into":[215],"an":[216],"open-source":[217],"repository.":[218]},"counts_by_year":[],"updated_date":"2026-03-18T06:31:55.123368","created_date":"2026-03-18T00:00:00"}