{"id":"https://openalex.org/W4290876361","doi":"https://doi.org/10.1145/3534678.3539321","title":"GraphMAE: Self-Supervised Masked Graph Autoencoders","display_name":"GraphMAE: Self-Supervised Masked Graph Autoencoders","publication_year":2022,"publication_date":"2022-08-12","ids":{"openalex":"https://openalex.org/W4290876361","doi":"https://doi.org/10.1145/3534678.3539321"},"language":"en","primary_location":{"id":"doi:10.1145/3534678.3539321","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3534678.3539321","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3534678.3539321","source":{"id":"https://openalex.org/S4363608767","display_name":"Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"hybrid","oa_url":"https://dl.acm.org/doi/pdf/10.1145/3534678.3539321","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5060102585","display_name":"Zhenyu Hou","orcid":"https://orcid.org/0000-0002-1624-2149"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Zhenyu Hou","raw_affiliation_strings":["Tsinghua University, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100441094","display_name":"Xiao Liu","orcid":"https://orcid.org/0000-0002-9226-4569"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiao Liu","raw_affiliation_strings":["Tsinghua University, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5035985651","display_name":"Yukuo Cen","orcid":"https://orcid.org/0000-0001-5682-2810"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yukuo Cen","raw_affiliation_strings":["Tsinghua University, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5052284218","display_name":"Yuxiao Dong","orcid":"https://orcid.org/0000-0002-6092-2002"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yuxiao Dong","raw_affiliation_strings":["Tsinghua University, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5082599714","display_name":"Hongxia Yang","orcid":null},"institutions":[{"id":"https://openalex.org/I45928872","display_name":"Alibaba Group (China)","ror":"https://ror.org/00k642b80","country_code":"CN","type":"company","lineage":["https://openalex.org/I45928872"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Hongxia Yang","raw_affiliation_strings":["DAMO Academy, Alibaba Group, Hangzhou, China"],"affiliations":[{"raw_affiliation_string":"DAMO Academy, Alibaba Group, Hangzhou, China","institution_ids":["https://openalex.org/I45928872"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100668507","display_name":"Chunjie Wang","orcid":"https://orcid.org/0000-0001-8081-0748"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chunjie Wang","raw_affiliation_strings":["BirenTech Research, Shanghai, China"],"affiliations":[{"raw_affiliation_string":"BirenTech Research, Shanghai, China","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5044791875","display_name":"Jie Tang","orcid":"https://orcid.org/0000-0003-3487-4593"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jie Tang","raw_affiliation_strings":["Tsinghua University, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":7,"corresponding_author_ids":["https://openalex.org/A5060102585"],"corresponding_institution_ids":["https://openalex.org/I99065089"],"apc_list":null,"apc_paid":null,"fwci":48.8318,"has_fulltext":true,"cited_by_count":500,"citation_normalized_percentile":{"value":0.99947006,"is_in_top_1_percent":true,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":89,"max":100},"biblio":{"volume":null,"issue":null,"first_page":"594","last_page":"604"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11273","display_name":"Advanced Graph Neural Networks","score":0.9997000098228455,"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":0.9997000098228455,"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/T10028","display_name":"Topic Modeling","score":0.9836999773979187,"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/T10269","display_name":"Epigenetics and DNA Methylation","score":0.9308000206947327,"subfield":{"id":"https://openalex.org/subfields/1312","display_name":"Molecular Biology"},"field":{"id":"https://openalex.org/fields/13","display_name":"Biochemistry, Genetics and Molecular Biology"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7671523094177246},{"id":"https://openalex.org/keywords/autoencoder","display_name":"Autoencoder","score":0.6679912805557251},{"id":"https://openalex.org/keywords/generative-grammar","display_name":"Generative grammar","score":0.6428855657577515},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6340599060058594},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.6031322479248047},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5664029121398926},{"id":"https://openalex.org/keywords/generative-model","display_name":"Generative model","score":0.5170372128486633},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.4764592945575714},{"id":"https://openalex.org/keywords/feature-learning","display_name":"Feature learning","score":0.44157329201698303},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.3771931529045105},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3522642254829407},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.33057737350463867}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7671523094177246},{"id":"https://openalex.org/C101738243","wikidata":"https://www.wikidata.org/wiki/Q786435","display_name":"Autoencoder","level":3,"score":0.6679912805557251},{"id":"https://openalex.org/C39890363","wikidata":"https://www.wikidata.org/wiki/Q36108","display_name":"Generative grammar","level":2,"score":0.6428855657577515},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6340599060058594},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.6031322479248047},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5664029121398926},{"id":"https://openalex.org/C167966045","wikidata":"https://www.wikidata.org/wiki/Q5532625","display_name":"Generative model","level":3,"score":0.5170372128486633},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.4764592945575714},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.44157329201698303},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.3771931529045105},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3522642254829407},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.33057737350463867},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0},{"id":"https://openalex.org/C55493867","wikidata":"https://www.wikidata.org/wiki/Q7094","display_name":"Biochemistry","level":1,"score":0.0},{"id":"https://openalex.org/C104317684","wikidata":"https://www.wikidata.org/wiki/Q7187","display_name":"Gene","level":2,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3534678.3539321","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3534678.3539321","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3534678.3539321","source":{"id":"https://openalex.org/S4363608767","display_name":"Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.1145/3534678.3539321","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3534678.3539321","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3534678.3539321","source":{"id":"https://openalex.org/S4363608767","display_name":"Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","raw_type":"proceedings-article"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/4","display_name":"Quality Education","score":0.6000000238418579}],"awards":[{"id":"https://openalex.org/G1040948671","display_name":null,"funder_award_id":"61836013","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G1121271761","display_name":null,"funder_award_id":"Program","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G1231421488","display_name":null,"funder_award_id":"under","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G2802911279","display_name":null,"funder_award_id":"Young","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G3297568266","display_name":null,"funder_award_id":"6183601","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G3317480652","display_name":null,"funder_award_id":"Science","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G37568934","display_name":null,"funder_award_id":"Grant","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G391238517","display_name":null,"funder_award_id":", and","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G5614798656","display_name":null,"funder_award_id":"61825602","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G5848258319","display_name":null,"funder_award_id":"0 and","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G5939423041","display_name":null,"funder_award_id":"Technology","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G5947669560","display_name":null,"funder_award_id":"61825602","funder_id":"https://openalex.org/F4320336125","funder_display_name":"National Science Fund for Distinguished Young Scholars"},{"id":"https://openalex.org/G5994120800","display_name":null,"funder_award_id":"Natural","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G6058138561","display_name":null,"funder_award_id":", No.","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G848032724","display_name":null,"funder_award_id":"Science","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G8955107213","display_name":null,"funder_award_id":"Major","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"},{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320336125","display_name":"National Science Fund for Distinguished Young Scholars","ror":null}],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4290876361.pdf","grobid_xml":"https://content.openalex.org/works/W4290876361.grobid-xml"},"referenced_works_count":17,"referenced_works":["https://openalex.org/W1840338487","https://openalex.org/W1888005072","https://openalex.org/W2008857988","https://openalex.org/W2025768430","https://openalex.org/W2153635508","https://openalex.org/W2154851992","https://openalex.org/W2767404761","https://openalex.org/W2808409763","https://openalex.org/W2962756421","https://openalex.org/W3036446966","https://openalex.org/W3040213512","https://openalex.org/W3080997787","https://openalex.org/W3086452730","https://openalex.org/W3099152386","https://openalex.org/W3104097132","https://openalex.org/W3104425534","https://openalex.org/W4288347894"],"related_works":["https://openalex.org/W2669956259","https://openalex.org/W4249005693","https://openalex.org/W4392946183","https://openalex.org/W3088732000","https://openalex.org/W2983142544","https://openalex.org/W2891059443","https://openalex.org/W4365211920","https://openalex.org/W4281663961","https://openalex.org/W3208888551","https://openalex.org/W4313561566"],"abstract_inverted_index":{"Self-supervised":[0],"learning":[1,164,203],"(SSL)":[2],"has":[3,13,66],"been":[4,47],"extensively":[5],"explored":[6],"in":[7,17,51,75],"recent":[8],"years.":[9],"Particularly,":[10],"generative":[11,58,121,185,201],"SSL":[12,59],"seen":[14],"emerging":[15],"success":[16],"natural":[18],"language":[19],"processing":[20],"and":[21,31,43,83,100,141,184,196],"other":[22,76],"fields,":[23],"such":[24],"as":[25,73],"the":[26,48,55,71,85,90,147,198],"wide":[27],"adoption":[28],"of":[29,57,92,126,150,193,200],"BERT":[30],"GPT.":[32],"Despite":[33],"this,":[34],"contrastive":[35,183],"learning---which":[36],"heavily":[37],"relies":[38],"on":[39,60,133,156,204],"structural":[40],"data":[41],"augmentation":[42],"complicated":[44],"training":[45,98,149],"strategies---has":[46],"dominant":[49],"approach":[50],"graph":[52,63,107,123,163,172,194],"SSL,":[53],"while":[54],"progress":[56],"graphs,":[61],"especially":[62],"autoencoders":[64,195],"(GAEs),":[65],"thus":[67],"far":[68],"not":[69],"reached":[70],"potential":[72,199],"promised":[74],"fields.":[77],"In":[78],"this":[79],"paper,":[80],"we":[81,129],"identify":[82],"examine":[84],"issues":[86,119],"that":[87,116,145,169],"negatively":[88],"impact":[89],"development":[91],"GAEs,":[93],"including":[94],"their":[95],"reconstruction":[96,135],"objective,":[97],"robustness,":[99],"error":[101,144],"metric.":[102],"We":[103,152],"present":[104],"a":[105,138],"masked":[106],"autoencoder":[108,173],"GraphMAE":[109],"(code":[110],"is":[111],"publicly":[112],"available":[113],"at":[114],"https://github.com/THUDM/GraphMAE)":[115],"mitigates":[117],"these":[118],"for":[120,160],"self-supervised":[122,202],"learning.":[124],"Instead":[125],"reconstructing":[127],"structures,":[128],"propose":[130],"to":[131],"focus":[132],"feature":[134],"with":[136,174],"both":[137,182],"masking":[139],"strategy":[140],"scaled":[142],"cosine":[143],"benefit":[146],"robust":[148],"GraphMAE.":[151],"conduct":[153],"extensive":[154],"experiments":[155],"21":[157],"public":[158],"datasets":[159],"three":[161],"different":[162],"tasks.":[165],"The":[166],"results":[167],"manifest":[168],"GraphMAE---a":[170],"simple":[171],"our":[175],"careful":[176],"designs---can":[177],"consistently":[178],"generate":[179],"outperformance":[180],"over":[181],"state-of-the-art":[186],"baselines.":[187],"This":[188],"study":[189],"provides":[190],"an":[191],"understanding":[192],"demonstrates":[197],"graphs.":[205]},"counts_by_year":[{"year":2026,"cited_by_count":31},{"year":2025,"cited_by_count":188},{"year":2024,"cited_by_count":199},{"year":2023,"cited_by_count":78},{"year":2022,"cited_by_count":3},{"year":2021,"cited_by_count":1}],"updated_date":"2026-04-16T08:26:57.006410","created_date":"2025-10-10T00:00:00"}