{"id":"https://openalex.org/W7155527273","doi":"https://doi.org/10.1145/3799830.3799843","title":"Flex-GAD : Flexible Graph Anomaly Detection","display_name":"Flex-GAD : Flexible Graph Anomaly Detection","publication_year":2025,"publication_date":"2025-12-17","ids":{"openalex":"https://openalex.org/W7155527273","doi":"https://doi.org/10.1145/3799830.3799843"},"language":null,"primary_location":{"id":"doi:10.1145/3799830.3799843","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3799830.3799843","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 13th ACM IKDD International Conference on Data Science","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.1145/3799830.3799843","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5134484112","display_name":"Apu Chakraborty","orcid":"https://orcid.org/0009-0006-0821-3272"},"institutions":[{"id":"https://openalex.org/I4210121466","display_name":"Indian Institute of Technology Bhilai","ror":"https://ror.org/02sscsx71","country_code":"IN","type":"education","lineage":["https://openalex.org/I4210121466"]}],"countries":["IN"],"is_corresponding":true,"raw_author_name":"Apu Chakraborty","raw_affiliation_strings":["IIT Bhilai, Durg, Chhattisgarh, India"],"raw_orcid":"https://orcid.org/0009-0006-0821-3272","affiliations":[{"raw_affiliation_string":"IIT Bhilai, Durg, Chhattisgarh, India","institution_ids":["https://openalex.org/I4210121466"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5103092135","display_name":"Anshul Kumar","orcid":"https://orcid.org/0000-0002-4703-043X"},"institutions":[{"id":"https://openalex.org/I4210121466","display_name":"Indian Institute of Technology Bhilai","ror":"https://ror.org/02sscsx71","country_code":"IN","type":"education","lineage":["https://openalex.org/I4210121466"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Anshul Kumar","raw_affiliation_strings":["IIT Bhilai, Durg, Chhattisgarh, India"],"raw_orcid":"https://orcid.org/0009-0001-4230-8877","affiliations":[{"raw_affiliation_string":"IIT Bhilai, Durg, Chhattisgarh, India","institution_ids":["https://openalex.org/I4210121466"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5101803496","display_name":"Gagan Raj Gupta","orcid":"https://orcid.org/0000-0002-8568-2949"},"institutions":[{"id":"https://openalex.org/I4210121466","display_name":"Indian Institute of Technology Bhilai","ror":"https://ror.org/02sscsx71","country_code":"IN","type":"education","lineage":["https://openalex.org/I4210121466"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Gagan Raj Gupta","raw_affiliation_strings":["IIT Bhilai, Durg, Chhattisgarh, India"],"raw_orcid":"https://orcid.org/0000-0002-8568-2949","affiliations":[{"raw_affiliation_string":"IIT Bhilai, Durg, Chhattisgarh, India","institution_ids":["https://openalex.org/I4210121466"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5134484112"],"corresponding_institution_ids":["https://openalex.org/I4210121466"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.86657352,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"115","last_page":"123"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11273","display_name":"Advanced Graph Neural Networks","score":0.9409000277519226,"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":0.9409000277519226,"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":0.015799999237060547,"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"}},{"id":"https://openalex.org/T10887","display_name":"Bioinformatics and Genomic Networks","score":0.0066999997943639755,"subfield":{"id":"https://openalex.org/subfields/1312","display_name":"Molecular Biology"},"field":{"id":"https://openalex.org/fields/13","display_name":"Biochemistry, Genetics and Molecular Biology"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/anomaly-detection","display_name":"Anomaly detection","score":0.6506999731063843},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.6313999891281128},{"id":"https://openalex.org/keywords/node","display_name":"Node (physics)","score":0.5651000142097473},{"id":"https://openalex.org/keywords/encoder","display_name":"Encoder","score":0.5647000074386597},{"id":"https://openalex.org/keywords/weighting","display_name":"Weighting","score":0.5213000178337097},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.462799996137619}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6923999786376953},{"id":"https://openalex.org/C739882","wikidata":"https://www.wikidata.org/wiki/Q3560506","display_name":"Anomaly detection","level":2,"score":0.6506999731063843},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.6313999891281128},{"id":"https://openalex.org/C62611344","wikidata":"https://www.wikidata.org/wiki/Q1062658","display_name":"Node (physics)","level":2,"score":0.5651000142097473},{"id":"https://openalex.org/C118505674","wikidata":"https://www.wikidata.org/wiki/Q42586063","display_name":"Encoder","level":2,"score":0.5647000074386597},{"id":"https://openalex.org/C183115368","wikidata":"https://www.wikidata.org/wiki/Q856577","display_name":"Weighting","level":2,"score":0.5213000178337097},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.46810001134872437},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.462799996137619},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.4239000082015991},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.39559999108314514},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.3952000141143799},{"id":"https://openalex.org/C2780598303","wikidata":"https://www.wikidata.org/wiki/Q65921492","display_name":"Flexibility (engineering)","level":2,"score":0.3675999939441681},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3569999933242798},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.31040000915527344},{"id":"https://openalex.org/C12997251","wikidata":"https://www.wikidata.org/wiki/Q567560","display_name":"Anomaly (physics)","level":2,"score":0.27399998903274536},{"id":"https://openalex.org/C106301342","wikidata":"https://www.wikidata.org/wiki/Q4117933","display_name":"Entropy (arrow of time)","level":2,"score":0.2565999925136566}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3799830.3799843","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3799830.3799843","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 13th ACM IKDD International Conference on Data Science","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.1145/3799830.3799843","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3799830.3799843","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 13th ACM IKDD International Conference on Data Science","raw_type":"proceedings-article"},"sustainable_development_goals":[{"score":0.7310444116592407,"id":"https://metadata.un.org/sdg/16","display_name":"Peace, Justice and strong institutions"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":15,"referenced_works":["https://openalex.org/W1995443851","https://openalex.org/W2134008243","https://openalex.org/W2741114205","https://openalex.org/W2808544127","https://openalex.org/W2944250323","https://openalex.org/W2997671625","https://openalex.org/W2998336824","https://openalex.org/W3048072497","https://openalex.org/W3093664513","https://openalex.org/W3133518153","https://openalex.org/W4205942400","https://openalex.org/W4221095033","https://openalex.org/W4254182148","https://openalex.org/W4285066127","https://openalex.org/W4379927591"],"related_works":[],"abstract_inverted_index":{"Detecting":[0],"anomalous":[1],"nodes":[2],"in":[3,27],"attributed":[4,135],"networks,":[5,32],"where":[6],"each":[7],"node":[8,52,80,124,140],"is":[9,18],"associated":[10],"with":[11,87,137],"both":[12],"structural":[13,84],"connections":[14],"and":[15,24,36,76,107,142,162,183],"descriptive":[16],"attributes,":[17],"essential":[19],"for":[20,46],"identifying":[21],"fraud,":[22],"misinformation,":[23],"suspicious":[25],"behavior":[26],"domains":[28],"such":[29],"as":[30],"social":[31],"academic":[33],"citation":[34],"graphs,":[35],"e-commerce":[37],"platforms.":[38],"We":[39,129],"propose":[40],"Flex-GAD,":[41],"a":[42,68,88,98],"novel":[43,69],"unsupervised":[44],"framework":[45,66],"graph":[47,63,166],"anomaly":[48],"detection":[49],"at":[50],"the":[51,111,121,154],"level.":[53],"Flex-GAD":[54,131,145],"integrates":[55],"two":[56],"encoders":[57],"to":[58,73],"capture":[59],"complementary":[60],"aspects":[61],"of":[62,110,120,151],"data.":[64],"The":[65],"incorporates":[67],"community-based":[70],"GCN":[71],"encoder":[72],"model":[74],"intra-community":[75],"inter-community":[77],"information":[78],"into":[79],"embeddings,":[81],"thereby":[82],"ensuring":[83],"consistency,":[85],"along":[86],"standard":[89],"attribute":[90,143],"encoder.":[91],"These":[92],"diverse":[93,165],"representations":[94],"are":[95],"fused":[96],"using":[97],"self-attention-based":[99],"representation":[100,125],"fusion":[101,115],"module,":[102],"which":[103],"enables":[104],"adaptive":[105],"weighting":[106],"effective":[108],"integration":[109],"encoded":[112],"information.":[113],"This":[114],"mechanism":[116],"allows":[117],"automatic":[118],"emphasis":[119],"most":[122],"relevant":[123],"across":[126,164,193],"different":[127],"encoders.":[128],"evaluate":[130],"on":[132,191],"seven":[133,194],"real-world":[134],"graphs":[136],"varying":[138],"sizes,":[139],"degrees,":[141],"homogeneity.":[144],"achieves":[146],"an":[147],"average":[148,192],"AUC":[149],"improvement":[150],"7.98%":[152],"over":[153],"previously":[155],"best-performing":[156],"method,":[157],"GAD-NR,":[158],"demonstrating":[159],"its":[160],"effectiveness":[161],"flexibility":[163],"structures.":[167],"Moreover,":[168],"it":[169],"significantly":[170],"reduces":[171],"training":[172],"time,":[173],"running":[174],"102":[175],"\u00d7":[176,185],"faster":[177,186],"per":[178,187],"epoch":[179,188],"than":[180,189],"Anomaly":[181],"DAE":[182],"3":[184],"GAD-NR":[190],"benchmark":[195],"datasets.":[196]},"counts_by_year":[],"updated_date":"2026-04-25T06:06:54.107920","created_date":"2026-04-25T00:00:00"}