{"id":"https://openalex.org/W3198104617","doi":"https://doi.org/10.1145/3460426.3463604","title":"Multi-Initialization Graph Meta-Learning for Node Classification","display_name":"Multi-Initialization Graph Meta-Learning for Node Classification","publication_year":2021,"publication_date":"2021-08-24","ids":{"openalex":"https://openalex.org/W3198104617","doi":"https://doi.org/10.1145/3460426.3463604","mag":"3198104617"},"language":"en","primary_location":{"id":"doi:10.1145/3460426.3463604","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3460426.3463604","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2021 International Conference on Multimedia Retrieval","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/A5005677589","display_name":"Feng Zhao","orcid":"https://orcid.org/0000-0002-4593-2720"},"institutions":[{"id":"https://openalex.org/I76130692","display_name":"Zhejiang University","ror":"https://ror.org/00a2xv884","country_code":"CN","type":"education","lineage":["https://openalex.org/I76130692"]},{"id":"https://openalex.org/I3133055985","display_name":"Westlake University","ror":"https://ror.org/05hfa4n20","country_code":"CN","type":"education","lineage":["https://openalex.org/I3133055985"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Feng Zhao","raw_affiliation_strings":["Zhejiang University &amp; Westlake University, Hangzhou, China"],"affiliations":[{"raw_affiliation_string":"Zhejiang University &amp; Westlake University, Hangzhou, China","institution_ids":["https://openalex.org/I3133055985","https://openalex.org/I76130692"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100665181","display_name":"Donglin Wang","orcid":"https://orcid.org/0000-0002-8188-3735"},"institutions":[{"id":"https://openalex.org/I3133055985","display_name":"Westlake University","ror":"https://ror.org/05hfa4n20","country_code":"CN","type":"education","lineage":["https://openalex.org/I3133055985"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Donglin Wang","raw_affiliation_strings":["Westlake University &amp; Westlake Institule for Advanced Study, HangZhou, China"],"affiliations":[{"raw_affiliation_string":"Westlake University &amp; Westlake Institule for Advanced Study, HangZhou, China","institution_ids":["https://openalex.org/I3133055985"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5073097606","display_name":"Xintao Xiang","orcid":null},"institutions":[{"id":"https://openalex.org/I118347636","display_name":"Australian National University","ror":"https://ror.org/019wvm592","country_code":"AU","type":"education","lineage":["https://openalex.org/I118347636"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Xintao Xiang","raw_affiliation_strings":["Australian\u00a0National\u00a0University, Canberra, ACT, Australia"],"affiliations":[{"raw_affiliation_string":"Australian\u00a0National\u00a0University, Canberra, ACT, Australia","institution_ids":["https://openalex.org/I118347636"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5005677589"],"corresponding_institution_ids":["https://openalex.org/I3133055985","https://openalex.org/I76130692"],"apc_list":null,"apc_paid":null,"fwci":0.5439,"has_fulltext":false,"cited_by_count":4,"citation_normalized_percentile":{"value":0.72803626,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":96},"biblio":{"volume":null,"issue":null,"first_page":"402","last_page":"410"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11273","display_name":"Advanced Graph Neural Networks","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"}},"topics":[{"id":"https://openalex.org/T11273","display_name":"Advanced Graph Neural Networks","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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.9923999905586243,"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":0.9825999736785889,"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/initialization","display_name":"Initialization","score":0.7745540738105774},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7591312527656555},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.6125063896179199},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.565179705619812},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5425847172737122},{"id":"https://openalex.org/keywords/node","display_name":"Node (physics)","score":0.44564497470855713},{"id":"https://openalex.org/keywords/contextual-image-classification","display_name":"Contextual image classification","score":0.42671138048171997},{"id":"https://openalex.org/keywords/modal","display_name":"Modal","score":0.4190051853656769},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4036118686199188},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.21954494714736938},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.17066100239753723}],"concepts":[{"id":"https://openalex.org/C114466953","wikidata":"https://www.wikidata.org/wiki/Q6034165","display_name":"Initialization","level":2,"score":0.7745540738105774},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7591312527656555},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.6125063896179199},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.565179705619812},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5425847172737122},{"id":"https://openalex.org/C62611344","wikidata":"https://www.wikidata.org/wiki/Q1062658","display_name":"Node (physics)","level":2,"score":0.44564497470855713},{"id":"https://openalex.org/C75294576","wikidata":"https://www.wikidata.org/wiki/Q5165192","display_name":"Contextual image classification","level":3,"score":0.42671138048171997},{"id":"https://openalex.org/C71139939","wikidata":"https://www.wikidata.org/wiki/Q910194","display_name":"Modal","level":2,"score":0.4190051853656769},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4036118686199188},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.21954494714736938},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.17066100239753723},{"id":"https://openalex.org/C188027245","wikidata":"https://www.wikidata.org/wiki/Q750446","display_name":"Polymer chemistry","level":1,"score":0.0},{"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},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3460426.3463604","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3460426.3463604","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2021 International Conference on Multimedia Retrieval","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":30,"referenced_works":["https://openalex.org/W1533841329","https://openalex.org/W1888005072","https://openalex.org/W2153579005","https://openalex.org/W2154851992","https://openalex.org/W2187089797","https://openalex.org/W2415243320","https://openalex.org/W2625674597","https://openalex.org/W2743159750","https://openalex.org/W2751808960","https://openalex.org/W2753160622","https://openalex.org/W2798836702","https://openalex.org/W2808000122","https://openalex.org/W2907492528","https://openalex.org/W2962756421","https://openalex.org/W2963066159","https://openalex.org/W2963341924","https://openalex.org/W2964105864","https://openalex.org/W2970105755","https://openalex.org/W2970903810","https://openalex.org/W2984323660","https://openalex.org/W3002924435","https://openalex.org/W3011667710","https://openalex.org/W3034637015","https://openalex.org/W3039075121","https://openalex.org/W3080670519","https://openalex.org/W3100993589","https://openalex.org/W3104097132","https://openalex.org/W3105705953","https://openalex.org/W4210257598","https://openalex.org/W6681968150"],"related_works":["https://openalex.org/W3204184292","https://openalex.org/W3176564347","https://openalex.org/W1985458517","https://openalex.org/W2355833770","https://openalex.org/W3031039437","https://openalex.org/W183202219","https://openalex.org/W3095877357","https://openalex.org/W2072565696","https://openalex.org/W2050451745","https://openalex.org/W2378903222"],"abstract_inverted_index":{"Meta-learning":[0],"aims":[1],"to":[2,16,71,94,100,146,165],"acquire":[3],"common":[4,47],"knowledge":[5],"from":[6,90],"a":[7,31,45,112],"large":[8],"amount":[9],"of":[10,33,56,124,135,181],"similar":[11],"tasks":[12,18,51],"and":[13,39,52,126,130,141],"then":[14],"adapts":[15],"unseen":[17],"within":[19],"few":[20],"gradient":[21],"updates.":[22],"Existing":[23],"graph":[24,119,143],"meta-learning":[25],"algorithms":[26],"show":[27],"appealing":[28],"performance":[29,83],"in":[30,183],"variety":[32],"domains":[34],"such":[35],"as":[36],"node":[37,120,170,185],"classification":[38,186],"link":[40],"prediction.":[41],"These":[42],"methods":[43],"find":[44],"single":[46],"initialization":[48],"for":[49,63,75,84,118,169],"entire":[50],"ignore":[53],"the":[54,154,161,179],"diversity":[55],"task":[57],"distributions,":[58],"which":[59,80],"might":[60],"be":[61],"insufficient":[62],"multi-modal":[64,85],"tasks.":[65,187],"Recent":[66],"approaches":[67],"adopt":[68],"modulation":[69,98,128,136,149,163],"network":[70,99,117],"generate":[72],"task-specific":[73,148,167],"parameters":[74],"further":[76,158],"achieving":[77],"multiple":[78],"initializations,":[79],"shows":[81],"excellent":[82],"image":[86,91],"classification.":[87,171],"However,":[88],"different":[89],"classification,":[92,121],"how":[93],"design":[95],"an":[96],"effective":[97],"handle":[101],"graph-structure":[102,176],"dataset":[103],"is":[104,157],"still":[105],"challenging.":[106],"In":[107,133],"this":[108,152],"paper,":[109],"we":[110,138],"propose":[111],"Multi-Initialization":[113],"Graph":[114],"Meta-Learning":[115],"(MI-GML)":[116],"mainly":[122],"consisting":[123],"local":[125,140],"global":[127,142],"neworks":[129],"meta":[131,155],"learner.":[132],"terms":[134],"network,":[137],"exploit":[139],"structure":[144],"information":[145],"extract":[147],"parameters.":[150],"On":[151],"basis,":[153],"learner":[156],"modulated":[159],"by":[160],"corresponding":[162],"parameter":[164],"produce":[166],"representation":[168],"Experimental":[172],"results":[173],"on":[174],"three":[175],"datasets":[177],"demonstrate":[178],"effectiveness":[180],"MI-GML":[182],"few-shot":[184]},"counts_by_year":[{"year":2023,"cited_by_count":2},{"year":2022,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
