{"id":"https://openalex.org/W4385565718","doi":"https://doi.org/10.1145/3580305.3599371","title":"Graph Neural Bandits","display_name":"Graph Neural Bandits","publication_year":2023,"publication_date":"2023-08-04","ids":{"openalex":"https://openalex.org/W4385565718","doi":"https://doi.org/10.1145/3580305.3599371"},"language":"en","primary_location":{"id":"doi:10.1145/3580305.3599371","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3580305.3599371","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","raw_type":"proceedings-article"},"type":"article","indexed_in":["arxiv","crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2308.10808","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5008489037","display_name":"Yunzhe Qi","orcid":"https://orcid.org/0000-0001-5828-7436"},"institutions":[{"id":"https://openalex.org/I157725225","display_name":"University of Illinois Urbana-Champaign","ror":"https://ror.org/047426m28","country_code":"US","type":"education","lineage":["https://openalex.org/I157725225"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Yunzhe Qi","raw_affiliation_strings":["University of Illinois at Urbana-Champaign, Champaign, IL, USA"],"affiliations":[{"raw_affiliation_string":"University of Illinois at Urbana-Champaign, Champaign, IL, USA","institution_ids":["https://openalex.org/I157725225"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5047387636","display_name":"Yikun Ban","orcid":"https://orcid.org/0000-0003-3035-4849"},"institutions":[{"id":"https://openalex.org/I157725225","display_name":"University of Illinois Urbana-Champaign","ror":"https://ror.org/047426m28","country_code":"US","type":"education","lineage":["https://openalex.org/I157725225"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Yikun Ban","raw_affiliation_strings":["University of Illinois at Urbana-Champaign, Champaign, IL, USA"],"affiliations":[{"raw_affiliation_string":"University of Illinois at Urbana-Champaign, Champaign, IL, USA","institution_ids":["https://openalex.org/I157725225"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5073158087","display_name":"Jingrui He","orcid":"https://orcid.org/0000-0002-6429-6272"},"institutions":[{"id":"https://openalex.org/I157725225","display_name":"University of Illinois Urbana-Champaign","ror":"https://ror.org/047426m28","country_code":"US","type":"education","lineage":["https://openalex.org/I157725225"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Jingrui He","raw_affiliation_strings":["University of Illinois at Urbana-Champaign, Champaign, IL, USA"],"affiliations":[{"raw_affiliation_string":"University of Illinois at Urbana-Champaign, Champaign, IL, USA","institution_ids":["https://openalex.org/I157725225"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5008489037"],"corresponding_institution_ids":["https://openalex.org/I157725225"],"apc_list":null,"apc_paid":null,"fwci":1.9965,"has_fulltext":true,"cited_by_count":8,"citation_normalized_percentile":{"value":0.87150109,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":97,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"1920","last_page":"1931"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12101","display_name":"Advanced Bandit Algorithms Research","score":0.9997000098228455,"subfield":{"id":"https://openalex.org/subfields/1803","display_name":"Management Science and Operations Research"},"field":{"id":"https://openalex.org/fields/18","display_name":"Decision Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},"topics":[{"id":"https://openalex.org/T12101","display_name":"Advanced Bandit Algorithms Research","score":0.9997000098228455,"subfield":{"id":"https://openalex.org/subfields/1803","display_name":"Management Science and Operations Research"},"field":{"id":"https://openalex.org/fields/18","display_name":"Decision Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T10203","display_name":"Recommender Systems and Techniques","score":0.9983000159263611,"subfield":{"id":"https://openalex.org/subfields/1710","display_name":"Information Systems"},"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/T11612","display_name":"Stochastic Gradient Optimization Techniques","score":0.9883000254631042,"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":0.8035455942153931},{"id":"https://openalex.org/keywords/leverage","display_name":"Leverage (statistics)","score":0.7901787757873535},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5684478878974915},{"id":"https://openalex.org/keywords/dilemma","display_name":"Dilemma","score":0.5664588809013367},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.5633779764175415},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5041190385818481},{"id":"https://openalex.org/keywords/recommender-system","display_name":"Recommender system","score":0.5022246837615967},{"id":"https://openalex.org/keywords/collaborative-filtering","display_name":"Collaborative filtering","score":0.45663315057754517},{"id":"https://openalex.org/keywords/deep-neural-networks","display_name":"Deep neural networks","score":0.431557297706604},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.4277052879333496},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.4147798418998718},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3493359088897705},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.3347075283527374},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.0819983184337616}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8035455942153931},{"id":"https://openalex.org/C153083717","wikidata":"https://www.wikidata.org/wiki/Q6535263","display_name":"Leverage (statistics)","level":2,"score":0.7901787757873535},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5684478878974915},{"id":"https://openalex.org/C2778496695","wikidata":"https://www.wikidata.org/wiki/Q254128","display_name":"Dilemma","level":2,"score":0.5664588809013367},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.5633779764175415},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5041190385818481},{"id":"https://openalex.org/C557471498","wikidata":"https://www.wikidata.org/wiki/Q554950","display_name":"Recommender system","level":2,"score":0.5022246837615967},{"id":"https://openalex.org/C21569690","wikidata":"https://www.wikidata.org/wiki/Q94702","display_name":"Collaborative filtering","level":3,"score":0.45663315057754517},{"id":"https://openalex.org/C2984842247","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep neural networks","level":3,"score":0.431557297706604},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.4277052879333496},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.4147798418998718},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3493359088897705},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.3347075283527374},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.0819983184337616},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"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":2,"locations":[{"id":"doi:10.1145/3580305.3599371","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3580305.3599371","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","raw_type":"proceedings-article"},{"id":"pmh:oai:arXiv.org:2308.10808","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2308.10808","pdf_url":"https://arxiv.org/pdf/2308.10808","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by-nc-sa","license_id":"https://openalex.org/licenses/cc-by-nc-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2308.10808","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2308.10808","pdf_url":"https://arxiv.org/pdf/2308.10808","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by-nc-sa","license_id":"https://openalex.org/licenses/cc-by-nc-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G1366408413","display_name":null,"funder_award_id":"211790","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G2926720356","display_name":null,"funder_award_id":"32799","funder_id":"https://openalex.org/F4320332299","funder_display_name":"National Institute of Food and Agriculture"},{"id":"https://openalex.org/G360821608","display_name":"EAGER: Weakly Supervised Graph Neural Networks","funder_award_id":"2137468","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G4233114941","display_name":null,"funder_award_id":"1024178","funder_id":"https://openalex.org/F4320332299","funder_display_name":"National Institute of Food and Agriculture"},{"id":"https://openalex.org/G5620962805","display_name":null,"funder_award_id":"67021","funder_id":"https://openalex.org/F4320332299","funder_display_name":"National Institute of Food and Agriculture"},{"id":"https://openalex.org/G6770826516","display_name":null,"funder_award_id":"2020-67021-32799","funder_id":"https://openalex.org/F4320332299","funder_display_name":"National Institute of Food and Agriculture"},{"id":"https://openalex.org/G7286171126","display_name":null,"funder_award_id":"1947203","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G7374246537","display_name":"III: Small: RareXplain: A Computational Framework for Explainable Rare Category Analysis","funder_award_id":"2117902","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G8228424633","display_name":null,"funder_award_id":"IIS-1947203, IIS-2117902, IIS-2137468","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G848032724","display_name":null,"funder_award_id":"Science","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"}],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"},{"id":"https://openalex.org/F4320306114","display_name":"U.S. Department of Agriculture","ror":"https://ror.org/01na82s61"},{"id":"https://openalex.org/F4320332299","display_name":"National Institute of Food and Agriculture","ror":"https://ror.org/05qx3fv49"}],"has_content":{"pdf":true,"grobid_xml":false},"content_urls":{"pdf":"https://content.openalex.org/works/W4385565718.pdf"},"referenced_works_count":46,"referenced_works":["https://openalex.org/W1487320471","https://openalex.org/W1579652926","https://openalex.org/W1839697241","https://openalex.org/W1981698497","https://openalex.org/W2105828468","https://openalex.org/W2112420033","https://openalex.org/W2119738618","https://openalex.org/W2154455818","https://openalex.org/W2340290367","https://openalex.org/W2605350416","https://openalex.org/W2620047599","https://openalex.org/W2804057010","https://openalex.org/W2807021761","https://openalex.org/W2809090039","https://openalex.org/W2899748887","https://openalex.org/W2912801008","https://openalex.org/W2912998451","https://openalex.org/W2916106175","https://openalex.org/W2945827670","https://openalex.org/W2946840143","https://openalex.org/W2952575904","https://openalex.org/W2962818688","https://openalex.org/W2963588253","https://openalex.org/W2963695795","https://openalex.org/W2964015378","https://openalex.org/W2965212020","https://openalex.org/W3004578093","https://openalex.org/W3034511923","https://openalex.org/W3045200674","https://openalex.org/W3091650971","https://openalex.org/W3100278010","https://openalex.org/W3100848837","https://openalex.org/W3102163087","https://openalex.org/W3125733173","https://openalex.org/W3153860772","https://openalex.org/W3168472704","https://openalex.org/W3170945223","https://openalex.org/W3171739383","https://openalex.org/W3203675963","https://openalex.org/W4221155843","https://openalex.org/W4281750729","https://openalex.org/W4285598033","https://openalex.org/W4289389616","https://openalex.org/W4293412117","https://openalex.org/W4298328120","https://openalex.org/W6609612681"],"related_works":["https://openalex.org/W2772628444","https://openalex.org/W1484355083","https://openalex.org/W4220714703","https://openalex.org/W2098758514","https://openalex.org/W2735929803","https://openalex.org/W3008845055","https://openalex.org/W2170391450","https://openalex.org/W4376854386","https://openalex.org/W2202724490","https://openalex.org/W2508671622"],"abstract_inverted_index":{"Contextual":[0],"bandits":[1],"algorithms":[2,26],"aim":[3],"to":[4,30,34,59,100,136],"choose":[5],"the":[6,10,21,39,61,84,102,138],"optimal":[7],"arm":[8],"with":[9,131],"highest":[11],"reward":[12],"out":[13],"of":[14,17,37,73,94,140],"a":[15,52],"set":[16],"candidates":[18],"based":[19],"on":[20,110,124],"contextual":[22],"information.":[23],"Various":[24],"bandit":[25],"have":[27],"been":[28],"applied":[29],"real-world":[31],"applications":[32],"due":[33],"their":[35],"ability":[36],"tackling":[38],"exploitation-exploration":[40],"dilemma.":[41],"Motivated":[42],"by":[43,67],"online":[44],"recommendation":[45,103],"scenarios,":[46],"in":[47,79,92,129],"this":[48],"paper,":[49],"we":[50,82,105],"propose":[51],"framework":[53],"named":[54],"Graph":[55],"Neural":[56],"Bandits":[57],"(GNB)":[58],"leverage":[60],"collaborative":[62,86],"nature":[63],"among":[64],"users":[65],"empowered":[66],"graph":[68],"neural":[69],"networks":[70],"(GNNs).":[71],"Instead":[72],"estimating":[74],"rigid":[75],"user":[76,90,112],"clusters":[77],"as":[78],"existing":[80],"works,":[81],"model":[83],"\"fine-grained\"":[85],"effects":[87],"through":[88],"estimated":[89,111],"graphs":[91,113],"terms":[93],"exploitation":[95,115],"and":[96,116,121],"exploration":[97],"respectively.":[98],"Then,":[99],"refine":[101],"strategy,":[104],"utilize":[106],"separate":[107],"GNN-based":[108],"models":[109],"for":[114],"adaptive":[117],"exploration.":[118],"Theoretical":[119],"analysis":[120],"experimental":[122],"results":[123],"multiple":[125],"real":[126],"data":[127],"sets":[128],"comparison":[130],"state-of-the-art":[132],"baselines":[133],"are":[134],"provided":[135],"demonstrate":[137],"effectiveness":[139],"our":[141],"proposed":[142],"framework.":[143]},"counts_by_year":[{"year":2025,"cited_by_count":4},{"year":2024,"cited_by_count":4}],"updated_date":"2026-04-10T15:06:20.359241","created_date":"2025-10-10T00:00:00"}
