{"id":"https://openalex.org/W3212281123","doi":"https://doi.org/10.1109/tnnls.2022.3190420","title":"Deep Unsupervised Active Learning on Learnable Graphs","display_name":"Deep Unsupervised Active Learning on Learnable Graphs","publication_year":2022,"publication_date":"2022-07-27","ids":{"openalex":"https://openalex.org/W3212281123","doi":"https://doi.org/10.1109/tnnls.2022.3190420","mag":"3212281123","pmid":"https://pubmed.ncbi.nlm.nih.gov/35895649"},"language":"en","primary_location":{"id":"doi:10.1109/tnnls.2022.3190420","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tnnls.2022.3190420","pdf_url":null,"source":{"id":"https://openalex.org/S4210175523","display_name":"IEEE Transactions on Neural Networks and Learning Systems","issn_l":"2162-237X","issn":["2162-237X","2162-2388"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Neural Networks and Learning Systems","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","pubmed"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5036556540","display_name":"Handong Ma","orcid":"https://orcid.org/0000-0001-9702-249X"},"institutions":[{"id":"https://openalex.org/I150229711","display_name":"University of Electronic Science and Technology of China","ror":"https://ror.org/04qr3zq92","country_code":"CN","type":"education","lineage":["https://openalex.org/I150229711"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Handong Ma","raw_affiliation_strings":["School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, China"],"affiliations":[{"raw_affiliation_string":"School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, China","institution_ids":["https://openalex.org/I150229711"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100330875","display_name":"Changsheng Li","orcid":"https://orcid.org/0000-0001-9789-7632"},"institutions":[{"id":"https://openalex.org/I125839683","display_name":"Beijing Institute of Technology","ror":"https://ror.org/01skt4w74","country_code":"CN","type":"education","lineage":["https://openalex.org/I125839683","https://openalex.org/I890469752"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Changsheng Li","raw_affiliation_strings":["School of Computer Science and Technology, Beijing Institute of Technology, Beijing, China"],"affiliations":[{"raw_affiliation_string":"School of Computer Science and Technology, Beijing Institute of Technology, Beijing, China","institution_ids":["https://openalex.org/I125839683"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5020272723","display_name":"Xinchu Shi","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Xinchu Shi","raw_affiliation_strings":["Meituan Group, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Meituan Group, Beijing, China","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5014346487","display_name":"Ye Yuan","orcid":"https://orcid.org/0000-0002-0247-9866"},"institutions":[{"id":"https://openalex.org/I125839683","display_name":"Beijing Institute of Technology","ror":"https://ror.org/01skt4w74","country_code":"CN","type":"education","lineage":["https://openalex.org/I125839683","https://openalex.org/I890469752"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Ye Yuan","raw_affiliation_strings":["School of Computer Science and Technology, Beijing Institute of Technology, Beijing, China"],"affiliations":[{"raw_affiliation_string":"School of Computer Science and Technology, Beijing Institute of Technology, Beijing, China","institution_ids":["https://openalex.org/I125839683"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5054991337","display_name":"Guoren Wang","orcid":"https://orcid.org/0000-0002-0181-8379"},"institutions":[{"id":"https://openalex.org/I125839683","display_name":"Beijing Institute of Technology","ror":"https://ror.org/01skt4w74","country_code":"CN","type":"education","lineage":["https://openalex.org/I125839683","https://openalex.org/I890469752"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Guoren Wang","raw_affiliation_strings":["School of Computer Science and Technology, Beijing Institute of Technology, Beijing, China"],"affiliations":[{"raw_affiliation_string":"School of Computer Science and Technology, Beijing Institute of Technology, Beijing, China","institution_ids":["https://openalex.org/I125839683"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5036556540"],"corresponding_institution_ids":["https://openalex.org/I150229711"],"apc_list":null,"apc_paid":null,"fwci":0.9724,"has_fulltext":false,"cited_by_count":8,"citation_normalized_percentile":{"value":0.7876928,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":99},"biblio":{"volume":"35","issue":"2","first_page":"2894","last_page":"2900"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11273","display_name":"Advanced Graph Neural Networks","score":0.9993000030517578,"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":0.9993000030517578,"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/T12072","display_name":"Machine Learning and Algorithms","score":0.9991999864578247,"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":0.9911999702453613,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/leverage","display_name":"Leverage (statistics)","score":0.6400874257087708},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6292369961738586},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5755353569984436},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.5499656796455383},{"id":"https://openalex.org/keywords/unsupervised-learning","display_name":"Unsupervised learning","score":0.5494682788848877},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5061249136924744},{"id":"https://openalex.org/keywords/smoothing","display_name":"Smoothing","score":0.4630233645439148},{"id":"https://openalex.org/keywords/feature-learning","display_name":"Feature learning","score":0.4609025716781616},{"id":"https://openalex.org/keywords/notation","display_name":"Notation","score":0.455829918384552},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.4402257800102234},{"id":"https://openalex.org/keywords/autoencoder","display_name":"Autoencoder","score":0.4378681182861328},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.2950672507286072},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.24258163571357727}],"concepts":[{"id":"https://openalex.org/C153083717","wikidata":"https://www.wikidata.org/wiki/Q6535263","display_name":"Leverage (statistics)","level":2,"score":0.6400874257087708},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6292369961738586},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5755353569984436},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.5499656796455383},{"id":"https://openalex.org/C8038995","wikidata":"https://www.wikidata.org/wiki/Q1152135","display_name":"Unsupervised learning","level":2,"score":0.5494682788848877},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5061249136924744},{"id":"https://openalex.org/C3770464","wikidata":"https://www.wikidata.org/wiki/Q775963","display_name":"Smoothing","level":2,"score":0.4630233645439148},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.4609025716781616},{"id":"https://openalex.org/C45357846","wikidata":"https://www.wikidata.org/wiki/Q2001982","display_name":"Notation","level":2,"score":0.455829918384552},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4402257800102234},{"id":"https://openalex.org/C101738243","wikidata":"https://www.wikidata.org/wiki/Q786435","display_name":"Autoencoder","level":3,"score":0.4378681182861328},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.2950672507286072},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.24258163571357727},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.0},{"id":"https://openalex.org/C94375191","wikidata":"https://www.wikidata.org/wiki/Q11205","display_name":"Arithmetic","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/tnnls.2022.3190420","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tnnls.2022.3190420","pdf_url":null,"source":{"id":"https://openalex.org/S4210175523","display_name":"IEEE Transactions on Neural Networks and Learning Systems","issn_l":"2162-237X","issn":["2162-237X","2162-2388"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Neural Networks and Learning Systems","raw_type":"journal-article"},{"id":"pmid:35895649","is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/35895649","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,"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":"IEEE transactions on neural networks and learning systems","raw_type":null}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Quality Education","score":0.4099999964237213,"id":"https://metadata.un.org/sdg/4"}],"awards":[{"id":"https://openalex.org/G2937404559","display_name":null,"funder_award_id":"U2001211","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G678045099","display_name":null,"funder_award_id":"62122013","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":45,"referenced_works":["https://openalex.org/W37083855","https://openalex.org/W185973768","https://openalex.org/W1526543462","https://openalex.org/W2009134948","https://openalex.org/W2109751703","https://openalex.org/W2114188922","https://openalex.org/W2187089797","https://openalex.org/W2441269247","https://openalex.org/W2471138382","https://openalex.org/W2519887557","https://openalex.org/W2572397382","https://openalex.org/W2608346273","https://openalex.org/W2614195334","https://openalex.org/W2615556757","https://openalex.org/W2906217954","https://openalex.org/W2907492528","https://openalex.org/W2951517713","https://openalex.org/W2962711740","https://openalex.org/W2963017945","https://openalex.org/W2963172229","https://openalex.org/W2963393358","https://openalex.org/W2963858333","https://openalex.org/W2964002344","https://openalex.org/W2964012239","https://openalex.org/W2964015378","https://openalex.org/W2984303785","https://openalex.org/W2994821362","https://openalex.org/W2997494090","https://openalex.org/W3034565211","https://openalex.org/W3035166710","https://openalex.org/W3120689699","https://openalex.org/W3173696794","https://openalex.org/W3205626500","https://openalex.org/W3209108897","https://openalex.org/W4210257598","https://openalex.org/W4220667080","https://openalex.org/W4297733535","https://openalex.org/W4302085792","https://openalex.org/W6601518771","https://openalex.org/W6607574751","https://openalex.org/W6726873649","https://openalex.org/W6731874217","https://openalex.org/W6738279980","https://openalex.org/W6754929296","https://openalex.org/W6771263443"],"related_works":["https://openalex.org/W2983142544","https://openalex.org/W2891059443","https://openalex.org/W4281663961","https://openalex.org/W3208888551","https://openalex.org/W4313561566","https://openalex.org/W3208386644","https://openalex.org/W4220682630","https://openalex.org/W4389832810","https://openalex.org/W3163146846","https://openalex.org/W3133533225"],"abstract_inverted_index":{"Recently,":[0],"deep":[1,50],"learning":[2,37,53,63,137],"has":[3],"been":[4],"successfully":[5],"applied":[6],"to":[7,16,32,67,119,133],"unsupervised":[8,40,51,139],"active":[9,41,52,140],"learning.":[10,42,141],"However,":[11],"the":[12,26,78,115,123,130,149],"current":[13],"method":[14],"attempts":[15],"learn":[17,98],"a":[18,48,95,99],"nonlinear":[19],"transformation":[20],"via":[21,55],"an":[22],"auto-encoder":[23],"while":[24],"ignoring":[25],"sample":[27,70],"relation,":[28],"leaving":[29],"huge":[30],"room":[31],"design":[33],"more":[34,82],"effective":[35],"representation":[36,71],"mechanisms":[38],"for":[39,138],"In":[43],"this":[44,128],"brief,":[45],"we":[46,86],"propose":[47],"novel":[49],"model":[54],"learnable":[56],"graphs,":[57],"named":[58],"ALLGs.":[59],"ALLG":[60],"benefits":[61],"from":[62],"optimal":[64],"graph":[65,80,93,102,135],"structures":[66],"acquire":[68],"better":[69],"and":[72,84,97],"select":[73],"representative":[74],"samples.":[75],"To":[76,122],"make":[77],"learned":[79],"structure":[81,136],"stable":[83],"effective,":[85],"take":[87],"into":[88],"account":[89],"k":[90],"-nearest":[91],"neighbor":[92],"as":[94],"priori":[96],"relation":[100],"propagation":[101],"structure.":[103],"We":[104],"also":[105],"incorporate":[106],"shortcut":[107],"connections":[108],"among":[109],"different":[110],"layers,":[111],"which":[112],"can":[113],"alleviate":[114],"well-known":[116],"over-smoothing":[117],"problem":[118],"some":[120],"extent.":[121],"best":[124],"of":[125,151],"our":[126,152],"knowledge,":[127],"is":[129],"first":[131],"attempt":[132],"leverage":[134],"Extensive":[142],"experiments":[143],"performed":[144],"on":[145],"six":[146],"datasets":[147],"demonstrate":[148],"efficacy":[150],"method.":[153]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":3},{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":1}],"updated_date":"2026-04-03T22:45:19.894376","created_date":"2025-10-10T00:00:00"}