{"id":"https://openalex.org/W2978558422","doi":"https://doi.org/10.1109/ijcnn.2019.8851802","title":"Smooth Deep Network Embedding","display_name":"Smooth Deep Network Embedding","publication_year":2019,"publication_date":"2019-07-01","ids":{"openalex":"https://openalex.org/W2978558422","doi":"https://doi.org/10.1109/ijcnn.2019.8851802","mag":"2978558422"},"language":"en","primary_location":{"id":"doi:10.1109/ijcnn.2019.8851802","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn.2019.8851802","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 International Joint Conference on Neural Networks (IJCNN)","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"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/A5016702781","display_name":"Mengyu Zheng","orcid":null},"institutions":[{"id":"https://openalex.org/I4210165038","display_name":"University of Chinese Academy of Sciences","ror":"https://ror.org/05qbk4x57","country_code":"CN","type":"education","lineage":["https://openalex.org/I19820366","https://openalex.org/I4210165038"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Mengyu Zheng","raw_affiliation_strings":["School of Cyber Security, University of Chinese Academy of Sciences, Beijing, China"],"affiliations":[{"raw_affiliation_string":"School of Cyber Security, University of Chinese Academy of Sciences, Beijing, China","institution_ids":["https://openalex.org/I4210165038"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5085319576","display_name":"Chuan Zhou","orcid":"https://orcid.org/0000-0001-9958-8673"},"institutions":[{"id":"https://openalex.org/I4210165038","display_name":"University of Chinese Academy of Sciences","ror":"https://ror.org/05qbk4x57","country_code":"CN","type":"education","lineage":["https://openalex.org/I19820366","https://openalex.org/I4210165038"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Chuan Zhou","raw_affiliation_strings":["School of Cyber Security, University of Chinese Academy of Sciences, Beijing, China"],"affiliations":[{"raw_affiliation_string":"School of Cyber Security, University of Chinese Academy of Sciences, Beijing, China","institution_ids":["https://openalex.org/I4210165038"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5007475662","display_name":"Jia Wu","orcid":"https://orcid.org/0000-0002-1371-5801"},"institutions":[{"id":"https://openalex.org/I99043593","display_name":"Macquarie University","ror":"https://ror.org/01sf06y89","country_code":"AU","type":"education","lineage":["https://openalex.org/I99043593"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Jia Wu","raw_affiliation_strings":["Department of Computing, Macquarie University, Sydney, Australia"],"affiliations":[{"raw_affiliation_string":"Department of Computing, Macquarie University, Sydney, Australia","institution_ids":["https://openalex.org/I99043593"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5013643897","display_name":"Li Guo","orcid":"https://orcid.org/0000-0003-3821-4058"},"institutions":[{"id":"https://openalex.org/I4210165038","display_name":"University of Chinese Academy of Sciences","ror":"https://ror.org/05qbk4x57","country_code":"CN","type":"education","lineage":["https://openalex.org/I19820366","https://openalex.org/I4210165038"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Li Guo","raw_affiliation_strings":["School of Cyber Security, University of Chinese Academy of Sciences, Beijing, China"],"affiliations":[{"raw_affiliation_string":"School of Cyber Security, University of Chinese Academy of Sciences, Beijing, China","institution_ids":["https://openalex.org/I4210165038"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5016702781"],"corresponding_institution_ids":["https://openalex.org/I4210165038"],"apc_list":null,"apc_paid":null,"fwci":0.14,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.57407304,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":94},"biblio":{"volume":"9","issue":null,"first_page":"1","last_page":"8"},"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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.9968000054359436,"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/T12702","display_name":"Brain Tumor Detection and Classification","score":0.9850000143051147,"subfield":{"id":"https://openalex.org/subfields/2808","display_name":"Neurology"},"field":{"id":"https://openalex.org/fields/28","display_name":"Neuroscience"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/embedding","display_name":"Embedding","score":0.8680909872055054},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7286355495452881},{"id":"https://openalex.org/keywords/smoothness","display_name":"Smoothness","score":0.6713880896568298},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.6242669224739075},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5536431670188904},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.518409013748169},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.45031216740608215},{"id":"https://openalex.org/keywords/network-architecture","display_name":"Network architecture","score":0.4324413537979126},{"id":"https://openalex.org/keywords/contrast","display_name":"Contrast (vision)","score":0.42113468050956726},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.36845874786376953},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.3684312105178833},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.17781656980514526},{"id":"https://openalex.org/keywords/computer-network","display_name":"Computer network","score":0.06401821970939636}],"concepts":[{"id":"https://openalex.org/C41608201","wikidata":"https://www.wikidata.org/wiki/Q980509","display_name":"Embedding","level":2,"score":0.8680909872055054},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7286355495452881},{"id":"https://openalex.org/C102634674","wikidata":"https://www.wikidata.org/wiki/Q868473","display_name":"Smoothness","level":2,"score":0.6713880896568298},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.6242669224739075},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5536431670188904},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.518409013748169},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.45031216740608215},{"id":"https://openalex.org/C193415008","wikidata":"https://www.wikidata.org/wiki/Q639681","display_name":"Network architecture","level":2,"score":0.4324413537979126},{"id":"https://openalex.org/C2776502983","wikidata":"https://www.wikidata.org/wiki/Q690182","display_name":"Contrast (vision)","level":2,"score":0.42113468050956726},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.36845874786376953},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3684312105178833},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.17781656980514526},{"id":"https://openalex.org/C31258907","wikidata":"https://www.wikidata.org/wiki/Q1301371","display_name":"Computer network","level":1,"score":0.06401821970939636},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ijcnn.2019.8851802","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn.2019.8851802","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 International Joint Conference on Neural Networks (IJCNN)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/11","score":0.4099999964237213,"display_name":"Sustainable cities and communities"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":54,"referenced_works":["https://openalex.org/W1560607100","https://openalex.org/W1888005072","https://openalex.org/W2046253692","https://openalex.org/W2048586760","https://openalex.org/W2053186076","https://openalex.org/W2062797058","https://openalex.org/W2097308346","https://openalex.org/W2099021783","https://openalex.org/W2118585731","https://openalex.org/W2136693028","https://openalex.org/W2136922672","https://openalex.org/W2154851992","https://openalex.org/W2258064579","https://openalex.org/W2294347342","https://openalex.org/W2393319904","https://openalex.org/W2427862964","https://openalex.org/W2439554109","https://openalex.org/W2574817444","https://openalex.org/W2605441573","https://openalex.org/W2618235498","https://openalex.org/W2731516742","https://openalex.org/W2740929390","https://openalex.org/W2788836009","https://openalex.org/W2788872664","https://openalex.org/W2803678876","https://openalex.org/W2803831897","https://openalex.org/W2808130788","https://openalex.org/W2897698945","https://openalex.org/W2905499052","https://openalex.org/W2911738047","https://openalex.org/W2950898568","https://openalex.org/W2962756421","https://openalex.org/W2963540169","https://openalex.org/W2964283260","https://openalex.org/W2968868585","https://openalex.org/W2972209102","https://openalex.org/W3022413497","https://openalex.org/W3098276446","https://openalex.org/W3104097132","https://openalex.org/W3105705953","https://openalex.org/W4210257598","https://openalex.org/W6640551753","https://openalex.org/W6662948333","https://openalex.org/W6677656871","https://openalex.org/W6690815549","https://openalex.org/W6731976766","https://openalex.org/W6738369334","https://openalex.org/W6748406900","https://openalex.org/W6748521504","https://openalex.org/W6751569023","https://openalex.org/W6753087691","https://openalex.org/W6758327135","https://openalex.org/W6807384801","https://openalex.org/W6834473044"],"related_works":["https://openalex.org/W2393022482","https://openalex.org/W2377346130","https://openalex.org/W2081900870","https://openalex.org/W2361092061","https://openalex.org/W2319775965","https://openalex.org/W2357314690","https://openalex.org/W2191886813","https://openalex.org/W1986317414","https://openalex.org/W2163394011","https://openalex.org/W2795976185"],"abstract_inverted_index":{"Network":[0,127],"embedding":[1,54,99,137],"is":[2,33,109],"an":[3,111],"efficient":[4],"method":[5,157],"to":[6,35,62,70,103,151],"learn":[7],"low-dimensional":[8],"representations":[9],"of":[10,51,65],"vertexes":[11],"in":[12,49,68,119,162],"networks":[13],"since":[14],"the":[15,37,63,98,152],"network":[16,31,40,53,92],"structure":[17],"can":[18,44,158],"be":[19],"captured":[20],"and":[21,76,135],"preserved":[22],"through":[23],"this":[24,117,120],"process.":[25],"Unlike":[26],"shallow":[27],"models,":[28,67],"deep":[29,107],"neural":[30],"framework":[32,108],"able":[34],"capture":[36],"highly":[38],"non-linear":[39],"structure.":[41],"Therefore,":[42],"it":[43],"achieve":[45,159],"much":[46],"better":[47],"performance":[48],"comparison":[50],"traditional":[52],"methods.":[55],"However,":[56],"few":[57],"attention":[58],"has":[59],"been":[60],"paid":[61],"smoothness":[64,81],"such":[66],"contrast":[69],"numerous":[71],"research":[72],"works":[73],"for":[74],"image":[75],"text":[77],"fields.":[78],"Methods":[79],"without":[80],"are":[82],"not":[83],"robust":[84],"enough,":[85],"which":[86,132],"means":[87],"that":[88,149],"slight":[89],"changes":[90,96],"on":[91,97,143],"may":[93],"lead":[94],"dramatic":[95],"results.":[100,138],"Hence,":[101],"how":[102],"find":[104],"a":[105,124],"smooth":[106],"still":[110],"open":[112],"yet":[113],"important":[114],"problem.":[115],"To":[116],"end,":[118],"paper,":[121],"we":[122,140],"propose":[123],"Smooth":[125],"Deep":[126],"Embedding":[128],"method,":[129],"namely":[130],"SmNE,":[131],"generates":[133],"stable":[134],"reliable":[136],"Empirically,":[139],"conduct":[141],"experiments":[142],"real-world":[144],"networks.":[145],"The":[146],"results":[147],"show":[148],"compared":[150],"state-of-the-art":[153],"methods,":[154],"our":[155],"proposed":[156],"significant":[160],"gains":[161],"several":[163],"applications.":[164]},"counts_by_year":[{"year":2020,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
