{"id":"https://openalex.org/W3158076488","doi":null,"title":"Representation Learning using Graph Autoencoders with Residual Connections.","display_name":"Representation Learning using Graph Autoencoders with Residual Connections.","publication_year":2021,"publication_date":"2021-05-03","ids":{"openalex":"https://openalex.org/W3158076488","mag":"3158076488"},"language":"en","primary_location":{"id":"mag:3158076488","doi":null,"is_oa":true,"landing_page_url":"http://export.arxiv.org/pdf/2105.00695","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"arXiv (Cornell University)","raw_type":null},"volume":null,"type":"preprint","is_traditional":true,"indexed_in":[],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"http://export.arxiv.org/pdf/2105.00695","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5002565790","display_name":"Indrit Nallbani","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Indrit Nallbani","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5022470846","display_name":"Aydin Ayanzadeh","orcid":"https://orcid.org/0000-0002-8816-3204","observed_orcids":["https://orcid.org/0000-0002-8816-3204"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Aydin Ayanzadeh","raw_affiliation_strings":[],"raw_orcid":"https://orcid.org/0000-0002-8816-3204","affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5018521122","display_name":"Reyhan Kevser Keser","orcid":"https://orcid.org/0000-0003-2170-2151","observed_orcids":["https://orcid.org/0000-0003-2170-2151"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Reyhan Kevser Keser","raw_affiliation_strings":[],"raw_orcid":"https://orcid.org/0000-0003-2170-2151","affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5025661679","display_name":"Nurullah \u00c7al\u0131k","orcid":"https://orcid.org/0000-0002-7351-4980","observed_orcids":["https://orcid.org/0000-0002-7351-4980"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Nurullah \u00c7al\u0131k","raw_affiliation_strings":[],"raw_orcid":"https://orcid.org/0000-0002-7351-4980","affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5042082747","display_name":"Beh\u00e7et U\u011fur T\u00f6rey\u0131n","orcid":"https://orcid.org/0000-0003-4406-2783","observed_orcids":["https://orcid.org/0000-0003-4406-2783"]},"institutions":[{"id":"https://openalex.org/I48912391","display_name":"Istanbul Technical University","ror":"https://ror.org/059636586","country_code":"TR","type":"education","lineage":["https://openalex.org/I48912391"]}],"countries":["TR"],"is_corresponding":false,"raw_author_name":"Beh\u00e7et U\u011fur T\u00f6rey\u0131n","raw_affiliation_strings":["Istanbul Technical University"],"raw_orcid":"https://orcid.org/0000-0003-4406-2783","affiliations":[{"raw_affiliation_string":"Istanbul Technical University","institution_ids":["https://openalex.org/I48912391"]}]}],"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":1,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"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/T12292","display_name":"Graph Theory and Algorithms","score":0.0,"subfield":{"id":"https://openalex.org/subfields/1703","display_name":"Computational Theory and Mathematics"},"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"}}],"keywords":[{"id":"https://openalex.org/keywords/graph-autoencoder","display_name":"graph autoencoder","score":0.9959999918937683},{"id":"https://openalex.org/keywords/skip-connections","display_name":"skip connections","score":0.9129999876022339},{"id":"https://openalex.org/keywords/variational-graph-autoencoder","display_name":"variational graph autoencoder","score":0.9049999713897705},{"id":"https://openalex.org/keywords/network-embedding","display_name":"graph embedding","score":0.6129999756813049},{"id":"https://openalex.org/keywords/representation-learning","display_name":"representation learning","score":0.7120000123977661},{"id":"https://openalex.org/keywords/link-prediction","display_name":"link prediction","score":0.7229999899864197}],"concepts":[{"id":"https://openalex.org/C155512373","wikidata":"https://www.wikidata.org/wiki/Q287450","display_name":"Residual","level":2,"score":0.8853505849838257},{"id":"https://openalex.org/C101738243","wikidata":"https://www.wikidata.org/wiki/Q786435","display_name":"Autoencoder","level":3,"score":0.8614936470985413},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.6736501455307007},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5735722780227661},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.572272539138794},{"id":"https://openalex.org/C41608201","wikidata":"https://www.wikidata.org/wiki/Q980509","display_name":"Embedding","level":2,"score":0.5600179433822632},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5225914716720581},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.46017634868621826},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.39753177762031555},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.33872365951538086}],"mesh":[],"locations_count":1,"locations":[{"id":"mag:3158076488","doi":null,"is_oa":true,"landing_page_url":"http://export.arxiv.org/pdf/2105.00695","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"arXiv (Cornell University)","raw_type":null}],"best_oa_location":{"id":"mag:3158076488","doi":null,"is_oa":true,"landing_page_url":"http://export.arxiv.org/pdf/2105.00695","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"arXiv (Cornell University)","raw_type":null},"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":27,"referenced_works":["https://openalex.org/W2127265454","https://openalex.org/W2154851992","https://openalex.org/W2162630660","https://openalex.org/W2168190036","https://openalex.org/W2194775991","https://openalex.org/W2244807774","https://openalex.org/W2467604901","https://openalex.org/W2554952599","https://openalex.org/W2770250693","https://openalex.org/W2796335507","https://openalex.org/W2808856341","https://openalex.org/W2808867307","https://openalex.org/W2808987817","https://openalex.org/W2903871660","https://openalex.org/W2918342466","https://openalex.org/W2924557457","https://openalex.org/W2940243131","https://openalex.org/W2945996535","https://openalex.org/W2962756421","https://openalex.org/W2963076818","https://openalex.org/W2964324797","https://openalex.org/W2965341826","https://openalex.org/W2996604169","https://openalex.org/W3034334914","https://openalex.org/W3052570231","https://openalex.org/W3086430417","https://openalex.org/W3118477984"],"related_works":["https://openalex.org/W4300237897","https://openalex.org/W3137729291","https://openalex.org/W3118477984","https://openalex.org/W3206835043","https://openalex.org/W4285356660","https://openalex.org/W2554952599","https://openalex.org/W4212876221","https://openalex.org/W4285606307","https://openalex.org/W2981086289","https://openalex.org/W2899680271"],"abstract_inverted_index":{"Graph":[0],"autoencoders":[1,15],"are":[2],"very":[3],"efficient":[4],"at":[5],"embedding":[6],"graph-based":[7,62],"complex":[8],"data":[9],"sets.":[10],"However,":[11],"most":[12],"of":[13,28,38,59],"the":[14,26,36,57,60,92],"have":[16],"shallow":[17,43],"depths":[18],"and":[19,44,48],"their":[20],"efficiency":[21],"tends":[22],"to":[23,42],"decrease":[24],"with":[25,72,87],"increase":[27],"layer":[29],"depth.":[30],"In":[31],"this":[32],"paper,":[33],"we":[34,65],"study":[35],"effect":[37],"adding":[39],"residual":[40,54,74],"connections":[41,55],"deep":[45,61],"graph":[46,69],"variational":[47,70],"vanilla":[49],"autoencoders.":[50,63],"We":[51],"show":[52,78],"that":[53,79],"improve":[56],"accuracy":[58],"Furthermore,":[64],"propose":[66],"Res-VGAE,":[67],"a":[68],"autoencoder":[71],"different":[73],"connections.":[75],"Our":[76],"experiments":[77],"our":[80],"model":[81],"achieves":[82],"superior":[83],"results":[84],"when":[85],"compared":[86],"other":[88],"autoencoder-based":[89],"models":[90],"for":[91],"link":[93],"prediction":[94],"task.":[95]},"counts_by_year":[{"year":2021,"cited_by_count":1}],"updated_date":"2026-10-16T09:59:22.235173","created_date":"2021-05-10T00:00:00"}
