{"id":"https://openalex.org/W4290927864","doi":"https://doi.org/10.1145/3534678.3539038","title":"Graph Neural Network Training and Data Tiering","display_name":"Graph Neural Network Training and Data Tiering","publication_year":2022,"publication_date":"2022-08-12","ids":{"openalex":"https://openalex.org/W4290927864","doi":"https://doi.org/10.1145/3534678.3539038"},"language":"en","primary_location":{"id":"doi:10.1145/3534678.3539038","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3534678.3539038","pdf_url":null,"source":{"id":"https://openalex.org/S4363608767","display_name":"Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","raw_type":"proceedings-article"},"type":"conference-paper","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/A5037706821","display_name":"Seungwon Min","orcid":"https://orcid.org/0000-0001-7195-7182"},"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":"Seung Won Min","raw_affiliation_strings":["University of Illinois at Urbana-Champaign, Urbana, IL, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Illinois at Urbana-Champaign, Urbana, IL, USA","institution_ids":["https://openalex.org/I157725225"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5073106039","display_name":"Kun Wu","orcid":"https://orcid.org/0000-0002-0149-1409"},"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":"Kun Wu","raw_affiliation_strings":["University of Illinois at Urbana-Champaign, Urbana, IL, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Illinois at Urbana-Champaign, Urbana, IL, USA","institution_ids":["https://openalex.org/I157725225"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5024169920","display_name":"Mert Hidayeto\u011flu","orcid":"https://orcid.org/0000-0001-9276-5075"},"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":"Mert Hidayetoglu","raw_affiliation_strings":["University of Illinois at Urbana-Champaign, Urbana, IL, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Illinois at Urbana-Champaign, Urbana, IL, USA","institution_ids":["https://openalex.org/I157725225"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5030156276","display_name":"Jinjun Xiong","orcid":"https://orcid.org/0000-0002-2620-4859"},"institutions":[{"id":"https://openalex.org/I63190737","display_name":"University at Buffalo, State University of New York","ror":"https://ror.org/01y64my43","country_code":"US","type":"education","lineage":["https://openalex.org/I63190737"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Jinjun Xiong","raw_affiliation_strings":["University at Buffalo, Buffalo, NY, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University at Buffalo, Buffalo, NY, USA","institution_ids":["https://openalex.org/I63190737"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5064457872","display_name":"Xiang Song","orcid":"https://orcid.org/0000-0002-1704-4339"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Xiang Song","raw_affiliation_strings":["AWS AI Research and Education, Santa Clara, CA, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"AWS AI Research and Education, Santa Clara, CA, USA","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5040404999","display_name":"Wen\u2010mei Hwu","orcid":"https://orcid.org/0000-0003-2532-5349"},"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":"Wen-mei Hwu","raw_affiliation_strings":["University of Illinois at Urbana-Champaign &amp; NVIDIA, Urbana, IL, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Illinois at Urbana-Champaign &amp; NVIDIA, Urbana, IL, USA","institution_ids":["https://openalex.org/I157725225"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":15,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"3555","last_page":"3565"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11273","display_name":"Advanced Graph Neural Networks","score":1.0,"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":1.0,"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/T12292","display_name":"Graph Theory and Algorithms","score":0.9891999959945679,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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/T10203","display_name":"Recommender Systems and Techniques","score":0.9731000065803528,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8685320615768433},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.6497066617012024},{"id":"https://openalex.org/keywords/training-set","display_name":"Training set","score":0.49076399207115173},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.48900163173675537},{"id":"https://openalex.org/keywords/overhead","display_name":"Overhead (engineering)","score":0.4749320149421692},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.4352375268936157},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3600701093673706},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.35894840955734253},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.35242268443107605},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.3141154944896698},{"id":"https://openalex.org/keywords/operating-system","display_name":"Operating system","score":0.10051426291465759}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8685320615768433},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.6497066617012024},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.49076399207115173},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.48900163173675537},{"id":"https://openalex.org/C2779960059","wikidata":"https://www.wikidata.org/wiki/Q7113681","display_name":"Overhead (engineering)","level":2,"score":0.4749320149421692},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.4352375268936157},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3600701093673706},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.35894840955734253},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.35242268443107605},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.3141154944896698},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.10051426291465759}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3534678.3539038","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3534678.3539038","pdf_url":null,"source":{"id":"https://openalex.org/S4363608767","display_name":"Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/16","score":0.7200000286102295,"display_name":"Peace, Justice and strong institutions"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":9,"referenced_works":["https://openalex.org/W2069360577","https://openalex.org/W2728059831","https://openalex.org/W2914721378","https://openalex.org/W3022945404","https://openalex.org/W3086105743","https://openalex.org/W3096566397","https://openalex.org/W3099825604","https://openalex.org/W3167254314","https://openalex.org/W3198239267"],"related_works":["https://openalex.org/W2952348651","https://openalex.org/W2375742443","https://openalex.org/W2391251536","https://openalex.org/W2149381099","https://openalex.org/W4200520489","https://openalex.org/W1483190388","https://openalex.org/W2362198218","https://openalex.org/W3147767149","https://openalex.org/W2061536531","https://openalex.org/W1982750869"],"abstract_inverted_index":{"Graph":[0],"Neural":[1],"Networks":[2],"(GNNs)":[3],"have":[4],"shown":[5],"success":[6],"in":[7,142],"learning":[8],"from":[9,90],"graph-structured":[10],"data,":[11],"with":[12,173],"applications":[13],"to":[14,60,95,116],"fraud":[15],"detection,":[16],"recommendation,":[17],"and":[18,35,42,63,113,134,158,179],"knowledge":[19],"graph":[20],"reasoning.":[21],"However,":[22],"training":[23,93,131,161],"GNN":[24,71,92,130,164],"efficiently":[25],"is":[26,33],"challenging":[27],"because:":[28],"1)":[29],"GPU":[30],"memory":[31],"capacity":[32],"limited":[34],"can":[36],"be":[37],"insufficient":[38],"for":[39],"large":[40],"datasets,":[41],"2)":[43],"the":[44,80,119,137,160,166],"graph-based":[45],"data":[46,50,68,74,103,111],"structure":[47,81],"causes":[48],"irregular":[49],"access":[51,114],"patterns.":[52],"In":[53],"this":[54],"work,":[55],"we":[56,106,135],"provide":[57,108],"a":[58,97,109,143],"method":[59,76],"statistically":[61],"analyze":[62],"identify":[64],"more":[65],"frequently":[66],"accessed":[67],"ahead":[69],"of":[70,82,128,139,163,175,177,181],"training.":[72],"Our":[73],"tiering":[75,104],"not":[77],"only":[78],"utilizes":[79],"input":[83],"graph,":[84],"but":[85],"also":[86,124],"an":[87],"insight":[88],"gained":[89],"actual":[91],"process":[94],"achieve":[96],"higher":[98],"prediction":[99],"result.":[100],"With":[101],"our":[102,140,151],"method,":[105],"additionally":[107],"new":[110],"placement":[112],"strategy":[115,141],"further":[117],"minimize":[118],"CPU-GPU":[120,154],"communication":[121],"overhead.":[122],"We":[123],"take":[125],"into":[126],"account":[127],"multi-GPU":[129,144],"as":[132],"well":[133],"demonstrate":[136],"effectiveness":[138],"system.":[145],"The":[146],"evaluation":[147],"results":[148],"show":[149],"that":[150],"work":[152],"reduces":[153],"traffic":[155],"by":[156,169],"87-95%":[157],"improves":[159],"speed":[162],"over":[165],"existing":[167],"solutions":[168],"1.6-2.1x":[170],"on":[171],"graphs":[172],"hundreds":[174],"millions":[176],"nodes":[178],"billions":[180],"edges.":[182]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2025,"cited_by_count":5},{"year":2024,"cited_by_count":6},{"year":2023,"cited_by_count":2}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}
