{"id":"https://openalex.org/W2964767742","doi":"https://doi.org/10.1145/3292500.3330848","title":"AutoNE","display_name":"AutoNE","publication_year":2019,"publication_date":"2019-07-25","ids":{"openalex":"https://openalex.org/W2964767742","doi":"https://doi.org/10.1145/3292500.3330848","mag":"2964767742"},"language":"en","primary_location":{"id":"doi:10.1145/3292500.3330848","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3292500.3330848","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining","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/A5062499743","display_name":"Ke Tu","orcid":"https://orcid.org/0009-0009-4922-1684"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Ke Tu","raw_affiliation_strings":["Tsinghua University, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101871710","display_name":"Jianxin Ma","orcid":"https://orcid.org/0009-0002-4614-5320"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jianxin Ma","raw_affiliation_strings":["Tsinghua University, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5009228005","display_name":"Peng Cui","orcid":"https://orcid.org/0000-0003-2957-8511"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Peng Cui","raw_affiliation_strings":["Tsinghua University, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5062247330","display_name":"Jian Pei","orcid":"https://orcid.org/0000-0002-2200-8711"},"institutions":[{"id":"https://openalex.org/I18014758","display_name":"Simon Fraser University","ror":"https://ror.org/0213rcc28","country_code":"CA","type":"education","lineage":["https://openalex.org/I18014758"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Jian Pei","raw_affiliation_strings":["Simon Fraser University and JD.com, Burnaby, Canada"],"affiliations":[{"raw_affiliation_string":"Simon Fraser University and JD.com, Burnaby, Canada","institution_ids":["https://openalex.org/I18014758"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100339293","display_name":"Wenwu Zhu","orcid":"https://orcid.org/0000-0003-2236-9290"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wenwu Zhu","raw_affiliation_strings":["Tsinghua University, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5062499743"],"corresponding_institution_ids":["https://openalex.org/I99065089"],"apc_list":null,"apc_paid":null,"fwci":3.7804,"has_fulltext":false,"cited_by_count":34,"citation_normalized_percentile":{"value":0.94744214,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":90,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"216","last_page":"225"},"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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.9976999759674072,"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/T12535","display_name":"Machine Learning and Data Classification","score":0.9966999888420105,"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/hyperparameter","display_name":"Hyperparameter","score":0.8482738733291626},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7895443439483643},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5785735249519348},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5390796661376953},{"id":"https://openalex.org/keywords/node","display_name":"Node (physics)","score":0.42381617426872253}],"concepts":[{"id":"https://openalex.org/C8642999","wikidata":"https://www.wikidata.org/wiki/Q4171168","display_name":"Hyperparameter","level":2,"score":0.8482738733291626},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7895443439483643},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5785735249519348},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5390796661376953},{"id":"https://openalex.org/C62611344","wikidata":"https://www.wikidata.org/wiki/Q1062658","display_name":"Node (physics)","level":2,"score":0.42381617426872253},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"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":1,"locations":[{"id":"doi:10.1145/3292500.3330848","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3292500.3330848","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":48,"referenced_works":["https://openalex.org/W60686164","https://openalex.org/W1500474910","https://openalex.org/W1502922572","https://openalex.org/W1578099820","https://openalex.org/W1648113946","https://openalex.org/W1888005072","https://openalex.org/W2005126631","https://openalex.org/W2047940964","https://openalex.org/W2048393640","https://openalex.org/W2090891622","https://openalex.org/W2097998348","https://openalex.org/W2101578906","https://openalex.org/W2102539288","https://openalex.org/W2108114251","https://openalex.org/W2121947440","https://openalex.org/W2153579005","https://openalex.org/W2153959628","https://openalex.org/W2154851992","https://openalex.org/W2157825442","https://openalex.org/W2171567099","https://openalex.org/W2182353144","https://openalex.org/W2393319904","https://openalex.org/W2553303224","https://openalex.org/W2559997609","https://openalex.org/W2574817444","https://openalex.org/W2604942799","https://openalex.org/W2804381853","https://openalex.org/W2807021761","https://openalex.org/W2808856341","https://openalex.org/W2808867307","https://openalex.org/W2809156873","https://openalex.org/W2809660921","https://openalex.org/W2899457449","https://openalex.org/W2950182411","https://openalex.org/W2962756421","https://openalex.org/W2962975498","https://openalex.org/W2963460103","https://openalex.org/W2963821229","https://openalex.org/W2964234429","https://openalex.org/W2998993395","https://openalex.org/W3100848837","https://openalex.org/W3103995645","https://openalex.org/W3104097132","https://openalex.org/W3105705953","https://openalex.org/W3106039696","https://openalex.org/W3124229194","https://openalex.org/W4291474301","https://openalex.org/W6731976766"],"related_works":["https://openalex.org/W4295309597","https://openalex.org/W4210794429","https://openalex.org/W4223456145","https://openalex.org/W4309113015","https://openalex.org/W3199608561","https://openalex.org/W4283697347","https://openalex.org/W4295681619","https://openalex.org/W3014815208","https://openalex.org/W4298144215","https://openalex.org/W4375930479"],"abstract_inverted_index":{"Network":[0],"embedding":[1],"(NE)":[2],"aims":[3],"to":[4,52,67,82,126,147,166,176,221],"embed":[5],"the":[6,18,41,89,97,105,129,159,168,174,177,186,193,197,209,223],"nodes":[7],"of":[8,37,70,91,100,131,155,188],"a":[9,12,34,121,132,142,164],"network":[10,24,43,77],"into":[11],"vector":[13],"space,":[14],"and":[15,23,75,84,162,218],"serves":[16],"as":[17,73],"bridge":[19],"between":[20],"machine":[21,58],"learning":[22,59],"data.":[25],"Despite":[26],"their":[27],"widespread":[28],"success,":[29],"NE":[30,133],"algorithms":[31],"typically":[32],"contain":[33],"large":[35],"number":[36,187],"hyperparameters":[38,130,172,195],"for":[39,196],"preserving":[40],"various":[42],"properties,":[44],"which":[45],"must":[46],"be":[47],"carefully":[48],"tuned":[49],"in":[50,212],"order":[51],"achieve":[53],"satisfactory":[54],"performance.":[55],"Though":[56],"automated":[57],"(AutoML)":[60],"has":[61],"achieved":[62],"promising":[63],"results":[64],"when":[65,191],"applied":[66],"many":[68],"types":[69],"data":[71,78],"such":[72],"images":[74],"texts,":[76],"poses":[79],"great":[80],"challenges":[81],"AutoML":[83],"remains":[85],"largely":[86],"ignored":[87],"by":[88],"literature":[90],"AutoML.":[92],"The":[93,181],"biggest":[94],"obstacle":[95],"is":[96],"massive":[98,136,179],"scale":[99],"real-world":[101],"networks,":[102],"along":[103],"with":[104],"coupled":[106],"node":[107],"relationships":[108],"that":[109,203,213],"make":[110],"any":[111],"straightforward":[112],"sampling":[113],"strategy":[114,146],"problematic.":[115],"In":[116,138],"this":[117],"paper,":[118],"we":[119,140],"propose":[120],"novel":[122],"framework,":[123],"named":[124],"AutoNE,":[125],"automatically":[127],"optimize":[128],"algorithm":[134],"on":[135,158],"networks.":[137],"detail,":[139],"employ":[141],"multi-start":[143],"random":[144],"walk":[145],"sample":[148],"several":[149],"small":[150],"sub-networks,":[151],"perform":[152],"each":[153],"trial":[154],"configuration":[156],"selection":[157],"sampled":[160],"sub-network,":[161],"design":[163],"meta-leaner":[165],"transfer":[167],"knowledge":[169],"about":[170],"optimal":[171,194,224],"from":[173],"sub-networks":[175],"original":[178,198],"network.":[180,199],"transferred":[182],"meta-knowledge":[183],"greatly":[184],"reduces":[185],"trials":[189,220],"required":[190],"predicting":[192],"Extensive":[200],"experiments":[201],"demonstrate":[202],"our":[204],"framework":[205],"can":[206],"significantly":[207],"outperform":[208],"existing":[210],"methods,":[211],"it":[214],"needs":[215],"less":[216],"time":[217],"fewer":[219],"find":[222],"hyperparameters.":[225]},"counts_by_year":[{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":3},{"year":2022,"cited_by_count":6},{"year":2021,"cited_by_count":13},{"year":2020,"cited_by_count":4},{"year":2019,"cited_by_count":4},{"year":2018,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2019-08-13T00:00:00"}