{"id":"https://openalex.org/W3201472824","doi":"https://doi.org/10.1109/ijcnn52387.2021.9533507","title":"Semi-supervised Anomaly Detection on Attributed Graphs","display_name":"Semi-supervised Anomaly Detection on Attributed Graphs","publication_year":2021,"publication_date":"2021-07-18","ids":{"openalex":"https://openalex.org/W3201472824","doi":"https://doi.org/10.1109/ijcnn52387.2021.9533507","mag":"3201472824"},"language":"en","primary_location":{"id":"doi:10.1109/ijcnn52387.2021.9533507","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn52387.2021.9533507","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 International Joint Conference on Neural Networks (IJCNN)","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/A5030880294","display_name":"Atsutoshi Kumagai","orcid":"https://orcid.org/0000-0002-2915-4615"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Atsutoshi Kumagai","raw_affiliation_strings":["NTT Software Innovation Center, Tokyo, Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"NTT Software Innovation Center, Tokyo, Japan","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5034538103","display_name":"Tomoharu Iwata","orcid":"https://orcid.org/0000-0003-4425-1971"},"institutions":[{"id":"https://openalex.org/I2251713219","display_name":"NTT (Japan)","ror":"https://ror.org/00berct97","country_code":"JP","type":"company","lineage":["https://openalex.org/I2251713219"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Tomoharu Iwata","raw_affiliation_strings":["NTT Communication Science Laboratories, Kyoto, Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"NTT Communication Science Laboratories, Kyoto, Japan","institution_ids":["https://openalex.org/I2251713219"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5101917967","display_name":"Yasuhiro Fujiwara","orcid":"https://orcid.org/0000-0001-9578-1118"},"institutions":[{"id":"https://openalex.org/I2251713219","display_name":"NTT (Japan)","ror":"https://ror.org/00berct97","country_code":"JP","type":"company","lineage":["https://openalex.org/I2251713219"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Yasuhiro Fujiwara","raw_affiliation_strings":["NTT Communication Science Laboratories, Kanagawa, Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"NTT Communication Science Laboratories, Kanagawa, Japan","institution_ids":["https://openalex.org/I2251713219"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":38,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"8"},"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/T10400","display_name":"Network Security and Intrusion Detection","score":0.9994000196456909,"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/T10064","display_name":"Complex Network Analysis Techniques","score":0.9943000078201294,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/hypersphere","display_name":"Hypersphere","score":0.9096132516860962},{"id":"https://openalex.org/keywords/anomaly-detection","display_name":"Anomaly detection","score":0.6725097894668579},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6447617411613464},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.5716274380683899},{"id":"https://openalex.org/keywords/graph-embedding","display_name":"Graph embedding","score":0.5507388114929199},{"id":"https://openalex.org/keywords/embedding","display_name":"Embedding","score":0.5089054107666016},{"id":"https://openalex.org/keywords/node","display_name":"Node (physics)","score":0.46181783080101013},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.43578287959098816},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.40808531641960144},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.336786150932312},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3256164789199829}],"concepts":[{"id":"https://openalex.org/C2776562905","wikidata":"https://www.wikidata.org/wiki/Q306610","display_name":"Hypersphere","level":2,"score":0.9096132516860962},{"id":"https://openalex.org/C739882","wikidata":"https://www.wikidata.org/wiki/Q3560506","display_name":"Anomaly detection","level":2,"score":0.6725097894668579},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6447617411613464},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.5716274380683899},{"id":"https://openalex.org/C75564084","wikidata":"https://www.wikidata.org/wiki/Q5597085","display_name":"Graph embedding","level":3,"score":0.5507388114929199},{"id":"https://openalex.org/C41608201","wikidata":"https://www.wikidata.org/wiki/Q980509","display_name":"Embedding","level":2,"score":0.5089054107666016},{"id":"https://openalex.org/C62611344","wikidata":"https://www.wikidata.org/wiki/Q1062658","display_name":"Node (physics)","level":2,"score":0.46181783080101013},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.43578287959098816},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.40808531641960144},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.336786150932312},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3256164789199829},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.0},{"id":"https://openalex.org/C66938386","wikidata":"https://www.wikidata.org/wiki/Q633538","display_name":"Structural engineering","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ijcnn52387.2021.9533507","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn52387.2021.9533507","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 International Joint Conference on Neural Networks (IJCNN)","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":103,"referenced_works":["https://openalex.org/W1492581097","https://openalex.org/W1522301498","https://openalex.org/W1614298861","https://openalex.org/W1887038067","https://openalex.org/W1888005072","https://openalex.org/W1970088130","https://openalex.org/W2000661457","https://openalex.org/W2027731328","https://openalex.org/W2089554624","https://openalex.org/W2114996745","https://openalex.org/W2122361470","https://openalex.org/W2122646361","https://openalex.org/W2127979711","https://openalex.org/W2132870739","https://openalex.org/W2134008243","https://openalex.org/W2135689168","https://openalex.org/W2139823104","https://openalex.org/W2148143831","https://openalex.org/W2153959628","https://openalex.org/W2154455818","https://openalex.org/W2154851992","https://openalex.org/W2187089797","https://openalex.org/W2230089931","https://openalex.org/W2296719434","https://openalex.org/W2402531259","https://openalex.org/W2554952599","https://openalex.org/W2598402539","https://openalex.org/W2621614835","https://openalex.org/W2741114205","https://openalex.org/W2743138268","https://openalex.org/W2786088545","https://openalex.org/W2789159078","https://openalex.org/W2803697594","https://openalex.org/W2808544127","https://openalex.org/W2809503262","https://openalex.org/W2899771611","https://openalex.org/W2900470550","https://openalex.org/W2910068345","https://openalex.org/W2914765253","https://openalex.org/W2916106175","https://openalex.org/W2918342466","https://openalex.org/W2937385938","https://openalex.org/W2944250323","https://openalex.org/W2946262940","https://openalex.org/W2949435814","https://openalex.org/W2950577311","https://openalex.org/W2962756421","https://openalex.org/W2962767366","https://openalex.org/W2963312446","https://openalex.org/W2963858333","https://openalex.org/W2963893312","https://openalex.org/W2964015378","https://openalex.org/W2964121744","https://openalex.org/W2969836179","https://openalex.org/W2970535483","https://openalex.org/W2983576094","https://openalex.org/W2995458562","https://openalex.org/W2996268457","https://openalex.org/W2996592317","https://openalex.org/W2999426717","https://openalex.org/W3008609114","https://openalex.org/W3034213836","https://openalex.org/W3046355609","https://openalex.org/W3090532443","https://openalex.org/W3093664513","https://openalex.org/W3093823951","https://openalex.org/W3104097132","https://openalex.org/W4288335160","https://openalex.org/W4288419263","https://openalex.org/W4293321333","https://openalex.org/W4294558607","https://openalex.org/W4297733535","https://openalex.org/W4299281608","https://openalex.org/W4322614756","https://openalex.org/W4386554450","https://openalex.org/W6631190155","https://openalex.org/W6636510571","https://openalex.org/W6639415379","https://openalex.org/W6680128238","https://openalex.org/W6680434193","https://openalex.org/W6682494755","https://openalex.org/W6699364125","https://openalex.org/W6726873649","https://openalex.org/W6730084236","https://openalex.org/W6738964360","https://openalex.org/W6745537798","https://openalex.org/W6748102297","https://openalex.org/W6748366383","https://openalex.org/W6751494907","https://openalex.org/W6756561102","https://openalex.org/W6758101687","https://openalex.org/W6759307309","https://openalex.org/W6760001035","https://openalex.org/W6760045743","https://openalex.org/W6761365013","https://openalex.org/W6762759627","https://openalex.org/W6763324549","https://openalex.org/W6767443168","https://openalex.org/W6771932116","https://openalex.org/W6772296262","https://openalex.org/W6774508804","https://openalex.org/W6781391132","https://openalex.org/W6784697731"],"related_works":["https://openalex.org/W4384263420","https://openalex.org/W4386076684","https://openalex.org/W3036264823","https://openalex.org/W3206528106","https://openalex.org/W2123605750","https://openalex.org/W2912814903","https://openalex.org/W2088740331","https://openalex.org/W2950907416","https://openalex.org/W3038102983","https://openalex.org/W2082479932"],"abstract_inverted_index":{"We":[0],"propose":[1],"a":[2,19,63,98,110,119,163],"simple":[3],"yet":[4],"effective":[5],"method":[6,54,156,196],"for":[7,91],"detecting":[8],"anomalous":[9,131],"instances":[10,32,42,128],"on":[11,58,78,162],"an":[12],"attribute":[13],"graph":[14,61,76,82,178,189],"with":[15,185],"label":[16,160],"information":[17,161],"of":[18,22,81,105,109,118,126,166],"small":[20,164],"number":[21],"instances.":[23],"Although":[24],"standard":[25],"anomaly":[26,92,200],"detection":[27,201],"methods":[28],"usually":[29],"assume":[30],"that":[31,121,193],"are":[33,43,112],"independent":[34],"and":[35,151,180],"identically":[36],"distributed,":[37],"in":[38,48,62,94],"many":[39],"real-world":[40,187],"applications,":[41],"often":[44],"explicitly":[45],"connected,":[46],"resulting":[47],"so-called":[49],"attributed":[50,60,188],"graphs.":[51],"The":[52,154],"proposed":[53,155,195],"embeds":[55],"nodes":[56,167],"(instances)":[57],"the":[59,75,79,103,107,116,123,134,145,148,175,194],"latent":[64],"space":[65],"by":[66,142,171],"taking":[67,172],"into":[68,173],"account":[69,174],"their":[70],"attributes":[71],"as":[72,74],"well":[73],"structure":[77],"basis":[80],"convolutional":[83],"networks":[84],"(GCNs).":[85],"To":[86],"learn":[87],"node":[88,124,149],"embeddings":[89,125,150],"specialized":[90],"detection,":[93],"which":[95],"there":[96],"is":[97],"class":[99,181],"imbalance":[100],"due":[101],"to":[102,114,139,168],"rarity":[104],"anomalies,":[106],"parameters":[108],"GCN":[111],"trained":[113],"minimize":[115],"volume":[117],"hypersphere":[120,152],"encloses":[122],"normal":[127],"while":[129],"embedding":[130],"ones":[132,170],"outside":[133],"hypersphere.":[135],"This":[136],"enables":[137],"us":[138],"detect":[140],"anomalies":[141],"simply":[143],"calculating":[144],"distances":[146],"between":[147],"center.":[153],"can":[157],"effectively":[158],"propagate":[159],"amount":[165],"unlabeled":[169],"node's":[176],"attributes,":[177],"structure,":[179],"imbalance.":[182],"In":[183],"experiments":[184],"five":[186],"datasets,":[190],"we":[191],"demonstrate":[192],"outperforms":[197],"various":[198],"existing":[199],"methods.":[202]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":8},{"year":2024,"cited_by_count":12},{"year":2023,"cited_by_count":8},{"year":2022,"cited_by_count":7},{"year":2021,"cited_by_count":2}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}