{"id":"https://openalex.org/W4412055945","doi":"https://doi.org/10.1007/s10618-025-01115-5","title":"Towards automated self-supervised learning for truly unsupervised graph anomaly detection","display_name":"Towards automated self-supervised learning for truly unsupervised graph anomaly detection","publication_year":2025,"publication_date":"2025-07-05","ids":{"openalex":"https://openalex.org/W4412055945","doi":"https://doi.org/10.1007/s10618-025-01115-5"},"language":"en","primary_location":{"id":"doi:10.1007/s10618-025-01115-5","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s10618-025-01115-5","pdf_url":"https://link.springer.com/content/pdf/10.1007/s10618-025-01115-5.pdf","source":{"id":"https://openalex.org/S121920818","display_name":"Data Mining and Knowledge Discovery","issn_l":"1384-5810","issn":["1384-5810","1573-756X"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319900","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Science+Business Media","Springer Nature"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Data Mining and Knowledge Discovery","raw_type":"journal-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"hybrid","oa_url":"https://link.springer.com/content/pdf/10.1007/s10618-025-01115-5.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5100428658","display_name":"Zhong Li","orcid":"https://orcid.org/0000-0003-1124-5778"},"institutions":[{"id":"https://openalex.org/I121797337","display_name":"Leiden University","ror":"https://ror.org/027bh9e22","country_code":"NL","type":"education","lineage":["https://openalex.org/I121797337"]}],"countries":["NL"],"is_corresponding":true,"raw_author_name":"Zhong Li","raw_affiliation_strings":["Leiden Institute of Advanced Computer Science (LIACS), Leiden University, Leiden, The Netherlands"],"raw_orcid":"https://orcid.org/0000-0003-1124-5778","affiliations":[{"raw_affiliation_string":"Leiden Institute of Advanced Computer Science (LIACS), Leiden University, Leiden, The Netherlands","institution_ids":["https://openalex.org/I121797337"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100449558","display_name":"Yuhang Wang","orcid":"https://orcid.org/0000-0001-5181-0271"},"institutions":[{"id":"https://openalex.org/I121797337","display_name":"Leiden University","ror":"https://ror.org/027bh9e22","country_code":"NL","type":"education","lineage":["https://openalex.org/I121797337"]}],"countries":["NL"],"is_corresponding":false,"raw_author_name":"Yuhang Wang","raw_affiliation_strings":["Leiden Institute of Advanced Computer Science (LIACS), Leiden University, Leiden, The Netherlands"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Leiden Institute of Advanced Computer Science (LIACS), Leiden University, Leiden, The Netherlands","institution_ids":["https://openalex.org/I121797337"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5022646570","display_name":"Matthijs van Leeuwen","orcid":"https://orcid.org/0000-0002-0510-3549"},"institutions":[{"id":"https://openalex.org/I121797337","display_name":"Leiden University","ror":"https://ror.org/027bh9e22","country_code":"NL","type":"education","lineage":["https://openalex.org/I121797337"]}],"countries":["NL"],"is_corresponding":false,"raw_author_name":"Matthijs van Leeuwen","raw_affiliation_strings":["Leiden Institute of Advanced Computer Science (LIACS), Leiden University, Leiden, The Netherlands"],"raw_orcid":"https://orcid.org/0000-0002-0510-3549","affiliations":[{"raw_affiliation_string":"Leiden Institute of Advanced Computer Science (LIACS), Leiden University, Leiden, The Netherlands","institution_ids":["https://openalex.org/I121797337"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5100428658"],"corresponding_institution_ids":["https://openalex.org/I121797337"],"apc_list":{"value":2390,"currency":"EUR","value_usd":2990},"apc_paid":{"value":2390,"currency":"EUR","value_usd":2990},"fwci":0.7006,"has_fulltext":true,"cited_by_count":1,"citation_normalized_percentile":{"value":0.72767377,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":97},"biblio":{"volume":"39","issue":"5","first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10400","display_name":"Network Security and Intrusion Detection","score":0.9998999834060669,"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"}},"topics":[{"id":"https://openalex.org/T10400","display_name":"Network Security and Intrusion Detection","score":0.9998999834060669,"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/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9995999932289124,"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/T12127","display_name":"Software System Performance and Reliability","score":0.9979000091552734,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/anomaly-detection","display_name":"Anomaly detection","score":0.7743229866027832},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6291775703430176},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5981643199920654},{"id":"https://openalex.org/keywords/unsupervised-learning","display_name":"Unsupervised learning","score":0.5621891617774963},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.5513402223587036},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5302196145057678},{"id":"https://openalex.org/keywords/semi-supervised-learning","display_name":"Semi-supervised learning","score":0.4324439465999603},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.38494306802749634},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.32217562198638916},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.12476181983947754}],"concepts":[{"id":"https://openalex.org/C739882","wikidata":"https://www.wikidata.org/wiki/Q3560506","display_name":"Anomaly detection","level":2,"score":0.7743229866027832},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6291775703430176},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5981643199920654},{"id":"https://openalex.org/C8038995","wikidata":"https://www.wikidata.org/wiki/Q1152135","display_name":"Unsupervised learning","level":2,"score":0.5621891617774963},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.5513402223587036},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5302196145057678},{"id":"https://openalex.org/C58973888","wikidata":"https://www.wikidata.org/wiki/Q1041418","display_name":"Semi-supervised learning","level":2,"score":0.4324439465999603},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.38494306802749634},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.32217562198638916},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.12476181983947754}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1007/s10618-025-01115-5","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s10618-025-01115-5","pdf_url":"https://link.springer.com/content/pdf/10.1007/s10618-025-01115-5.pdf","source":{"id":"https://openalex.org/S121920818","display_name":"Data Mining and Knowledge Discovery","issn_l":"1384-5810","issn":["1384-5810","1573-756X"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319900","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Science+Business Media","Springer Nature"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Data Mining and Knowledge Discovery","raw_type":"journal-article"},{"id":"pmh:oai:scholarlypublications.universiteitleiden.nl:item_4255259","is_oa":true,"landing_page_url":"https://hdl.handle.net/1887/4255259","pdf_url":"https://scholarlypublications.universiteitleiden.nl/access/item%3A4255260/view","source":{"id":"https://openalex.org/S4306400850","display_name":"Leiden Repository (Leiden University)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I121797337","host_organization_name":"Leiden University","host_organization_lineage":["https://openalex.org/I121797337"],"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":"Data Mining and Knowledge Discovery","raw_type":"Article / Letter to editor"}],"best_oa_location":{"id":"doi:10.1007/s10618-025-01115-5","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s10618-025-01115-5","pdf_url":"https://link.springer.com/content/pdf/10.1007/s10618-025-01115-5.pdf","source":{"id":"https://openalex.org/S121920818","display_name":"Data Mining and Knowledge Discovery","issn_l":"1384-5810","issn":["1384-5810","1573-756X"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319900","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Science+Business Media","Springer Nature"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Data Mining and Knowledge Discovery","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G1502848176","display_name":null,"funder_award_id":"P18-03","funder_id":"https://openalex.org/F4320321800","funder_display_name":"Nederlandse Organisatie voor Wetenschappelijk Onderzoek"}],"funders":[{"id":"https://openalex.org/F4320321800","display_name":"Nederlandse Organisatie voor Wetenschappelijk Onderzoek","ror":"https://ror.org/04jsz6e67"}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4412055945.pdf","grobid_xml":"https://content.openalex.org/works/W4412055945.grobid-xml"},"referenced_works_count":69,"referenced_works":["https://openalex.org/W46790137","https://openalex.org/W1510052597","https://openalex.org/W1992276041","https://openalex.org/W1996747841","https://openalex.org/W2022322548","https://openalex.org/W2062769337","https://openalex.org/W2064058256","https://openalex.org/W2089554624","https://openalex.org/W2133299088","https://openalex.org/W2138621811","https://openalex.org/W2142889610","https://openalex.org/W2157825442","https://openalex.org/W2562576330","https://openalex.org/W2736287575","https://openalex.org/W2741114205","https://openalex.org/W2743138268","https://openalex.org/W2808544127","https://openalex.org/W2902415114","https://openalex.org/W2944250323","https://openalex.org/W2949736877","https://openalex.org/W2963486145","https://openalex.org/W2965683718","https://openalex.org/W3007200303","https://openalex.org/W3015799890","https://openalex.org/W3034213836","https://openalex.org/W3035682985","https://openalex.org/W3035739162","https://openalex.org/W3039137796","https://openalex.org/W3045004532","https://openalex.org/W3047916742","https://openalex.org/W3086452730","https://openalex.org/W3093664513","https://openalex.org/W3097975205","https://openalex.org/W3126928293","https://openalex.org/W3128318087","https://openalex.org/W3129850062","https://openalex.org/W3133518153","https://openalex.org/W3175514463","https://openalex.org/W3175611124","https://openalex.org/W3179950556","https://openalex.org/W3197075030","https://openalex.org/W3199048887","https://openalex.org/W3199755688","https://openalex.org/W3210350882","https://openalex.org/W4205942400","https://openalex.org/W4210471555","https://openalex.org/W4213224406","https://openalex.org/W4254182148","https://openalex.org/W4281955767","https://openalex.org/W4285066127","https://openalex.org/W4293704747","https://openalex.org/W4312702896","https://openalex.org/W4318811779","https://openalex.org/W4318823201","https://openalex.org/W4319783026","https://openalex.org/W4382239148","https://openalex.org/W4383199656","https://openalex.org/W4386798152","https://openalex.org/W4387185386","https://openalex.org/W4388099759","https://openalex.org/W4398252541","https://openalex.org/W4402673444","https://openalex.org/W6600020652","https://openalex.org/W6600140940","https://openalex.org/W6600168703","https://openalex.org/W6601939488","https://openalex.org/W6605394581","https://openalex.org/W6677978660","https://openalex.org/W6733130978"],"related_works":["https://openalex.org/W4285233543","https://openalex.org/W3148060700","https://openalex.org/W4230838436","https://openalex.org/W3196155444","https://openalex.org/W4321844043","https://openalex.org/W2794908468","https://openalex.org/W4297883248","https://openalex.org/W2513638114","https://openalex.org/W4390062853","https://openalex.org/W3210156800"],"abstract_inverted_index":{"Abstract":[0],"Self-supervised":[1],"learning":[2],"(SSL)":[3],"is":[4,110],"an":[5,95,107,161],"emerging":[6],"paradigm":[7],"that":[8,33],"exploits":[9],"supervisory":[10],"signals":[11],"generated":[12],"from":[13],"the":[14,45,51,54,59,130,195,202],"data":[15,133],"itself,":[16],"and":[17,57,91,114,201],"many":[18,137],"recent":[19,138,183],"studies":[20,139],"have":[21,145],"leveraged":[22],"SSL":[23,47,87,172],"to":[24,100,116,150,159,168],"conduct":[25],"graph":[26,70,142,185],"anomaly":[27,71,143,175,186],"detection.":[28,176],"However,":[29],"we":[30,157],"empirically":[31],"found":[32],"three":[34],"important":[35],"factors":[36],"can":[37],"substantially":[38],"impact":[39],"detection":[40,72,144,187],"performance":[41],"across":[42],"datasets:":[43],"(1)":[44],"specific":[46],"strategy":[48,164],"employed;":[49],"(2)":[50],"tuning":[52],"of":[53,61,119,129,204],"strategy\u2019s":[55],"hyperparameters;":[56],"(3)":[58],"allocation":[60],"combination":[62,92],"weights":[63],"when":[64],"using":[65,103,147,181],"multiple":[66],"strategies.":[67],"Most":[68],"SSL-based":[69,141,184],"methods":[73],"circumvent":[74],"these":[75],"issues":[76,197],"by":[77,83],"arbitrarily":[78],"or":[79],"selectively":[80],"(i.e.,":[81],"guided":[82],"label":[84,104,111,148],"information)":[85],"choosing":[86],"strategies,":[88],"hyperparameter":[89,199],"settings,":[90],"weights.":[93],"While":[94],"arbitrary":[96],"choice":[97],"may":[98],"lead":[99],"subpar":[101],"performance,":[102],"information":[105,112,149],"in":[106,171],"unsupervised":[108,174],"setting":[109],"leakage":[113],"leads":[115],"severe":[117],"overestimation":[118],"a":[120],"method\u2019s":[121],"performance.":[122],"Leakage":[123],"has":[124],"been":[125,146],"criticized":[126],"as":[127],"\u201cone":[128],"top":[131],"ten":[132],"mining":[134],"mistakes\",":[135],"yet":[136],"on":[140,189],"select":[151,169],"hyperparameters.":[152],"To":[153],"mitigate":[154],"this":[155],"issue,":[156],"propose":[158],"use":[160],"internal":[162],"evaluation":[163],"(with":[165],"theoretical":[166],"analysis)":[167],"hyperparameters":[170],"for":[173],"We":[177],"perform":[178],"extensive":[179],"experiments":[180],"10":[182],"algorithms":[188],"various":[190],"benchmark":[191],"datasets,":[192],"demonstrating":[193],"both":[194],"prior":[196],"with":[198],"selection":[200],"effectiveness":[203],"our":[205],"proposed":[206],"strategy.":[207]},"counts_by_year":[{"year":2026,"cited_by_count":1}],"updated_date":"2026-07-15T18:14:33.161393","created_date":"2025-10-10T00:00:00"}
