{"id":"https://openalex.org/W4379927591","doi":"https://doi.org/10.1145/3616855.3635767","title":"GAD-NR: Graph Anomaly Detection via Neighborhood Reconstruction","display_name":"GAD-NR: Graph Anomaly Detection via Neighborhood Reconstruction","publication_year":2024,"publication_date":"2024-03-04","ids":{"openalex":"https://openalex.org/W4379927591","doi":"https://doi.org/10.1145/3616855.3635767","pmid":"https://pubmed.ncbi.nlm.nih.gov/40018365"},"language":"en","primary_location":{"id":"doi:10.1145/3616855.3635767","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3616855.3635767","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3616855.3635767","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 17th ACM International Conference on Web Search and Data Mining","raw_type":"proceedings-article"},"type":"preprint","indexed_in":["arxiv","crossref","pubmed"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://dl.acm.org/doi/pdf/10.1145/3616855.3635767","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5075235854","display_name":"Amit Roy","orcid":"https://orcid.org/0000-0003-3037-8920"},"institutions":[{"id":"https://openalex.org/I219193219","display_name":"Purdue University West Lafayette","ror":"https://ror.org/02dqehb95","country_code":"US","type":"education","lineage":["https://openalex.org/I219193219"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Amit Roy","raw_affiliation_strings":["Purdue University, West Lafayette, USA"],"affiliations":[{"raw_affiliation_string":"Purdue University, West Lafayette, USA","institution_ids":["https://openalex.org/I219193219"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5059009894","display_name":"Juan Shu","orcid":"https://orcid.org/0009-0004-4967-8565"},"institutions":[{"id":"https://openalex.org/I219193219","display_name":"Purdue University West Lafayette","ror":"https://ror.org/02dqehb95","country_code":"US","type":"education","lineage":["https://openalex.org/I219193219"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Juan Shu","raw_affiliation_strings":["Purdue University, West Lafayette, USA"],"affiliations":[{"raw_affiliation_string":"Purdue University, West Lafayette, USA","institution_ids":["https://openalex.org/I219193219"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100405697","display_name":"Jia Li","orcid":"https://orcid.org/0000-0002-6362-4385"},"institutions":[{"id":"https://openalex.org/I200769079","display_name":"Hong Kong University of Science and Technology","ror":"https://ror.org/00q4vv597","country_code":"HK","type":"education","lineage":["https://openalex.org/I200769079"]}],"countries":["HK"],"is_corresponding":false,"raw_author_name":"Jia Li","raw_affiliation_strings":["Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong"],"affiliations":[{"raw_affiliation_string":"Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong","institution_ids":["https://openalex.org/I200769079"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5006897094","display_name":"Carl Yang","orcid":"https://orcid.org/0000-0001-9145-4531"},"institutions":[{"id":"https://openalex.org/I150468666","display_name":"Emory University","ror":"https://ror.org/03czfpz43","country_code":"US","type":"education","lineage":["https://openalex.org/I150468666"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Carl Yang","raw_affiliation_strings":["Emory University, Atlanta, USA"],"affiliations":[{"raw_affiliation_string":"Emory University, Atlanta, USA","institution_ids":["https://openalex.org/I150468666"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5066486687","display_name":"Olivier Elshocht","orcid":"https://orcid.org/0009-0007-7659-3526"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Olivier Elshocht","raw_affiliation_strings":["Sony R&D Center Brussels Laboratory, Zaventem, Belgium"],"affiliations":[{"raw_affiliation_string":"Sony R&D Center Brussels Laboratory, Zaventem, Belgium","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5102773779","display_name":"Jeroen B. J. Smeets","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Jeroen Smeets","raw_affiliation_strings":["Sony R&D Center Brussels Laboratory, Zaventem, Belgium"],"affiliations":[{"raw_affiliation_string":"Sony R&D Center Brussels Laboratory, Zaventem, Belgium","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100455219","display_name":"Pan Li","orcid":"https://orcid.org/0000-0003-3742-0845"},"institutions":[{"id":"https://openalex.org/I130701444","display_name":"Georgia Institute of Technology","ror":"https://ror.org/01zkghx44","country_code":"US","type":"education","lineage":["https://openalex.org/I130701444"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Pan Li","raw_affiliation_strings":["Georgia Institute of Technology, Atlanta, USA"],"affiliations":[{"raw_affiliation_string":"Georgia Institute of Technology, Atlanta, USA","institution_ids":["https://openalex.org/I130701444"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":7,"corresponding_author_ids":["https://openalex.org/A5075235854"],"corresponding_institution_ids":["https://openalex.org/I219193219"],"apc_list":null,"apc_paid":null,"fwci":24.1876,"has_fulltext":true,"cited_by_count":71,"citation_normalized_percentile":{"value":0.99653413,"is_in_top_1_percent":true,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":99,"max":100},"biblio":{"volume":"2024","issue":null,"first_page":"576","last_page":"585"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9998999834060669,"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":0.9998999834060669,"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":0.9987000226974487,"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/T11273","display_name":"Advanced Graph Neural Networks","score":0.9980999827384949,"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":0.5915465354919434},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5285338759422302},{"id":"https://openalex.org/keywords/anomaly","display_name":"Anomaly (physics)","score":0.45016252994537354},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.353087455034256},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.34166380763053894},{"id":"https://openalex.org/keywords/physics","display_name":"Physics","score":0.1438932716846466}],"concepts":[{"id":"https://openalex.org/C739882","wikidata":"https://www.wikidata.org/wiki/Q3560506","display_name":"Anomaly detection","level":2,"score":0.5915465354919434},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5285338759422302},{"id":"https://openalex.org/C12997251","wikidata":"https://www.wikidata.org/wiki/Q567560","display_name":"Anomaly (physics)","level":2,"score":0.45016252994537354},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.353087455034256},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.34166380763053894},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.1438932716846466},{"id":"https://openalex.org/C26873012","wikidata":"https://www.wikidata.org/wiki/Q214781","display_name":"Condensed matter physics","level":1,"score":0.0}],"mesh":[],"locations_count":5,"locations":[{"id":"doi:10.1145/3616855.3635767","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3616855.3635767","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3616855.3635767","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 17th ACM International Conference on Web Search and Data Mining","raw_type":"proceedings-article"},{"id":"pmid:40018365","is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/40018365","pdf_url":null,"source":{"id":"https://openalex.org/S4306525036","display_name":"PubMed","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the ... International Conference on Web Search & Data Mining. International Conference on Web Search & Data Mining","raw_type":null},{"id":"pmh:oai:arXiv.org:2306.01951","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2306.01951","pdf_url":"https://arxiv.org/pdf/2306.01951","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},{"id":"pmh:oai:pubmedcentral.nih.gov:11867731","is_oa":true,"landing_page_url":"https://www.ncbi.nlm.nih.gov/pmc/articles/11867731","pdf_url":"https://pmc.ncbi.nlm.nih.gov/articles/PMC11867731/pdf/nihms-2058355.pdf","source":{"id":"https://openalex.org/S2764455111","display_name":"PubMed Central","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Proc Int Conf Web Search Data Min","raw_type":"Text"},{"id":"pmh:oai:repository.hkust.edu.hk:1783.1-138499","is_oa":false,"landing_page_url":"http://repository.hkust.edu.hk/ir/Record/1783.1-138499","pdf_url":null,"source":{"id":"https://openalex.org/S4306401796","display_name":"Rare & Special e-Zone (The Hong Kong University of Science and Technology)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I200769079","host_organization_name":"Hong Kong University of Science and Technology","host_organization_lineage":["https://openalex.org/I200769079"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Conference paper"}],"best_oa_location":{"id":"doi:10.1145/3616855.3635767","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3616855.3635767","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3616855.3635767","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 17th ACM International Conference on Web Search and Data Mining","raw_type":"proceedings-article"},"sustainable_development_goals":[{"display_name":"Peace, Justice and strong institutions","id":"https://metadata.un.org/sdg/16","score":0.7300000190734863}],"awards":[],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4379927591.pdf","grobid_xml":"https://content.openalex.org/works/W4379927591.grobid-xml"},"referenced_works_count":76,"referenced_works":["https://openalex.org/W113059239","https://openalex.org/W1492581097","https://openalex.org/W1567226275","https://openalex.org/W1888005072","https://openalex.org/W1964274671","https://openalex.org/W1969288392","https://openalex.org/W1975223096","https://openalex.org/W1978054023","https://openalex.org/W1995443851","https://openalex.org/W2026565928","https://openalex.org/W2027287130","https://openalex.org/W2061122559","https://openalex.org/W2089554624","https://openalex.org/W2108898793","https://openalex.org/W2112429379","https://openalex.org/W2112837588","https://openalex.org/W2119850747","https://openalex.org/W2127979711","https://openalex.org/W2139688603","https://openalex.org/W2154851992","https://openalex.org/W2170902455","https://openalex.org/W2224609888","https://openalex.org/W2348679751","https://openalex.org/W2402531259","https://openalex.org/W2466279936","https://openalex.org/W2519887557","https://openalex.org/W2624431344","https://openalex.org/W2751074899","https://openalex.org/W2788579977","https://openalex.org/W2808544127","https://openalex.org/W2904341359","https://openalex.org/W2906836970","https://openalex.org/W2944250323","https://openalex.org/W2945996535","https://openalex.org/W2962756421","https://openalex.org/W2963893312","https://openalex.org/W2968406604","https://openalex.org/W2991305957","https://openalex.org/W2992818769","https://openalex.org/W2997501961","https://openalex.org/W2998336824","https://openalex.org/W3005292633","https://openalex.org/W3009901425","https://openalex.org/W3015799890","https://openalex.org/W3035298482","https://openalex.org/W3048072497","https://openalex.org/W3068123808","https://openalex.org/W3093664513","https://openalex.org/W3099064659","https://openalex.org/W3102969158","https://openalex.org/W3104097132","https://openalex.org/W3110547369","https://openalex.org/W3127584905","https://openalex.org/W3133518153","https://openalex.org/W3153085126","https://openalex.org/W3158418994","https://openalex.org/W3171259348","https://openalex.org/W3171903345","https://openalex.org/W3201293162","https://openalex.org/W3202177660","https://openalex.org/W4205143667","https://openalex.org/W4213147383","https://openalex.org/W4221150215","https://openalex.org/W4241522665","https://openalex.org/W4281655877","https://openalex.org/W4285066127","https://openalex.org/W4290877309","https://openalex.org/W4306813287","https://openalex.org/W4327656380","https://openalex.org/W4375798894","https://openalex.org/W4381714210","https://openalex.org/W6657096890","https://openalex.org/W6664745863","https://openalex.org/W6676587170","https://openalex.org/W6683820043","https://openalex.org/W6736685754"],"related_works":["https://openalex.org/W2806741695","https://openalex.org/W4290647774","https://openalex.org/W3189286258","https://openalex.org/W3207797160","https://openalex.org/W3210364259","https://openalex.org/W4300558037","https://openalex.org/W2667207928","https://openalex.org/W2912112202","https://openalex.org/W4377864969","https://openalex.org/W2033914206"],"abstract_inverted_index":{"Graph":[0,35],"Anomaly":[1],"Detection":[2],"(GAD)":[3],"is":[4,34,202],"a":[5,85,113,118,139],"technique":[6],"used":[7],"to":[8,105,133,190],"identify":[9,46],"abnormal":[10],"nodes":[11,73,163],"within":[12],"graphs,":[13],"finding":[14],"applications":[15],"in":[16,72,75,80,192,217],"network":[17],"security,":[18],"fraud":[19],"detection,":[20,24],"social":[21],"media":[22],"spam":[23],"and":[25,45,146,164],"various":[26],"other":[27],"domains.":[28],"A":[29],"common":[30],"method":[31],"for":[32,67,127,200],"GAD":[33],"Auto-Encoders":[36],"(GAEs),":[37],"which":[38],"encode":[39],"graph":[40,77,128],"data":[41],"into":[42],"node":[43,153],"representations":[44],"anomalies":[47,93,100,224,241],"by":[48],"assessing":[49],"the":[50,54,76,81,135,142,151,157,181,206,211,227,243],"reconstruction":[51,126,159],"quality":[52],"of":[53,121,138,183,223,226,240],"graphs":[55],"based":[56,149],"on":[57,150,176],"these":[58],"representations.":[59],"However,":[60],"existing":[61,212],"GAE":[62,122],"models":[63],"are":[64],"primarily":[65],"optimized":[66],"direct":[68],"link":[69],"reconstruction,":[70],"resulting":[71],"connected":[74],"being":[78],"clustered":[79],"latent":[82],"space.":[83],"As":[84],"result,":[86],"they":[87],"excel":[88],"at":[89,235],"detecting":[90,218,236],"cluster-type":[91],"structural":[92,99],"but":[94],"struggle":[95],"with":[96],"more":[97],"complex":[98],"that":[101,123,210],"do":[102],"not":[103],"conform":[104],"clusters.":[106],"To":[107],"address":[108],"this":[109],"limitation,":[110],"we":[111],"propose":[112],"novel":[114],"solution":[115],"called":[116],"GAD-NR,":[117,184],"new":[119],"variant":[120],"incorporates":[124],"neighborhood":[125,137,158],"anomaly":[129,248],"detection.":[130],"GAD-NR":[131,167,201,233],"aims":[132],"reconstruct":[134],"entire":[136],"node,":[140],"encompassing":[141],"local":[143],"structure,":[144],"self-attributes,":[145],"neighbor":[147],"attributes,":[148],"corresponding":[152],"representation.":[154],"By":[155],"comparing":[156],"loss":[160],"between":[161],"anomalous":[162],"normal":[165],"nodes,":[166],"can":[168],"effectively":[169],"detect":[170],"any":[171],"anomalies.":[172],"Extensive":[173],"experimentation":[174],"conducted":[175],"six":[177],"real-world":[178],"datasets":[179],"validates":[180],"effectiveness":[182],"showcasing":[185],"significant":[186],"improvements":[187],"(by":[188],"up":[189],"30%\u2191":[191],"AUC)":[193],"over":[194],"state-of-the-art":[195],"competitors.":[196],"The":[197],"source":[198],"code":[199],"openly":[203],"available.":[204],"Importantly,":[205],"comparative":[207],"analysis":[208],"reveals":[209],"methods":[213],"perform":[214],"well":[215],"only":[216],"one":[219],"or":[220],"two":[221],"types":[222,229,239],"out":[225],"three":[228,238],"studied.":[230],"In":[231],"contrast,":[232],"excels":[234],"all":[237],"across":[242],"datasets,":[244],"demonstrating":[245],"its":[246],"comprehensive":[247],"detection":[249],"capabilities.":[250]},"counts_by_year":[{"year":2026,"cited_by_count":10},{"year":2025,"cited_by_count":53},{"year":2024,"cited_by_count":8}],"updated_date":"2026-04-10T15:06:20.359241","created_date":"2025-10-10T00:00:00"}