{"id":"https://openalex.org/W4379806241","doi":"https://doi.org/10.1145/3591106.3592293","title":"TAGM: Task-Aware Graph Model for Few-shot Node Classification","display_name":"TAGM: Task-Aware Graph Model for Few-shot Node Classification","publication_year":2023,"publication_date":"2023-06-08","ids":{"openalex":"https://openalex.org/W4379806241","doi":"https://doi.org/10.1145/3591106.3592293"},"language":"en","primary_location":{"id":"doi:10.1145/3591106.3592293","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3591106.3592293","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3591106.3592293","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2023 ACM International Conference on Multimedia Retrieval","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://dl.acm.org/doi/pdf/10.1145/3591106.3592293","any_repository_has_fulltext":null},"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/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":["Westlake University, China"],"affiliations":[{"raw_affiliation_string":"Westlake University, China","institution_ids":["https://openalex.org/I3133055985"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5106710554","display_name":"Min Zhang","orcid":"https://orcid.org/0000-0003-1321-0050"},"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":"Min Zhang","raw_affiliation_strings":["Westlake University, China"],"affiliations":[{"raw_affiliation_string":"Westlake University, China","institution_ids":["https://openalex.org/I3133055985"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5059505330","display_name":"Tiancheng Huang","orcid":"https://orcid.org/0000-0001-7430-6086"},"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":"Tiancheng Huang","raw_affiliation_strings":["Westlake University, China"],"affiliations":[{"raw_affiliation_string":"Westlake University, China","institution_ids":["https://openalex.org/I3133055985"]}]},{"author_position":"last","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, China"],"affiliations":[{"raw_affiliation_string":"Westlake University, China","institution_ids":["https://openalex.org/I3133055985"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5005677589"],"corresponding_institution_ids":["https://openalex.org/I3133055985"],"apc_list":null,"apc_paid":null,"fwci":0.1746,"has_fulltext":true,"cited_by_count":1,"citation_normalized_percentile":{"value":0.52976098,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":94},"biblio":{"volume":null,"issue":null,"first_page":"462","last_page":"471"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11273","display_name":"Advanced Graph Neural Networks","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/T11273","display_name":"Advanced Graph Neural Networks","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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.9796000123023987,"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/computer-science","display_name":"Computer science","score":0.7813433408737183},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.5661766529083252},{"id":"https://openalex.org/keywords/feature-learning","display_name":"Feature learning","score":0.5501735806465149},{"id":"https://openalex.org/keywords/graph-embedding","display_name":"Graph embedding","score":0.5293406248092651},{"id":"https://openalex.org/keywords/encode","display_name":"ENCODE","score":0.49144232273101807},{"id":"https://openalex.org/keywords/embedding","display_name":"Embedding","score":0.49026063084602356},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4898214042186737},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.387381374835968},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.346365749835968},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3297027349472046}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7813433408737183},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.5661766529083252},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.5501735806465149},{"id":"https://openalex.org/C75564084","wikidata":"https://www.wikidata.org/wiki/Q5597085","display_name":"Graph embedding","level":3,"score":0.5293406248092651},{"id":"https://openalex.org/C66746571","wikidata":"https://www.wikidata.org/wiki/Q1134833","display_name":"ENCODE","level":3,"score":0.49144232273101807},{"id":"https://openalex.org/C41608201","wikidata":"https://www.wikidata.org/wiki/Q980509","display_name":"Embedding","level":2,"score":0.49026063084602356},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4898214042186737},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.387381374835968},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.346365749835968},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3297027349472046},{"id":"https://openalex.org/C55493867","wikidata":"https://www.wikidata.org/wiki/Q7094","display_name":"Biochemistry","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/C104317684","wikidata":"https://www.wikidata.org/wiki/Q7187","display_name":"Gene","level":2,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3591106.3592293","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3591106.3592293","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3591106.3592293","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2023 ACM International Conference on Multimedia Retrieval","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.1145/3591106.3592293","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3591106.3592293","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3591106.3592293","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2023 ACM International Conference on Multimedia Retrieval","raw_type":"proceedings-article"},"sustainable_development_goals":[{"score":0.5099999904632568,"display_name":"Decent work and economic growth","id":"https://metadata.un.org/sdg/8"}],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4379806241.pdf","grobid_xml":"https://content.openalex.org/works/W4379806241.grobid-xml"},"referenced_works_count":31,"referenced_works":["https://openalex.org/W1533841329","https://openalex.org/W1888005072","https://openalex.org/W2154851992","https://openalex.org/W2625674597","https://openalex.org/W2798836702","https://openalex.org/W2949879676","https://openalex.org/W2962756421","https://openalex.org/W2963680240","https://openalex.org/W2964105864","https://openalex.org/W2984323660","https://openalex.org/W2997198750","https://openalex.org/W3002924435","https://openalex.org/W3039075121","https://openalex.org/W3080670519","https://openalex.org/W3100993589","https://openalex.org/W3104097132","https://openalex.org/W3110608229","https://openalex.org/W3127690492","https://openalex.org/W3130274530","https://openalex.org/W3165369424","https://openalex.org/W3169024950","https://openalex.org/W3172133997","https://openalex.org/W3198104617","https://openalex.org/W3202469562","https://openalex.org/W3208981412","https://openalex.org/W3211423998","https://openalex.org/W3211477647","https://openalex.org/W4212857616","https://openalex.org/W4213091601","https://openalex.org/W4281263494","https://openalex.org/W4282942365"],"related_works":["https://openalex.org/W3036264823","https://openalex.org/W3206528106","https://openalex.org/W2123605750","https://openalex.org/W2912814903","https://openalex.org/W2088740331","https://openalex.org/W2950907416","https://openalex.org/W3038102983","https://openalex.org/W2082479932","https://openalex.org/W2932872266","https://openalex.org/W4281484020"],"abstract_inverted_index":{"Graph":[0,117],"representation":[1,30],"learning":[2,31,48,71],"has":[3,49],"attracted":[4],"tremendous":[5],"attention":[6],"due":[7],"to":[8,52,78,90,120,163,211],"its":[9],"remarkable":[10],"performance":[11,55],"in":[12,57,67,105,198,239],"variety":[13],"of":[14,44,59,102],"real-world":[15],"applications.":[16],"However,":[17],"because":[18,101],"data":[19,62],"labeling":[20],"is":[21,208],"always":[22],"time":[23],"and":[24,141,170,186,214],"resource":[25],"intensive,":[26],"current":[27],"supervised":[28],"graph":[29,46,68,130,136,143,220],"models":[32],"for":[33,97,139,152,183,217],"particular":[34],"tasks":[35,185],"frequently":[36],"suffer":[37],"from":[38],"label":[39],"sparsity":[40],"issues.":[41],"In":[42,109],"light":[43],"this,":[45],"few-shot":[47,240],"been":[50],"proposed":[51,232],"tackle":[53],"the":[54,98,103,165,175,187,194,203,236],"degradation":[56],"face":[58],"limited":[60],"annotated":[61],"challenge.":[63],"While":[64],"recent":[65],"advances":[66],"few":[69],"shot":[70],"achieve":[72],"promising":[73],"performance,":[74],"they":[75],"typically":[76],"force":[77],"use":[79],"a":[80,114,128,135,148,199,218],"generic":[81],"feature":[82,92,154],"embedding":[83],"across":[84],"various":[85],"tasks.":[86,108],"Ideally,":[87],"we":[88,112,126,159],"want":[89],"construct":[91],"embeddings":[93],"that":[94,133,230],"are":[95],"tuned":[96],"given":[99],"task":[100,161,212,219],"differences":[104],"distribution":[106],"between":[107],"this":[110,157],"work,":[111],"propose":[113],"novel":[115],"Task-Aware":[116],"Model":[118],"(TAGM)":[119],"learn":[121,164],"task-aware":[122],"node":[123,153],"embedding.":[124],"Specifically,":[125],"provide":[127],"new":[129],"cell":[131],"design":[132],"includes":[134],"convolution":[137],"layer":[138],"aggregating":[140],"updating":[142],"information":[144,162],"as":[145,147],"well":[146],"two-layer":[149],"linear":[150],"transformation":[151],"transformation.":[155],"On":[156],"basis,":[158],"encode":[160],"binary":[166],"weight":[167,176],"mask":[168,172,177,189],"set":[169,178,190],"gradient":[171,188],"set,":[173],"where":[174],"selects":[179],"different":[180,184,200],"network":[181,196],"parameters":[182,197],"can":[191],"dynamically":[192],"update":[193],"selected":[195],"manner":[201],"during":[202],"optimization":[204],"process.":[205],"Our":[206,222],"model":[207],"more":[209],"sensitive":[210],"identity":[213],"performs":[215],"better":[216],"input.":[221],"extensive":[223],"experiments":[224],"on":[225],"three":[226],"graph-structured":[227],"datasets":[228],"demonstrate":[229],"our":[231],"method":[233],"generally":[234],"outperforms":[235],"state-of-the-art":[237],"baselines":[238],"learning.":[241]},"counts_by_year":[{"year":2024,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}