{"id":"https://openalex.org/W4402569346","doi":"https://doi.org/10.1109/tnse.2024.3462462","title":"Graph Anomaly Detection via Multi-View Discriminative Awareness Learning","display_name":"Graph Anomaly Detection via Multi-View Discriminative Awareness Learning","publication_year":2024,"publication_date":"2024-09-17","ids":{"openalex":"https://openalex.org/W4402569346","doi":"https://doi.org/10.1109/tnse.2024.3462462"},"language":"en","primary_location":{"id":"doi:10.1109/tnse.2024.3462462","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tnse.2024.3462462","pdf_url":null,"source":{"id":"https://openalex.org/S2484352698","display_name":"IEEE Transactions on Network Science and Engineering","issn_l":"2327-4697","issn":["2327-4697","2334-329X"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Network Science and Engineering","raw_type":"journal-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/A5036698019","display_name":"Jie Lian","orcid":"https://orcid.org/0000-0003-2351-2570"},"institutions":[{"id":"https://openalex.org/I80947539","display_name":"Fuzhou University","ror":"https://ror.org/011xvna82","country_code":"CN","type":"education","lineage":["https://openalex.org/I80947539"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Jie Lian","raw_affiliation_strings":["College of Computer and Data Science, Fuzhou University, Fuzhou, China"],"affiliations":[{"raw_affiliation_string":"College of Computer and Data Science, Fuzhou University, Fuzhou, China","institution_ids":["https://openalex.org/I80947539"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5044212046","display_name":"Xiangsheng Wang","orcid":"https://orcid.org/0009-0002-9836-7486"},"institutions":[{"id":"https://openalex.org/I80947539","display_name":"Fuzhou University","ror":"https://ror.org/011xvna82","country_code":"CN","type":"education","lineage":["https://openalex.org/I80947539"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xuzheng Wang","raw_affiliation_strings":["College of Computer and Data Science, Fuzhou University, Fuzhou, China"],"affiliations":[{"raw_affiliation_string":"College of Computer and Data Science, Fuzhou University, Fuzhou, China","institution_ids":["https://openalex.org/I80947539"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5055311454","display_name":"Xincan Lin","orcid":"https://orcid.org/0000-0002-6567-6250"},"institutions":[{"id":"https://openalex.org/I80947539","display_name":"Fuzhou University","ror":"https://ror.org/011xvna82","country_code":"CN","type":"education","lineage":["https://openalex.org/I80947539"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xincan Lin","raw_affiliation_strings":["College of Computer and Data Science, Fuzhou University, Fuzhou, China"],"affiliations":[{"raw_affiliation_string":"College of Computer and Data Science, Fuzhou University, Fuzhou, China","institution_ids":["https://openalex.org/I80947539"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5023510892","display_name":"Zhihao Wu","orcid":"https://orcid.org/0000-0001-5835-9903"},"institutions":[{"id":"https://openalex.org/I80947539","display_name":"Fuzhou University","ror":"https://ror.org/011xvna82","country_code":"CN","type":"education","lineage":["https://openalex.org/I80947539"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhihao Wu","raw_affiliation_strings":["College of Computer and Data Science, Fuzhou University, Fuzhou, China"],"affiliations":[{"raw_affiliation_string":"College of Computer and Data Science, Fuzhou University, Fuzhou, China","institution_ids":["https://openalex.org/I80947539"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100604225","display_name":"Shiping Wang","orcid":"https://orcid.org/0000-0001-5195-9682"},"institutions":[{"id":"https://openalex.org/I80947539","display_name":"Fuzhou University","ror":"https://ror.org/011xvna82","country_code":"CN","type":"education","lineage":["https://openalex.org/I80947539"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Shiping Wang","raw_affiliation_strings":["College of Computer and Data Science, Fuzhou University, Fuzhou, China"],"affiliations":[{"raw_affiliation_string":"College of Computer and Data Science, Fuzhou University, Fuzhou, China","institution_ids":["https://openalex.org/I80947539"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100643723","display_name":"Wenzhong Guo","orcid":"https://orcid.org/0000-0003-4118-8823"},"institutions":[{"id":"https://openalex.org/I80947539","display_name":"Fuzhou University","ror":"https://ror.org/011xvna82","country_code":"CN","type":"education","lineage":["https://openalex.org/I80947539"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wenzhong Guo","raw_affiliation_strings":["College of Computer and Data Science, Fuzhou University, Fuzhou, China"],"affiliations":[{"raw_affiliation_string":"College of Computer and Data Science, Fuzhou University, Fuzhou, China","institution_ids":["https://openalex.org/I80947539"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":6,"corresponding_author_ids":["https://openalex.org/A5036698019"],"corresponding_institution_ids":["https://openalex.org/I80947539"],"apc_list":null,"apc_paid":null,"fwci":3.2635,"has_fulltext":false,"cited_by_count":9,"citation_normalized_percentile":{"value":0.93010138,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":98,"max":99},"biblio":{"volume":"11","issue":"6","first_page":"6623","last_page":"6635"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9976000189781189,"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":0.9976000189781189,"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/T10400","display_name":"Network Security and Intrusion Detection","score":0.9955999851226807,"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"}},{"id":"https://openalex.org/T11273","display_name":"Advanced Graph Neural Networks","score":0.977400004863739,"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/discriminative-model","display_name":"Discriminative model","score":0.6864084005355835},{"id":"https://openalex.org/keywords/anomaly-detection","display_name":"Anomaly detection","score":0.6738920211791992},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6090660095214844},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.47102007269859314},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.4680713415145874},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.40772783756256104},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.323299765586853},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.26476025581359863}],"concepts":[{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.6864084005355835},{"id":"https://openalex.org/C739882","wikidata":"https://www.wikidata.org/wiki/Q3560506","display_name":"Anomaly detection","level":2,"score":0.6738920211791992},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6090660095214844},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.47102007269859314},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.4680713415145874},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.40772783756256104},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.323299765586853},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.26476025581359863}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tnse.2024.3462462","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tnse.2024.3462462","pdf_url":null,"source":{"id":"https://openalex.org/S2484352698","display_name":"IEEE Transactions on Network Science and Engineering","issn_l":"2327-4697","issn":["2327-4697","2334-329X"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Network Science and Engineering","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/10","score":0.800000011920929,"display_name":"Reduced inequalities"}],"awards":[{"id":"https://openalex.org/G2979006455","display_name":null,"funder_award_id":"62276065","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G3794798619","display_name":null,"funder_award_id":"U21A20472","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W4389116644","https://openalex.org/W2153315159","https://openalex.org/W3103844505","https://openalex.org/W259157601","https://openalex.org/W4205463238","https://openalex.org/W2761785940","https://openalex.org/W1482209366","https://openalex.org/W2404514746","https://openalex.org/W1652783584","https://openalex.org/W2082783427"],"abstract_inverted_index":{"With":[0],"the":[1,63,68,71,93,120,125,136,142,153,161,164,169,174,189,192],"deeper":[2],"research":[3],"on":[4,33,152,184],"attributed":[5,165],"networks,":[6],"graph":[7,28,116,126],"anomaly":[8,29,117],"detection":[9,30],"is":[10,51],"becoming":[11],"an":[12,48,54],"increasingly":[13],"important":[14],"topic.":[15],"It":[16],"aims":[17],"to":[18,52,57,76,88,158,196],"identify":[19],"patterns":[20],"deviating":[21],"from":[22,180],"a":[23,105,147,181],"majority":[24],"of":[25,70,95,138,191],"nodes.":[26],"Currently,":[27],"algorithms":[31],"based":[32,80,151],"reconstruction-based":[34],"learning":[35,38,65,73,108,114,123],"and":[36,92,132,167],"contrastive-based":[37],"have":[39],"gained":[40],"significant":[41],"attention.":[42],"To":[43,98],"harness":[44],"diverse":[45],"supervised":[46],"signals,":[47],"intuitive":[49],"approach":[50],"find":[53],"elegant":[55],"strategy":[56],"fuse":[58],"these":[59,100],"two":[60],"paradigms,":[61],"forming":[62],"hybrid":[64,72,107,122],"paradigm.":[66],"Despite":[67],"success":[69],"paradigm,":[74,124],"due":[75],"its":[77],"subgraph":[78],"sampling":[79],"approach,":[81],"it":[82],"still":[83],"grapples":[84],"with":[85],"issues":[86],"related":[87],"unreliable":[89],"neighborhood":[90,170],"information":[91,162],"neglect":[94],"topological":[96],"details.":[97],"address":[99],"limitations,":[101],"this":[102],"paper":[103],"proposes":[104],"new":[106],"paradigm":[109],"via":[110],"multi-view":[111,143],"discriminative":[112],"awareness":[113],"for":[115],"detection.":[118],"Unlike":[119],"previous":[121],"reconstruction":[127],"module":[128,145],"fully":[129],"incorporates":[130],"attribute":[131],"topology":[133],"information,":[134],"enhancing":[135],"comprehensiveness":[137],"data":[139],"reconstruction.":[140],"Moreover,":[141],"discrimination":[144],"employs":[146],"view-level":[148],"contrast":[149],"method":[150,194],"complete":[154],"graph,":[155],"which":[156],"helps":[157],"comprehensively":[159],"extract":[160],"in":[163],"network":[166],"mitigates":[168],"unreliability":[171],"without":[172],"increasing":[173],"complexity.":[175],"The":[176],"experimental":[177],"results,":[178],"obtained":[179],"rigorous":[182],"evaluation":[183],"six":[185],"benchmark":[186],"datasets,":[187],"demonstrate":[188],"effectiveness":[190],"proposed":[193],"compared":[195],"existing":[197],"baseline":[198],"methods.":[199]},"counts_by_year":[{"year":2025,"cited_by_count":9}],"updated_date":"2025-12-22T23:10:17.713674","created_date":"2025-10-10T00:00:00"}
