{"id":"https://openalex.org/W4285606307","doi":"https://doi.org/10.24963/ijcai.2022/498","title":"Stabilizing and Enhancing Link Prediction through Deepened Graph Auto-Encoders","display_name":"Stabilizing and Enhancing Link Prediction through Deepened Graph Auto-Encoders","publication_year":2022,"publication_date":"2022-07-01","ids":{"openalex":"https://openalex.org/W4285606307","doi":"https://doi.org/10.24963/ijcai.2022/498","pmid":"https://pubmed.ncbi.nlm.nih.gov/36532945","pmcid":"https://www.ncbi.nlm.nih.gov/pmc/articles/9754798"},"language":"en","primary_location":{"id":"doi:10.24963/ijcai.2022/498","doi":"https://doi.org/10.24963/ijcai.2022/498","is_oa":true,"landing_page_url":"https://doi.org/10.24963/ijcai.2022/498","pdf_url":"https://www.ijcai.org/proceedings/2022/0498.pdf","source":{"id":"https://openalex.org/S4306419999","display_name":"International Joint Conference on Artificial Intelligence","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"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"},"volume":null,"type":"conference-paper","indexed_in":["crossref","pubmed"],"open_access":{"is_oa":true,"oa_status":"bronze","oa_url":"https://www.ijcai.org/proceedings/2022/0498.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5101974344","display_name":"Xinxing Wu","orcid":"https://orcid.org/0000-0001-9177-9352","observed_orcids":["https://orcid.org/0000-0001-9177-9352"]},"institutions":[{"id":"https://openalex.org/I143302722","display_name":"University of Kentucky","ror":"https://ror.org/02k3smh20","country_code":"US","type":"education","lineage":["https://openalex.org/I143302722"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Xinxing wu","raw_affiliation_strings":["University of Kentucky","University of Kentucky, Lexington, Kentucky, U.S.A"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Kentucky","institution_ids":["https://openalex.org/I143302722"]},{"raw_affiliation_string":"University of Kentucky, Lexington, Kentucky, U.S.A","institution_ids":["https://openalex.org/I143302722"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100768220","display_name":"Qiang Shawn Cheng","orcid":"https://orcid.org/0000-0002-3596-2838","observed_orcids":["https://orcid.org/0000-0002-3596-2838","https://orcid.org/0000-0002-5329-8811","https://orcid.org/0000-0002-4370-2120"]},"institutions":[{"id":"https://openalex.org/I143302722","display_name":"University of Kentucky","ror":"https://ror.org/02k3smh20","country_code":"US","type":"education","lineage":["https://openalex.org/I143302722"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Qiang Cheng","raw_affiliation_strings":["University of Kentucky","University of Kentucky, Lexington, Kentucky, U.S.A"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Kentucky","institution_ids":["https://openalex.org/I143302722"]},{"raw_affiliation_string":"University of Kentucky, Lexington, Kentucky, U.S.A","institution_ids":["https://openalex.org/I143302722"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5100768220"],"corresponding_institution_ids":["https://openalex.org/I143302722"],"apc_list":null,"apc_paid":null,"fwci":1.6577,"has_fulltext":true,"cited_by_count":14,"citation_normalized_percentile":{"value":0.86864415,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":99},"biblio":{"volume":"2022","issue":null,"first_page":"3587","last_page":"3593"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11273","display_name":"Advanced Graph Neural Networks","score":1.0,"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":1.0,"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/T10064","display_name":"Complex Network Analysis Techniques","score":0.0,"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"}},{"id":"https://openalex.org/T12292","display_name":"Graph Theory and Algorithms","score":0.0,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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/link-prediction","display_name":"link prediction","score":0.9940000176429749},{"id":"https://openalex.org/keywords/graph-autoencoder","display_name":"graph autoencoder","score":0.9940000176429749},{"id":"https://openalex.org/keywords/polynomial-filters","display_name":"polynomial filters","score":0.609000027179718},{"id":"https://openalex.org/keywords/graph-neural-networks","display_name":"graph neural networks","score":0.7379999756813049},{"id":"https://openalex.org/keywords/complex-networks","display_name":"complex networks","score":0.4309999942779541}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7813229560852051},{"id":"https://openalex.org/C118505674","wikidata":"https://www.wikidata.org/wiki/Q42586063","display_name":"Encoder","level":2,"score":0.655724287033081},{"id":"https://openalex.org/C101738243","wikidata":"https://www.wikidata.org/wiki/Q786435","display_name":"Autoencoder","level":3,"score":0.6421283483505249},{"id":"https://openalex.org/C2778753846","wikidata":"https://www.wikidata.org/wiki/Q6554239","display_name":"Link (geometry)","level":2,"score":0.6393908858299255},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.5832494497299194},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.5308476686477661},{"id":"https://openalex.org/C62611344","wikidata":"https://www.wikidata.org/wiki/Q1062658","display_name":"Node (physics)","level":2,"score":0.4873426854610443},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.45509785413742065},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.4352532625198364},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.39002132415771484},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3474830687046051},{"id":"https://openalex.org/C31258907","wikidata":"https://www.wikidata.org/wiki/Q1301371","display_name":"Computer network","level":1,"score":0.08866572380065918},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.0},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"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":3,"locations":[{"id":"doi:10.24963/ijcai.2022/498","doi":"https://doi.org/10.24963/ijcai.2022/498","is_oa":true,"landing_page_url":"https://doi.org/10.24963/ijcai.2022/498","pdf_url":"https://www.ijcai.org/proceedings/2022/0498.pdf","source":{"id":"https://openalex.org/S4306419999","display_name":"International Joint Conference on Artificial Intelligence","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"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"},{"id":"pmid:36532945","doi":null,"is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/36532945","pdf_url":null,"source":{"id":"https://openalex.org/S4306525036","display_name":"PubMed","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IJCAI : proceedings of the conference","raw_type":"Journal Article"},{"id":"pmh:oai:pubmedcentral.nih.gov:9754798","doi":null,"is_oa":true,"landing_page_url":"https://www.ncbi.nlm.nih.gov/pmc/articles/9754798","pdf_url":null,"source":{"id":"https://openalex.org/S2764455111","display_name":"PubMed Central","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"acceptedVersion","is_accepted":true,"is_published":false,"raw_source_name":"IJCAI (U S)","raw_type":"Text"}],"best_oa_location":{"id":"doi:10.24963/ijcai.2022/498","doi":"https://doi.org/10.24963/ijcai.2022/498","is_oa":true,"landing_page_url":"https://doi.org/10.24963/ijcai.2022/498","pdf_url":"https://www.ijcai.org/proceedings/2022/0498.pdf","source":{"id":"https://openalex.org/S4306419999","display_name":"International Joint Conference on Artificial Intelligence","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"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":[],"study_designs":[],"awards":[{"id":"https://openalex.org/G2160614868","display_name":"Continuous and Longitudinal Monitoring of Cerebral Blood Flow and Metabolism in Freely Moving Rodents","funder_award_id":"R56NS117587","funder_id":"https://openalex.org/F4320337359","funder_display_name":"National Institute of Neurological Disorders and Stroke"},{"id":"https://openalex.org/G6538951870","display_name":"Gene Profile Analysis of Associations between Alzheimer\u2019s Disease and Circadian Rhythmic Patterns of Human Brain Regions Using Machine Learning","funder_award_id":"1r21ag070909-01","funder_id":"https://openalex.org/F4320332161","funder_display_name":"National Institutes of Health"},{"id":"https://openalex.org/G7366643948","display_name":"Noninvasive Noncontact High-Density Optical Imaging of Neonatal Intraventricular Hemorrhage","funder_award_id":"R01HD101508","funder_id":"https://openalex.org/F4320337611","funder_display_name":"Eunice Kennedy Shriver National Institute of Child Health and Human Development"},{"id":"https://openalex.org/G7452299184","display_name":null,"funder_award_id":"W911NF","funder_id":"https://openalex.org/F4320338281","funder_display_name":"Army Research Office"},{"id":"https://openalex.org/G8375851502","display_name":null,"funder_award_id":"R21 AG070909","funder_id":"https://openalex.org/F4320337337","funder_display_name":"National Institute on Aging"}],"funders":[{"id":"https://openalex.org/F4320332161","display_name":"National Institutes of Health","ror":"https://ror.org/01cwqze88"},{"id":"https://openalex.org/F4320337337","display_name":"National Institute on Aging","ror":"https://ror.org/049v75w11"},{"id":"https://openalex.org/F4320337359","display_name":"National Institute of Neurological Disorders and Stroke","ror":"https://ror.org/01s5ya894"},{"id":"https://openalex.org/F4320337611","display_name":"Eunice Kennedy Shriver National Institute of Child Health and Human Development","ror":"https://ror.org/04byxyr05"},{"id":"https://openalex.org/F4320338281","display_name":"Army Research Office","ror":"https://ror.org/05epdh915"}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4285606307.pdf","grobid_xml":"https://content.openalex.org/works/W4285606307.grobid-xml"},"referenced_works_count":30,"referenced_works":["https://openalex.org/W1904365287","https://openalex.org/W2100495367","https://openalex.org/W2110798204","https://openalex.org/W2112796928","https://openalex.org/W2116341502","https://openalex.org/W2153959628","https://openalex.org/W2154851992","https://openalex.org/W2168627253","https://openalex.org/W2187089797","https://openalex.org/W2194775991","https://openalex.org/W2393319904","https://openalex.org/W2420733993","https://openalex.org/W2519887557","https://openalex.org/W2554952599","https://openalex.org/W2565538933","https://openalex.org/W2604682274","https://openalex.org/W2766453196","https://openalex.org/W2794980531","https://openalex.org/W2804057010","https://openalex.org/W2808409763","https://openalex.org/W2916106175","https://openalex.org/W2950697450","https://openalex.org/W2964051675","https://openalex.org/W2975966238","https://openalex.org/W2981525344","https://openalex.org/W2995509042","https://openalex.org/W2996268457","https://openalex.org/W3034492151","https://openalex.org/W4288088467","https://openalex.org/W4381713847"],"related_works":["https://openalex.org/W3137729291","https://openalex.org/W4241931197","https://openalex.org/W7160243082","https://openalex.org/W3100833928","https://openalex.org/W3158076488","https://openalex.org/W4300237897","https://openalex.org/W4212876221","https://openalex.org/W3198901887","https://openalex.org/W7202115407","https://openalex.org/W4411358707"],"abstract_inverted_index":{"Graph":[0],"neural":[1],"networks":[2],"have":[3],"been":[4],"widely":[5],"used":[6],"for":[7,46],"a":[8,16],"variety":[9],"of":[10,43,49,73,81,100,122],"learning":[11,20],"tasks.":[12],"Link":[13],"prediction":[14,48],"is":[15],"relatively":[17],"under-studied":[18],"graph":[19,32],"task,":[21],"with":[22,117],"current":[23,44],"state-of-the-art":[24],"models":[25],"based":[26],"on":[27,77,93],"one-":[28],"or":[29],"two-layer":[30],"shallow":[31,57],"auto-encoder":[33],"(GAE)":[34],"architectures.":[35],"In":[36],"this":[37,123],"paper,":[38],"we":[39,105],"overcome":[40],"the":[41,71,78,97],"limitation":[42],"methods":[45,64],"link":[47],"non-Euclidean":[50],"network":[51,88],"data,":[52],"which":[53],"can":[54,111],"only":[55],"use":[56],"GAEs":[58,74],"and":[59,83],"variational":[60],"GAEs.":[61],"Our":[62],"proposed":[63,102],"innovatively":[65],"incorporate":[66],"standard":[67],"auto-encoders":[68],"(AEs)":[69],"into":[70],"architectures":[72],"to":[75],"capitalize":[76],"intimate":[79],"coupling":[80],"node":[82],"edge":[84],"information":[85],"in":[86],"complex":[87],"data.":[89],"Empirically,":[90],"extensive":[91],"experiments":[92],"various":[94],"datasets":[95],"demonstrate":[96],"competitive":[98],"performance":[99],"our":[101,108],"approach.":[103],"Theoretically,":[104],"prove":[106],"that":[107],"deep":[109],"extensions":[110],"inclusively":[112],"express":[113],"multiple":[114],"polynomial":[115],"filters":[116],"different":[118],"orders.":[119],"The":[120],"codes":[121],"paper":[124],"are":[125],"available":[126],"at":[127],"https://github.com/xinxingwu-uk/DGAE.":[128]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2025,"cited_by_count":6},{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":5}],"updated_date":"2026-10-29T09:56:32.521200","created_date":"2022-07-16T00:00:00"}