{"id":"https://openalex.org/W4389310762","doi":"https://doi.org/10.1145/3635473","title":"Learning Hierarchical Task Structures for Few-shot Graph Classification","display_name":"Learning Hierarchical Task Structures for Few-shot Graph Classification","publication_year":2023,"publication_date":"2023-12-04","ids":{"openalex":"https://openalex.org/W4389310762","doi":"https://doi.org/10.1145/3635473"},"language":"en","primary_location":{"id":"doi:10.1145/3635473","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3635473","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3635473","source":{"id":"https://openalex.org/S41523882","display_name":"ACM Transactions on Knowledge Discovery from Data","issn_l":"1556-4681","issn":["1556-4681","1556-472X"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319798","host_organization_name":"Association for Computing Machinery","host_organization_lineage":["https://openalex.org/P4310319798"],"host_organization_lineage_names":["Association for Computing Machinery"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ACM Transactions on Knowledge Discovery from Data","raw_type":"journal-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"hybrid","oa_url":"https://dl.acm.org/doi/pdf/10.1145/3635473","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5100326218","display_name":"Song Wang","orcid":"https://orcid.org/0000-0003-1273-7694"},"institutions":[{"id":"https://openalex.org/I51556381","display_name":"University of Virginia","ror":"https://ror.org/0153tk833","country_code":"US","type":"education","lineage":["https://openalex.org/I51556381"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Song Wang","raw_affiliation_strings":["University of Virginia, USA"],"raw_orcid":"https://orcid.org/0000-0003-1273-7694","affiliations":[{"raw_affiliation_string":"University of Virginia, USA","institution_ids":["https://openalex.org/I51556381"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5047581320","display_name":"Yushun Dong","orcid":"https://orcid.org/0000-0001-7504-6159"},"institutions":[{"id":"https://openalex.org/I51556381","display_name":"University of Virginia","ror":"https://ror.org/0153tk833","country_code":"US","type":"education","lineage":["https://openalex.org/I51556381"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Yushun Dong","raw_affiliation_strings":["University of Virginia, USA"],"raw_orcid":"https://orcid.org/0000-0001-7504-6159","affiliations":[{"raw_affiliation_string":"University of Virginia, USA","institution_ids":["https://openalex.org/I51556381"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5055037545","display_name":"Xiao Huang","orcid":"https://orcid.org/0000-0002-3867-900X"},"institutions":[{"id":"https://openalex.org/I14243506","display_name":"Hong Kong Polytechnic University","ror":"https://ror.org/0030zas98","country_code":"HK","type":"education","lineage":["https://openalex.org/I14243506"]}],"countries":["HK"],"is_corresponding":false,"raw_author_name":"Xiao Huang","raw_affiliation_strings":["Hong Kong Polytechnic University, Hong Kong"],"raw_orcid":"https://orcid.org/0000-0002-3867-900X","affiliations":[{"raw_affiliation_string":"Hong Kong Polytechnic University, Hong Kong","institution_ids":["https://openalex.org/I14243506"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100418485","display_name":"Chen Chen","orcid":"https://orcid.org/0000-0002-7099-7905"},"institutions":[{"id":"https://openalex.org/I51556381","display_name":"University of Virginia","ror":"https://ror.org/0153tk833","country_code":"US","type":"education","lineage":["https://openalex.org/I51556381"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Chen Chen","raw_affiliation_strings":["University of Virginia, USA"],"raw_orcid":"https://orcid.org/0000-0002-7099-7905","affiliations":[{"raw_affiliation_string":"University of Virginia, USA","institution_ids":["https://openalex.org/I51556381"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5029588473","display_name":"Jundong Li","orcid":"https://orcid.org/0000-0002-1878-817X"},"institutions":[{"id":"https://openalex.org/I51556381","display_name":"University of Virginia","ror":"https://ror.org/0153tk833","country_code":"US","type":"education","lineage":["https://openalex.org/I51556381"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Jundong Li","raw_affiliation_strings":["University of Virginia, USA"],"raw_orcid":"https://orcid.org/0000-0002-1878-817X","affiliations":[{"raw_affiliation_string":"University of Virginia, USA","institution_ids":["https://openalex.org/I51556381"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.7674,"has_fulltext":true,"cited_by_count":6,"citation_normalized_percentile":{"value":0.77527809,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":99},"biblio":{"volume":"18","issue":"3","first_page":"1","last_page":"20"},"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":0.9986000061035156,"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"}},"topics":[{"id":"https://openalex.org/T11307","display_name":"Domain Adaptation and Few-Shot Learning","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/T11273","display_name":"Advanced Graph Neural Networks","score":0.9890000224113464,"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":0.6912415623664856},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.6193986535072327},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.5674383044242859},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5612058639526367},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5420895218849182},{"id":"https://openalex.org/keywords/disjoint-sets","display_name":"Disjoint sets","score":0.44238805770874023},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.321747750043869},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.19668903946876526},{"id":"https://openalex.org/keywords/combinatorics","display_name":"Combinatorics","score":0.0828506350517273}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6912415623664856},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.6193986535072327},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.5674383044242859},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5612058639526367},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5420895218849182},{"id":"https://openalex.org/C45340560","wikidata":"https://www.wikidata.org/wiki/Q215382","display_name":"Disjoint sets","level":2,"score":0.44238805770874023},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.321747750043869},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.19668903946876526},{"id":"https://openalex.org/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","level":1,"score":0.0828506350517273}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3635473","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3635473","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3635473","source":{"id":"https://openalex.org/S41523882","display_name":"ACM Transactions on Knowledge Discovery from Data","issn_l":"1556-4681","issn":["1556-4681","1556-472X"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319798","host_organization_name":"Association for Computing Machinery","host_organization_lineage":["https://openalex.org/P4310319798"],"host_organization_lineage_names":["Association for Computing Machinery"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ACM Transactions on Knowledge Discovery from Data","raw_type":"journal-article"}],"best_oa_location":{"id":"doi:10.1145/3635473","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3635473","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3635473","source":{"id":"https://openalex.org/S41523882","display_name":"ACM Transactions on Knowledge Discovery from Data","issn_l":"1556-4681","issn":["1556-4681","1556-472X"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319798","host_organization_name":"Association for Computing Machinery","host_organization_lineage":["https://openalex.org/P4310319798"],"host_organization_lineage_names":["Association for Computing Machinery"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ACM Transactions on Knowledge Discovery from Data","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G4932945845","display_name":"CAREER: Toward A Knowledge-Guided Framework for Personalized Decision Making","funder_award_id":"2144209","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G7344714104","display_name":"Collaborative Research: SAI-R: Dynamical Coupling of Physical and Social Infrastructures: Evaluating the Impacts of Social Capital on Access to Safe Well Water","funder_award_id":"2228534","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G7378744750","display_name":"III: Small: Collaborative Research: Demystifying Deep Learning on Graphs: From Basic Operations to Applications","funder_award_id":"2006844","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G984565250","display_name":null,"funder_award_id":"HV-2Q23-003, VV-1Q23-007","funder_id":"https://openalex.org/F328363578","funder_display_name":"Commonwealth Cyber Initiative"}],"funders":[{"id":"https://openalex.org/F328363578","display_name":"Commonwealth Cyber Initiative","ror":null},{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"},{"id":"https://openalex.org/F4320307791","display_name":"Cisco Systems","ror":"https://ror.org/03yt1ez60"},{"id":"https://openalex.org/F4320338382","display_name":"Thomas Jefferson National Accelerator Facility","ror":"https://ror.org/02vwzrd76"}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4389310762.pdf","grobid_xml":"https://content.openalex.org/works/W4389310762.grobid-xml"},"referenced_works_count":28,"referenced_works":["https://openalex.org/W1492230849","https://openalex.org/W1501856433","https://openalex.org/W2008056655","https://openalex.org/W2062797058","https://openalex.org/W2415243320","https://openalex.org/W2737925311","https://openalex.org/W2889234142","https://openalex.org/W2906943923","https://openalex.org/W2963703618","https://openalex.org/W2964105864","https://openalex.org/W2964316912","https://openalex.org/W2984323660","https://openalex.org/W2994972767","https://openalex.org/W2997198750","https://openalex.org/W3080670519","https://openalex.org/W3093957844","https://openalex.org/W3094500523","https://openalex.org/W3094624443","https://openalex.org/W3108009835","https://openalex.org/W3130274530","https://openalex.org/W3152893301","https://openalex.org/W3165369424","https://openalex.org/W3194562193","https://openalex.org/W3208465763","https://openalex.org/W4212857616","https://openalex.org/W4281263494","https://openalex.org/W4287991470","https://openalex.org/W6743446608"],"related_works":["https://openalex.org/W4256429076","https://openalex.org/W1971174658","https://openalex.org/W2099195351","https://openalex.org/W2348092930","https://openalex.org/W2012063272","https://openalex.org/W3129272755","https://openalex.org/W2138685729","https://openalex.org/W2587900963","https://openalex.org/W26693340","https://openalex.org/W1963777171"],"abstract_inverted_index":{"The":[0,206],"problem":[1,27],"of":[2,67,193,218],"few-shot":[3,40,151,183],"graph":[4,12,48,152,184,212],"classification":[5,118,153,185],"targets":[6],"at":[7,166],"assigning":[8],"class":[9,82],"labels":[10],"for":[11,21,201],"samples,":[13],"where":[14],"only":[15],"limited":[16,51],"labeled":[17,52],"graphs":[18],"are":[19],"provided":[20],"each":[22],"class.":[23],"To":[24,143],"solve":[25],"the":[26,38,89,108,121,135,177,182,198,202,216],"brought":[28],"by":[29],"label":[30],"scarcity,":[31],"recent":[32],"studies":[33,57,86],"have":[34],"proposed":[35],"to":[36,43,47,60,75,111,127,134,156,175],"adopt":[37],"prevalent":[39,211],"learning":[41,161],"framework":[42,154],"achieve":[44],"fast":[45],"adaptations":[46],"classes":[49],"with":[50,145],"graphs.":[53],"In":[54,100],"particular,":[55],"these":[56],"typically":[58],"propose":[59,148,171],"accumulate":[61],"meta-knowledge":[62,74],"across":[63],"a":[64,80,149,162,172,191],"large":[65],"number":[66],"meta-training":[68,94,141],"tasks,":[69],"and":[70,96,114,129,138,220],"then":[71],"generalize":[72],"such":[73,102],"meta-test":[76,112],"tasks":[77,95,113],"sampled":[78],"from":[79],"disjoint":[81],"set.":[83],"Nevertheless,":[84],"existing":[85],"generally":[87],"ignore":[88],"crucial":[90],"task":[91,103,131,158,164,179,204],"correlations":[92,104,132,159,180],"among":[93],"treat":[97],"them":[98],"independently.":[99],"fact,":[101],"can":[105,196],"help":[106],"promote":[107],"model":[109],"generalization":[110],"result":[115],"in":[116,140],"better":[117],"performance.":[119],"On":[120],"other":[122,223],"hand,":[123],"it":[124],"remains":[125],"challenging":[126],"capture":[128,157],"utilize":[130],"due":[133],"complex":[136],"components":[137],"interactions":[139],"tasks.":[142],"deal":[144],"this,":[146],"we":[147,188],"novel":[150],"FAITH":[155,194,219],"via":[160],"hierarchical":[163,203],"structure":[165],"different":[167],"granularities.":[168],"We":[169],"further":[170,214],"task-specific":[173],"classifier":[174],"incorporate":[176],"learned":[178],"into":[181],"process.":[186],"Moreover,":[187],"derive":[189],"FAITH+,":[190],"variant":[192],"that":[195],"improve":[197],"sampling":[199],"process":[200],"structure.":[205],"extensive":[207],"experiments":[208],"on":[209],"four":[210],"datasets":[213],"demonstrate":[215],"superiority":[217],"FAITH+":[221],"over":[222],"state-of-the-art":[224],"baselines.":[225]},"counts_by_year":[{"year":2026,"cited_by_count":3},{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":1}],"updated_date":"2026-08-12T21:12:35.861297","created_date":"2025-10-10T00:00:00"}