{"id":"https://openalex.org/W4287828111","doi":"https://doi.org/10.48550/arxiv.2003.04508","title":"Unsupervised Graph Embedding via Adaptive Graph Learning","display_name":"Unsupervised Graph Embedding via Adaptive Graph Learning","publication_year":2020,"publication_date":"2020-03-10","ids":{"openalex":"https://openalex.org/W4287828111","doi":"https://doi.org/10.48550/arxiv.2003.04508"},"language":"en","primary_location":{"id":"pmh:oai:arXiv.org:2003.04508","doi":null,"is_oa":true,"landing_page_url":"http://arxiv.org/abs/2003.04508","pdf_url":"https://arxiv.org/pdf/2003.04508","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":null,"raw_type":"text"},"volume":null,"type":"preprint","indexed_in":["arxiv","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2003.04508","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5100421978","display_name":"Rui Zhang","orcid":"https://orcid.org/0000-0001-9418-0863","observed_orcids":["https://orcid.org/0000-0001-9418-0863","https://orcid.org/0009-0006-3608-3110","https://orcid.org/0009-0008-8014-7665"]},"institutions":[{"id":"https://openalex.org/I17145004","display_name":"Northwestern Polytechnical University","ror":"https://ror.org/01y0j0j86","country_code":"CN","type":"education","lineage":["https://openalex.org/I17145004"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhang, Rui","raw_affiliation_strings":["School of Computer Science and Center for OPTical IMagery Analysis and Learning (OPTIMAL) , Northwestern Polytechnical University , Xi'an 710072 , Shaanxi , P. R. China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Computer Science and Center for OPTical IMagery Analysis and Learning (OPTIMAL) , Northwestern Polytechnical University , Xi'an 710072 , Shaanxi , P. R. China","institution_ids":["https://openalex.org/I17145004"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101828922","display_name":"Yunxing Zhang","orcid":"https://orcid.org/0000-0002-2081-2466","observed_orcids":["https://orcid.org/0000-0002-2081-2466"]},"institutions":[{"id":"https://openalex.org/I17145004","display_name":"Northwestern Polytechnical University","ror":"https://ror.org/01y0j0j86","country_code":"CN","type":"education","lineage":["https://openalex.org/I17145004"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhang, Yunxing","raw_affiliation_strings":["School of Computer Science and Center for OPTical IMagery Analysis and Learning (OPTIMAL) , Northwestern Polytechnical University , Xi'an 710072 , Shaanxi , P. R. China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Computer Science and Center for OPTical IMagery Analysis and Learning (OPTIMAL) , Northwestern Polytechnical University , Xi'an 710072 , Shaanxi , P. R. China","institution_ids":["https://openalex.org/I17145004"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5106943753","display_name":"Xuelong Li","orcid":"https://orcid.org/0000-0003-2924-946X","observed_orcids":["https://orcid.org/0000-0003-2924-946X"]},"institutions":[{"id":"https://openalex.org/I17145004","display_name":"Northwestern Polytechnical University","ror":"https://ror.org/01y0j0j86","country_code":"CN","type":"education","lineage":["https://openalex.org/I17145004"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Li, Xuelong","raw_affiliation_strings":["School of Computer Science and Center for OPTical IMagery Analysis and Learning (OPTIMAL) , Northwestern Polytechnical University , Xi'an 710072 , Shaanxi , P. R. China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Computer Science and Center for OPTical IMagery Analysis and Learning (OPTIMAL) , Northwestern Polytechnical University , Xi'an 710072 , Shaanxi , P. R. China","institution_ids":["https://openalex.org/I17145004"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I17145004"],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":true,"cited_by_count":2,"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/T10057","display_name":"Face and Expression Recognition","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"}},{"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/network-embedding","display_name":"graph embedding","score":0.7360000014305115},{"id":"https://openalex.org/keywords/graph-autoencoder","display_name":"graph autoencoder","score":0.984000027179718},{"id":"https://openalex.org/keywords/unsupervised-learning","display_name":"unsupervised learning","score":0.8080000281333923},{"id":"https://openalex.org/keywords/adaptive-graph-learning","display_name":"adaptive graph learning","score":0.9340000152587891},{"id":"https://openalex.org/keywords/variational-autoencoder","display_name":"variational autoencoder","score":0.6589999794960022},{"id":"https://openalex.org/keywords/node-clustering","display_name":"node clustering","score":0.8299999833106995},{"id":"https://openalex.org/keywords/node-classification","display_name":"node classification","score":0.9929999709129333},{"id":"https://openalex.org/keywords/network-visualization","display_name":"network visualization","score":0.996999979019165}],"concepts":[{"id":"https://openalex.org/C180356752","wikidata":"https://www.wikidata.org/wiki/Q727035","display_name":"Adjacency matrix","level":3,"score":0.7436749935150146},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5709472298622131},{"id":"https://openalex.org/C75564084","wikidata":"https://www.wikidata.org/wiki/Q5597085","display_name":"Graph embedding","level":3,"score":0.5504806637763977},{"id":"https://openalex.org/C110484373","wikidata":"https://www.wikidata.org/wiki/Q264398","display_name":"Adjacency list","level":2,"score":0.5339086651802063},{"id":"https://openalex.org/C78913703","wikidata":"https://www.wikidata.org/wiki/Q5597087","display_name":"Graph energy","level":5,"score":0.5109362006187439},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.4541777968406677},{"id":"https://openalex.org/C41608201","wikidata":"https://www.wikidata.org/wiki/Q980509","display_name":"Embedding","level":2,"score":0.45319944620132446},{"id":"https://openalex.org/C115178988","wikidata":"https://www.wikidata.org/wiki/Q772067","display_name":"Laplacian matrix","level":3,"score":0.43281427025794983},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.4138144254684448},{"id":"https://openalex.org/C22149727","wikidata":"https://www.wikidata.org/wiki/Q7940747","display_name":"Voltage graph","level":4,"score":0.38773924112319946},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3749665915966034},{"id":"https://openalex.org/C203776342","wikidata":"https://www.wikidata.org/wiki/Q1378376","display_name":"Line graph","level":3,"score":0.35221612453460693},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.24505996704101562}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:oai:arXiv.org:2003.04508","doi":null,"is_oa":true,"landing_page_url":"http://arxiv.org/abs/2003.04508","pdf_url":"https://arxiv.org/pdf/2003.04508","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":null,"raw_type":"text"},{"id":"doi:10.48550/arxiv.2003.04508","doi":"https://doi.org/10.48550/arxiv.2003.04508","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2003.04508","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":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2003.04508","doi":null,"is_oa":true,"landing_page_url":"http://arxiv.org/abs/2003.04508","pdf_url":"https://arxiv.org/pdf/2003.04508","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":null,"raw_type":"text"},"sustainable_development_goals":[],"sustainable_development_goals_aurora":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4287828111.pdf","grobid_xml":"https://content.openalex.org/works/W4287828111.grobid-xml"},"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W3206624081","https://openalex.org/W4213150077","https://openalex.org/W3090939855","https://openalex.org/W2963150133","https://openalex.org/W4388347373","https://openalex.org/W2608378520","https://openalex.org/W2365362095","https://openalex.org/W125803343","https://openalex.org/W4318995835","https://openalex.org/W3010598402"],"abstract_inverted_index":{"Graph":[0],"autoencoders":[1],"(GAEs)":[2],"are":[3,75,118],"powerful":[4],"tools":[5],"in":[6,120,133,188],"representation":[7],"learning":[8,63,73,100],"for":[9,144],"graph":[10,25,54,58,62,67,72,87,95,123,132,151,167,194],"embedding.":[11],"However,":[12],"the":[13,21,24,29,40,81,91,98,104,111,115,121,127,131,134,138,149,158,166],"performance":[14,147],"of":[15,23,28,84,130],"GAEs":[16,35,85],"is":[17,43,153,161],"very":[18],"dependent":[19],"on":[20,86,90,171],"quality":[22],"structure,":[26],"i.e.,":[27],"adjacency":[30,41,105,139],"matrix.":[31],"In":[32,48],"other":[33],"words,":[34],"would":[36],"perform":[37],"poorly":[38],"when":[39,148],"matrix":[42,106,140],"incomplete":[44],"or":[45],"be":[46,108,142],"disturbed.":[47],"this":[49],"paper,":[50],"two":[51],"novel":[52],"unsupervised":[53,57,66],"embedding":[55,59,68,146],"methods,":[56],"via":[60,69],"adaptive":[61,71,99,156],"(BAGE)":[64],"and":[65,177,193],"variational":[70],"(VBAGE)":[74],"proposed.":[76],"The":[77],"proposed":[78,159],"methods":[79,181],"expand":[80],"application":[82],"range":[83],"embedding,":[88],"i.e,":[89],"general":[92],"datasets":[93,173],"without":[94,107],"structure.":[96,168],"Meanwhile,":[97],"mechanism":[101],"can":[102,141],"initialize":[103],"affected":[109],"by":[110,184],"parameter.":[112],"Besides":[113],"that,":[114],"latent":[116],"representations":[117],"embedded":[119],"laplacian":[122],"structure":[124,129,152],"to":[125,165],"preserve":[126],"topology":[128],"vector":[135],"space.":[136],"Moreover,":[137],"self-learned":[143],"better":[145],"original":[150],"incomplete.":[154],"With":[155],"learning,":[157],"method":[160],"much":[162],"more":[163],"robust":[164],"Experimental":[169],"studies":[170],"several":[172],"validate":[174],"our":[175,180],"design":[176],"demonstrate":[178],"that":[179],"outperform":[182],"baselines":[183],"a":[185],"wide":[186],"margin":[187],"node":[189,191],"clustering,":[190],"classification,":[192],"visualization":[195],"tasks.":[196]},"counts_by_year":[{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":1}],"updated_date":"2026-09-30T09:27:27.532063","created_date":"2025-10-10T00:00:00"}