{"id":"https://openalex.org/W3090781815","doi":"https://doi.org/10.1109/ijcnn48605.2020.9207213","title":"Fuzzy Graph Neural Network for Few-Shot Learning","display_name":"Fuzzy Graph Neural Network for Few-Shot Learning","publication_year":2020,"publication_date":"2020-07-01","ids":{"openalex":"https://openalex.org/W3090781815","doi":"https://doi.org/10.1109/ijcnn48605.2020.9207213","mag":"3090781815"},"language":"en","primary_location":{"id":"doi:10.1109/ijcnn48605.2020.9207213","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn48605.2020.9207213","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 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/A5103888555","display_name":"Tong Wei","orcid":null},"institutions":[{"id":"https://openalex.org/I24943067","display_name":"Fudan University","ror":"https://ror.org/013q1eq08","country_code":"CN","type":"education","lineage":["https://openalex.org/I24943067"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Tong Wei","raw_affiliation_strings":["Shanghai Key Lab of Intelligent Information Processing School of Computer Science, Fudan University, Shanghai, China"],"affiliations":[{"raw_affiliation_string":"Shanghai Key Lab of Intelligent Information Processing School of Computer Science, Fudan University, Shanghai, China","institution_ids":["https://openalex.org/I24943067"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5089445128","display_name":"Junlin Hou","orcid":"https://orcid.org/0000-0002-7989-5913"},"institutions":[{"id":"https://openalex.org/I24943067","display_name":"Fudan University","ror":"https://ror.org/013q1eq08","country_code":"CN","type":"education","lineage":["https://openalex.org/I24943067"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Junlin Hou","raw_affiliation_strings":["Shanghai Key Lab of Intelligent Information Processing School of Computer Science, Fudan University, Shanghai, China"],"affiliations":[{"raw_affiliation_string":"Shanghai Key Lab of Intelligent Information Processing School of Computer Science, Fudan University, Shanghai, China","institution_ids":["https://openalex.org/I24943067"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5109435836","display_name":"Rui Feng","orcid":"https://orcid.org/0000-0002-0611-9518"},"institutions":[{"id":"https://openalex.org/I24943067","display_name":"Fudan University","ror":"https://ror.org/013q1eq08","country_code":"CN","type":"education","lineage":["https://openalex.org/I24943067"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Rui Feng","raw_affiliation_strings":["Shanghai Key Lab of Intelligent Information Processing School of Computer Science, Fudan University, Shanghai, China"],"affiliations":[{"raw_affiliation_string":"Shanghai Key Lab of Intelligent Information Processing School of Computer Science, Fudan University, Shanghai, China","institution_ids":["https://openalex.org/I24943067"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5103888555"],"corresponding_institution_ids":["https://openalex.org/I24943067"],"apc_list":null,"apc_paid":null,"fwci":1.7815,"has_fulltext":false,"cited_by_count":23,"citation_normalized_percentile":{"value":0.88218864,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":100},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"8"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.9994999766349792,"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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.9994999766349792,"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/T11273","display_name":"Advanced Graph Neural Networks","score":0.9986000061035156,"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":0.995199978351593,"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/inductive-bias","display_name":"Inductive bias","score":0.7546795606613159},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7062687873840332},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6310251355171204},{"id":"https://openalex.org/keywords/enhanced-data-rates-for-gsm-evolution","display_name":"Enhanced Data Rates for GSM Evolution","score":0.5756045579910278},{"id":"https://openalex.org/keywords/uniqueness","display_name":"Uniqueness","score":0.5179256796836853},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5150539875030518},{"id":"https://openalex.org/keywords/fuzzy-logic","display_name":"Fuzzy logic","score":0.5102288722991943},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.47092074155807495},{"id":"https://openalex.org/keywords/class","display_name":"Class (philosophy)","score":0.4549543261528015},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.4538685977458954},{"id":"https://openalex.org/keywords/multi-task-learning","display_name":"Multi-task learning","score":0.2809790372848511},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.16798517107963562},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.16538500785827637},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.14180773496627808}],"concepts":[{"id":"https://openalex.org/C197352929","wikidata":"https://www.wikidata.org/wiki/Q1074074","display_name":"Inductive bias","level":4,"score":0.7546795606613159},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7062687873840332},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6310251355171204},{"id":"https://openalex.org/C162307627","wikidata":"https://www.wikidata.org/wiki/Q204833","display_name":"Enhanced Data Rates for GSM Evolution","level":2,"score":0.5756045579910278},{"id":"https://openalex.org/C2777021972","wikidata":"https://www.wikidata.org/wiki/Q22976830","display_name":"Uniqueness","level":2,"score":0.5179256796836853},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5150539875030518},{"id":"https://openalex.org/C58166","wikidata":"https://www.wikidata.org/wiki/Q224821","display_name":"Fuzzy logic","level":2,"score":0.5102288722991943},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.47092074155807495},{"id":"https://openalex.org/C2777212361","wikidata":"https://www.wikidata.org/wiki/Q5127848","display_name":"Class (philosophy)","level":2,"score":0.4549543261528015},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.4538685977458954},{"id":"https://openalex.org/C28006648","wikidata":"https://www.wikidata.org/wiki/Q6934509","display_name":"Multi-task learning","level":3,"score":0.2809790372848511},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.16798517107963562},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.16538500785827637},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.14180773496627808},{"id":"https://openalex.org/C187736073","wikidata":"https://www.wikidata.org/wiki/Q2920921","display_name":"Management","level":1,"score":0.0},{"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/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ijcnn48605.2020.9207213","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn48605.2020.9207213","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 International Joint Conference on Neural Networks (IJCNN)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":73,"referenced_works":["https://openalex.org/W1501856433","https://openalex.org/W1836465849","https://openalex.org/W1964468250","https://openalex.org/W2039589504","https://openalex.org/W2076556084","https://openalex.org/W2115733720","https://openalex.org/W2117539524","https://openalex.org/W2194775991","https://openalex.org/W2506467459","https://openalex.org/W2519887557","https://openalex.org/W2601450892","https://openalex.org/W2604763608","https://openalex.org/W2606780347","https://openalex.org/W2625674597","https://openalex.org/W2742093937","https://openalex.org/W2753160622","https://openalex.org/W2770468159","https://openalex.org/W2787035179","https://openalex.org/W2787501667","https://openalex.org/W2789011104","https://openalex.org/W2791172704","https://openalex.org/W2798836702","https://openalex.org/W2801277493","https://openalex.org/W2803533564","https://openalex.org/W2892122929","https://openalex.org/W2895671740","https://openalex.org/W2905224888","https://openalex.org/W2910040013","https://openalex.org/W2919115771","https://openalex.org/W2943605315","https://openalex.org/W2949117887","https://openalex.org/W2949879676","https://openalex.org/W2950898568","https://openalex.org/W2962723986","https://openalex.org/W2962895018","https://openalex.org/W2963306862","https://openalex.org/W2963341924","https://openalex.org/W2963551321","https://openalex.org/W2963680240","https://openalex.org/W2963943197","https://openalex.org/W2964015378","https://openalex.org/W2964051675","https://openalex.org/W2964105864","https://openalex.org/W2964112702","https://openalex.org/W2970689606","https://openalex.org/W3091905774","https://openalex.org/W4211007335","https://openalex.org/W4289382675","https://openalex.org/W4293412117","https://openalex.org/W4297800546","https://openalex.org/W6638667902","https://openalex.org/W6690815549","https://openalex.org/W6717697761","https://openalex.org/W6726873649","https://openalex.org/W6735236233","https://openalex.org/W6736057607","https://openalex.org/W6736685754","https://openalex.org/W6742288159","https://openalex.org/W6743661861","https://openalex.org/W6746260573","https://openalex.org/W6747943641","https://openalex.org/W6748141922","https://openalex.org/W6748284727","https://openalex.org/W6749327742","https://openalex.org/W6751281049","https://openalex.org/W6751310286","https://openalex.org/W6751797489","https://openalex.org/W6753311412","https://openalex.org/W6754673040","https://openalex.org/W6755318597","https://openalex.org/W6755766585","https://openalex.org/W6757374366","https://openalex.org/W6783596713"],"related_works":["https://openalex.org/W571281153","https://openalex.org/W2949403936","https://openalex.org/W4301247218","https://openalex.org/W3023806604","https://openalex.org/W2952139525","https://openalex.org/W2129818458","https://openalex.org/W4289551052","https://openalex.org/W4390756076","https://openalex.org/W4243074410","https://openalex.org/W2969742028"],"abstract_inverted_index":{"Recent":[0],"works":[1],"have":[2,33],"shown":[3],"that":[4,153],"graph":[5],"neural":[6],"net-works":[7],"(GNNs)":[8],"can":[9],"substantially":[10],"improve":[11],"the":[12,40,61,94,100,104,127,146],"performance":[13,37,158],"of":[14,42,106,126,148],"few-shot":[15,84,169],"learning":[16,170],"benefitting":[17],"from":[18],"their":[19],"natural":[20],"ability":[21],"to":[22,39,92,103,115],"learn":[23],"inter-class":[24],"uniqueness":[25],"and":[26,53,139,159],"intra-class":[27],"commonality.":[28],"However,":[29],"previous":[30],"GNN":[31,72,91,165],"methods":[32,166],"not":[34],"achieved":[35],"satisfactory":[36],"due":[38],"absence":[41],"a":[43,66,111,160],"strong":[44],"relational":[45,77],"inductive":[46,78],"bias":[47],"which":[48,74],"determines":[49],"how":[50],"entities":[51],"interact":[52],"are":[54,129],"isolated.":[55],"In":[56],"this":[57],"paper,":[58],"inspired":[59],"by":[60,97,131],"fuzzy":[62,112],"theory,":[63],"we":[64,87,109],"propose":[65],"novel":[67],"meta-learning":[68],"method":[69,155],"called":[70],"Fuzzy":[71],"(FGNN),":[73],"obtains":[75],"superior":[76],"biases":[79],"in":[80],"each":[81],"episode,":[82],"for":[83,121],"learning.":[85],"Specifically,":[86],"employ":[88],"an":[89],"edge-focused":[90],"perform":[93],"edge":[95,107],"prediction":[96],"iteratively":[98],"updating":[99],"edge-labels.":[101],"According":[102],"output":[105],"prediction,":[108],"design":[110],"membership":[113],"function":[114],"achieve":[116],"more":[117],"exact":[118],"relationship":[119],"representations":[120],"node":[122],"classification.":[123],"The":[124,150],"parameters":[125],"FGNN":[128],"learned":[130],"episodic":[132],"training":[133],"with":[134],"mixed":[135],"loss":[136],"including":[137],"node-label":[138],"edge-label.":[140],"Extensive":[141],"experimental":[142],"evaluation":[143],"clearly":[144],"demonstrates":[145],"effectiveness":[147],"FGNN.":[149],"results":[151],"show":[152],"our":[154],"achieves":[156],"state-of-the-art":[157],"significant":[161],"improvement":[162],"over":[163],"other":[164],"on":[167],"two":[168],"benchmarks.":[171]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2025,"cited_by_count":5},{"year":2024,"cited_by_count":3},{"year":2023,"cited_by_count":8},{"year":2022,"cited_by_count":2},{"year":2021,"cited_by_count":3}],"updated_date":"2026-03-04T09:10:02.777135","created_date":"2025-10-10T00:00:00"}