{"id":"https://openalex.org/W4225163171","doi":"https://doi.org/10.24963/ijcai.2022/305","title":"Raising the Bar in Graph-level Anomaly Detection","display_name":"Raising the Bar in Graph-level Anomaly Detection","publication_year":2022,"publication_date":"2022-07-01","ids":{"openalex":"https://openalex.org/W4225163171","doi":"https://doi.org/10.24963/ijcai.2022/305"},"language":"en","primary_location":{"id":"doi:10.24963/ijcai.2022/305","is_oa":true,"landing_page_url":"https://doi.org/10.24963/ijcai.2022/305","pdf_url":"https://www.ijcai.org/proceedings/2022/0305.pdf","source":{"id":"https://openalex.org/S4363608755","display_name":"Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"bronze","oa_url":"https://www.ijcai.org/proceedings/2022/0305.pdf","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5100729546","display_name":"Chen Qiu","orcid":"https://orcid.org/0000-0002-1668-8773"},"institutions":[{"id":"https://openalex.org/I153267046","display_name":"University of Kaiserslautern","ror":"https://ror.org/04zrf7b53","country_code":"DE","type":"education","lineage":["https://openalex.org/I153267046"]},{"id":"https://openalex.org/I4210151956","display_name":"Robert Bosch (India)","ror":"https://ror.org/04my8ty22","country_code":"IN","type":"company","lineage":["https://openalex.org/I4210151956","https://openalex.org/I889804353"]}],"countries":["DE","IN"],"is_corresponding":false,"raw_author_name":"Chen Qiu","raw_affiliation_strings":["Bosch Center for Artificial Intelligence","TU Kaiserslautern, Germany"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Bosch Center for Artificial Intelligence","institution_ids":["https://openalex.org/I4210151956"]},{"raw_affiliation_string":"TU Kaiserslautern, Germany","institution_ids":["https://openalex.org/I153267046"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5091841504","display_name":"Marius Kloft","orcid":"https://orcid.org/0000-0001-6829-3725"},"institutions":[{"id":"https://openalex.org/I153267046","display_name":"University of Kaiserslautern","ror":"https://ror.org/04zrf7b53","country_code":"DE","type":"education","lineage":["https://openalex.org/I153267046"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Marius Kloft","raw_affiliation_strings":["TU Kaiserslautern, Germany"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"TU Kaiserslautern, Germany","institution_ids":["https://openalex.org/I153267046"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5036302820","display_name":"Stephan Mandt","orcid":"https://orcid.org/0000-0001-7836-7839"},"institutions":[{"id":"https://openalex.org/I204250578","display_name":"University of California, Irvine","ror":"https://ror.org/04gyf1771","country_code":"US","type":"education","lineage":["https://openalex.org/I204250578"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Stephan Mandt","raw_affiliation_strings":["University of California, Irvine, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of California, Irvine, USA","institution_ids":["https://openalex.org/I204250578"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5077309439","display_name":"Maja Rudolph","orcid":"https://orcid.org/0009-0007-3739-2203"},"institutions":[{"id":"https://openalex.org/I4210151956","display_name":"Robert Bosch (India)","ror":"https://ror.org/04my8ty22","country_code":"IN","type":"company","lineage":["https://openalex.org/I4210151956","https://openalex.org/I889804353"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Maja Rudolph","raw_affiliation_strings":["Bosch Center for Artificial Intelligence"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Bosch Center for Artificial Intelligence","institution_ids":["https://openalex.org/I4210151956"]}]}],"institutions":[],"countries_distinct_count":3,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":42,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"2196","last_page":"2203"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9994999766349792,"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":0.9994999766349792,"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":0.9972000122070312,"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":0.9937000274658203,"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/anomaly-detection","display_name":"Anomaly detection","score":0.861311674118042},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6871717572212219},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5760709047317505},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.524860143661499},{"id":"https://openalex.org/keywords/hypersphere","display_name":"Hypersphere","score":0.5085843801498413},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.5011951923370361},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4474209249019623},{"id":"https://openalex.org/keywords/training-set","display_name":"Training set","score":0.4422963261604309},{"id":"https://openalex.org/keywords/one-class-classification","display_name":"One-class classification","score":0.42192116379737854},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.39159518480300903},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.33225899934768677},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.2614283561706543},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.20093992352485657}],"concepts":[{"id":"https://openalex.org/C739882","wikidata":"https://www.wikidata.org/wiki/Q3560506","display_name":"Anomaly detection","level":2,"score":0.861311674118042},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6871717572212219},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5760709047317505},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.524860143661499},{"id":"https://openalex.org/C2776562905","wikidata":"https://www.wikidata.org/wiki/Q306610","display_name":"Hypersphere","level":2,"score":0.5085843801498413},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.5011951923370361},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4474209249019623},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.4422963261604309},{"id":"https://openalex.org/C34872919","wikidata":"https://www.wikidata.org/wiki/Q7092302","display_name":"One-class classification","level":3,"score":0.42192116379737854},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.39159518480300903},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.33225899934768677},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.2614283561706543},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.20093992352485657}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.24963/ijcai.2022/305","is_oa":true,"landing_page_url":"https://doi.org/10.24963/ijcai.2022/305","pdf_url":"https://www.ijcai.org/proceedings/2022/0305.pdf","source":{"id":"https://openalex.org/S4363608755","display_name":"Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.24963/ijcai.2022/305","is_oa":true,"landing_page_url":"https://doi.org/10.24963/ijcai.2022/305","pdf_url":"https://www.ijcai.org/proceedings/2022/0305.pdf","source":{"id":"https://openalex.org/S4363608755","display_name":"Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence","raw_type":"proceedings-article"},"sustainable_development_goals":[{"display_name":"Peace, Justice and strong institutions","score":0.8199999928474426,"id":"https://metadata.un.org/sdg/16"}],"awards":[{"id":"https://openalex.org/G117062289","display_name":null,"funder_award_id":"HR001120C0021","funder_id":"https://openalex.org/F4320332180","funder_display_name":"Defense Advanced Research Projects Agency"},{"id":"https://openalex.org/G1332273124","display_name":null,"funder_award_id":"KL 2698/2-1","funder_id":"https://openalex.org/F4320320879","funder_display_name":"Deutsche Forschungsgemeinschaft"},{"id":"https://openalex.org/G2230697032","display_name":null,"funder_award_id":"DE-SC0022331","funder_id":"https://openalex.org/F4320306084","funder_display_name":"U.S. Department of Energy"},{"id":"https://openalex.org/G3360711581","display_name":"MLWiNS: Ultra-Reliable Collaborative Computing for Autonomous Unmanned Aerial Vehicles","funder_award_id":"2003237","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G3429341725","display_name":"RI: Small: Deep Variational Data Compression","funder_award_id":"2007719","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G5810975199","display_name":"Collaborative Research: FW-HTF-RM: Intelligent Facilitation for Teams of the Future via Longitudinal Sensing in Context","funder_award_id":"1928718","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G907317167","display_name":null,"funder_award_id":"2698/2-1","funder_id":"https://openalex.org/F4320320879","funder_display_name":"Deutsche Forschungsgemeinschaft"}],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"},{"id":"https://openalex.org/F4320306084","display_name":"U.S. Department of Energy","ror":"https://ror.org/01bj3aw27"},{"id":"https://openalex.org/F4320320879","display_name":"Deutsche Forschungsgemeinschaft","ror":"https://ror.org/018mejw64"},{"id":"https://openalex.org/F4320321114","display_name":"Bundesministerium f\u00fcr Bildung und Forschung","ror":"https://ror.org/04pz7b180"},{"id":"https://openalex.org/F4320332180","display_name":"Defense Advanced Research Projects Agency","ror":"https://ror.org/02caytj08"}],"has_content":{"pdf":true,"grobid_xml":false},"content_urls":{"pdf":"https://content.openalex.org/works/W4225163171.pdf"},"referenced_works_count":27,"referenced_works":["https://openalex.org/W1179283095","https://openalex.org/W2008513692","https://openalex.org/W2131904035","https://openalex.org/W2142498761","https://openalex.org/W2737925311","https://openalex.org/W2750821778","https://openalex.org/W2786088545","https://openalex.org/W2786599352","https://openalex.org/W2803674491","https://openalex.org/W2803697594","https://openalex.org/W2804057010","https://openalex.org/W2944250323","https://openalex.org/W2951086007","https://openalex.org/W2952937126","https://openalex.org/W2962711740","https://openalex.org/W2964015378","https://openalex.org/W2966841471","https://openalex.org/W3005680577","https://openalex.org/W3034213836","https://openalex.org/W3042313988","https://openalex.org/W3083504878","https://openalex.org/W3095602948","https://openalex.org/W3114932221","https://openalex.org/W3129166376","https://openalex.org/W3167045034","https://openalex.org/W4287077916","https://openalex.org/W4287991183"],"related_works":["https://openalex.org/W2379219300","https://openalex.org/W4312271657","https://openalex.org/W4308235887","https://openalex.org/W2770473807","https://openalex.org/W2153477625","https://openalex.org/W3000197790","https://openalex.org/W3171512724","https://openalex.org/W2169365377","https://openalex.org/W2810292876","https://openalex.org/W2135836407"],"abstract_inverted_index":{"Graph-level":[0],"anomaly":[1,29,62],"detection":[2,15,30,39],"has":[3,26],"become":[4],"a":[5,72,88],"critical":[6],"topic":[7],"in":[8,20,71],"diverse":[9],"areas,":[10],"such":[11,34],"as":[12,35],"financial":[13],"fraud":[14],"and":[16,83,110],"detecting":[17,68],"anomalous":[18],"activities":[19],"social":[21],"networks.":[22],"While":[23],"most":[24],"research":[25],"focused":[27],"on":[28,60,78,114],"for":[31,48],"visual":[32],"data":[33,117],"images,":[36],"where":[37],"high":[38],"accuracies":[40],"have":[41],"been":[42],"obtained,":[43],"existing":[44,96,138],"deep":[45,90,97],"learning":[46,82,91],"approaches":[47,99],"graphs":[49,70],"currently":[50],"show":[51],"considerably":[52],"worse":[53],"performance.":[54],"This":[55],"paper":[56],"raises":[57],"the":[58,65,136],"bar":[59],"graph-level":[61],"detection,":[63],"i.e.,":[64],"task":[66],"of":[67,74,103,131],"abnormal":[69],"set":[73],"graphs.":[75],"By":[76],"drawing":[77],"ideas":[79],"from":[80],"self-supervised":[81],"transformation":[84],"learning,":[85],"we":[86],"present":[87],"new":[89],"approach":[92],"that":[93,123],"significantly":[94],"improves":[95],"one-class":[98],"by":[100],"fixing":[101],"some":[102],"their":[104],"known":[105],"problems,":[106],"including":[107],"hypersphere":[108],"collapse":[109],"performance":[111,129],"flip.":[112],"Experiments":[113],"nine":[115,120],"real-world":[116],"sets":[118],"involving":[119],"techniques":[121],"reveal":[122],"our":[124],"method":[125],"achieves":[126],"an":[127],"average":[128],"improvement":[130],"11.8%":[132],"AUC":[133],"compared":[134],"to":[135],"best":[137],"approach.":[139]},"counts_by_year":[{"year":2026,"cited_by_count":5},{"year":2025,"cited_by_count":16},{"year":2024,"cited_by_count":13},{"year":2023,"cited_by_count":7},{"year":2022,"cited_by_count":1}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}
