{"id":"https://openalex.org/W4414360053","doi":"https://doi.org/10.24963/ijcai.2025/405","title":"GCTAM: Global and Contextual Truncated Affinity Combined Maximization Model For Unsupervised Graph Anomaly Detection","display_name":"GCTAM: Global and Contextual Truncated Affinity Combined Maximization Model For Unsupervised Graph Anomaly Detection","publication_year":2025,"publication_date":"2025-09-01","ids":{"openalex":"https://openalex.org/W4414360053","doi":"https://doi.org/10.24963/ijcai.2025/405"},"language":"en","primary_location":{"id":"doi:10.24963/ijcai.2025/405","is_oa":false,"landing_page_url":"https://doi.org/10.24963/ijcai.2025/405","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence","raw_type":"proceedings-article"},"type":"conference-paper","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/A5077208736","display_name":"Xiong Zhang","orcid":null},"institutions":[{"id":"https://openalex.org/I189210763","display_name":"Yunnan University","ror":"https://ror.org/0040axw97","country_code":"CN","type":"education","lineage":["https://openalex.org/I189210763"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiong Zhang","raw_affiliation_strings":["Yunnan University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Yunnan University","institution_ids":["https://openalex.org/I189210763"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100664886","display_name":"Hong Peng","orcid":"https://orcid.org/0000-0001-6921-0637"},"institutions":[{"id":"https://openalex.org/I189210763","display_name":"Yunnan University","ror":"https://ror.org/0040axw97","country_code":"CN","type":"education","lineage":["https://openalex.org/I189210763"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Hong Peng","raw_affiliation_strings":["Yunnan University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Yunnan University","institution_ids":["https://openalex.org/I189210763"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101623518","display_name":"Zhenli He","orcid":"https://orcid.org/0000-0002-7986-2222"},"institutions":[{"id":"https://openalex.org/I189210763","display_name":"Yunnan University","ror":"https://ror.org/0040axw97","country_code":"CN","type":"education","lineage":["https://openalex.org/I189210763"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhenli He","raw_affiliation_strings":["Yunnan University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Yunnan University","institution_ids":["https://openalex.org/I189210763"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5044796317","display_name":"Cheng Xie","orcid":"https://orcid.org/0000-0002-4484-7428"},"institutions":[{"id":"https://openalex.org/I189210763","display_name":"Yunnan University","ror":"https://ror.org/0040axw97","country_code":"CN","type":"education","lineage":["https://openalex.org/I189210763"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Cheng Xie","raw_affiliation_strings":["Yunnan University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Yunnan University","institution_ids":["https://openalex.org/I189210763"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100641350","display_name":"Xin Jin","orcid":"https://orcid.org/0000-0003-2211-2006"},"institutions":[{"id":"https://openalex.org/I189210763","display_name":"Yunnan University","ror":"https://ror.org/0040axw97","country_code":"CN","type":"education","lineage":["https://openalex.org/I189210763"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xin Jin","raw_affiliation_strings":["Yunnan University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Yunnan University","institution_ids":["https://openalex.org/I189210763"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5101770417","display_name":"Hua Jiang","orcid":"https://orcid.org/0000-0003-1101-622X"},"institutions":[{"id":"https://openalex.org/I189210763","display_name":"Yunnan University","ror":"https://ror.org/0040axw97","country_code":"CN","type":"education","lineage":["https://openalex.org/I189210763"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Hua Jiang","raw_affiliation_strings":["Yunnan University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Yunnan University","institution_ids":["https://openalex.org/I189210763"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I189210763"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.21266818,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"3642","last_page":"3650"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11273","display_name":"Advanced Graph Neural Networks","score":0.9898999929428101,"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":0.9898999929428101,"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":0.9847000241279602,"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/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9397000074386597,"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/maximization","display_name":"Maximization","score":0.6341999769210815},{"id":"https://openalex.org/keywords/anomaly-detection","display_name":"Anomaly detection","score":0.6008999943733215},{"id":"https://openalex.org/keywords/limiting","display_name":"Limiting","score":0.5845999717712402},{"id":"https://openalex.org/keywords/anomaly","display_name":"Anomaly (physics)","score":0.579200029373169},{"id":"https://openalex.org/keywords/truncation","display_name":"Truncation (statistics)","score":0.5532000064849854},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.5199999809265137}],"concepts":[{"id":"https://openalex.org/C2776330181","wikidata":"https://www.wikidata.org/wiki/Q18358244","display_name":"Maximization","level":2,"score":0.6341999769210815},{"id":"https://openalex.org/C739882","wikidata":"https://www.wikidata.org/wiki/Q3560506","display_name":"Anomaly detection","level":2,"score":0.6008999943733215},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5922999978065491},{"id":"https://openalex.org/C188198153","wikidata":"https://www.wikidata.org/wiki/Q1613840","display_name":"Limiting","level":2,"score":0.5845999717712402},{"id":"https://openalex.org/C12997251","wikidata":"https://www.wikidata.org/wiki/Q567560","display_name":"Anomaly (physics)","level":2,"score":0.579200029373169},{"id":"https://openalex.org/C106195933","wikidata":"https://www.wikidata.org/wiki/Q7847935","display_name":"Truncation (statistics)","level":2,"score":0.5532000064849854},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.5199999809265137},{"id":"https://openalex.org/C2780283098","wikidata":"https://www.wikidata.org/wiki/Q4688960","display_name":"Affinities","level":2,"score":0.4819999933242798},{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.4571000039577484},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.39169999957084656},{"id":"https://openalex.org/C2164484","wikidata":"https://www.wikidata.org/wiki/Q5170150","display_name":"Core (optical fiber)","level":2,"score":0.32919999957084656},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.32850000262260437},{"id":"https://openalex.org/C182081679","wikidata":"https://www.wikidata.org/wiki/Q1275153","display_name":"Expectation\u2013maximization algorithm","level":3,"score":0.3249000012874603},{"id":"https://openalex.org/C109659709","wikidata":"https://www.wikidata.org/wiki/Q3407504","display_name":"Affinity propagation","level":5,"score":0.30959999561309814},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.30329999327659607},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.2930000126361847}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.24963/ijcai.2025/405","is_oa":false,"landing_page_url":"https://doi.org/10.24963/ijcai.2025/405","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Anomalies":[0],"often":[1],"occur":[2],"in":[3,18,168,189,202],"real-world":[4,159,192],"information":[5,44],"networks/graphs,":[6],"such":[7],"as":[8],"malevolent":[9],"users,":[10,14],"malicious":[11],"comments,":[12],"banned":[13],"and":[15,45,85,99,120,195,206,208],"fake":[16],"news":[17],"social":[19],"graphs.":[20],"The":[21,128],"latest":[22],"graph":[23,170],"anomaly":[24,38,106,171],"detection":[25,172],"methods":[26,72,167],"use":[27,136],"a":[28,78,114],"novel":[29,115],"mechanism":[30],"called":[31],"truncated":[32],"affinity":[33,122,142,151],"maximization":[34],"(TAM)":[35],"to":[36,65,77,123,135,139],"detect":[37],"nodes":[39,56,64,75],"without":[40],"using":[41],"any":[42],"label":[43],"achieve":[46],"impressive":[47],"results.":[48],"TAM":[49],"maximizes":[50],"the":[51,54,59,62,67,83,103,125,132,141,150,181,210,219],"affinities":[52,60,87],"among":[53],"normal":[55,98,153],"while":[57,146,213],"truncating":[58],"of":[61,88,105,131,143,152],"anomalous":[63,100,126,144],"identify":[66],"anomalies.":[68],"However,":[69],"existing":[70],"TAM-based":[71],"truncate":[73,124],"suspicious":[74],"according":[76],"rigid":[79],"threshold":[80],"that":[81,162],"ignores":[82],"specificity":[84],"high-order":[86],"different":[89],"nodes.":[90,127,154],"This":[91],"inevitably":[92],"causes":[93],"inefficient":[94],"truncations":[95],"from":[96],"both":[97],"nodes,":[101,145],"limiting":[102],"effectiveness":[104],"detection.":[107],"To":[108],"this":[109,111],"end,":[110],"paper":[112],"proposes":[113],"truncation":[116,138,148],"model":[117],"combining":[118],"contextual":[119,137],"global":[121,147],"core":[129],"idea":[130],"work":[133],"is":[134],"decrease":[140],"increases":[149],"Extensive":[155],"experiments":[156],"on":[157],"massive":[158],"datasets":[160],"show":[161],"our":[163,198],"method":[164,183,199],"surpasses":[165],"peer":[166],"most":[169,214],"tasks.":[173,220],"In":[174],"highlights,":[175],"compared":[176],"with":[177],"previous":[178,215],"state-of-the-art":[179],"methods,":[180],"proposed":[182],"has":[184],"+15%":[185],"~":[186],"+20%":[187],"improvements":[188],"two":[190],"famous":[191],"datasets,":[193,204],"Amazon":[194],"YelpChi.":[196],"Notably,":[197],"works":[200],"well":[201],"large":[203],"Amazin-all":[205],"YelpChi-all,":[207],"achieves":[209],"best":[211],"results,":[212],"models":[216],"cannot":[217],"complete":[218]},"counts_by_year":[],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}
