{"id":"https://openalex.org/W4283326779","doi":"https://doi.org/10.1145/3534678.3539423","title":"Nimble GNN Embedding with Tensor-Train Decomposition","display_name":"Nimble GNN Embedding with Tensor-Train Decomposition","publication_year":2022,"publication_date":"2022-08-12","ids":{"openalex":"https://openalex.org/W4283326779","doi":"https://doi.org/10.1145/3534678.3539423"},"language":"en","primary_location":{"id":"doi:10.1145/3534678.3539423","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3534678.3539423","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3534678.3539423","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":true,"oa_status":"bronze","oa_url":"https://dl.acm.org/doi/pdf/10.1145/3534678.3539423","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5029521417","display_name":"Chunxing Yin","orcid":null},"institutions":[{"id":"https://openalex.org/I130701444","display_name":"Georgia Institute of Technology","ror":"https://ror.org/01zkghx44","country_code":"US","type":"education","lineage":["https://openalex.org/I130701444"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Chunxing Yin","raw_affiliation_strings":["Georgia Institute of Technology, Atlanta, GA, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Georgia Institute of Technology, Atlanta, GA, USA","institution_ids":["https://openalex.org/I130701444"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5060763203","display_name":"Da Zheng","orcid":"https://orcid.org/0000-0001-8115-5415"},"institutions":[{"id":"https://openalex.org/I1311688040","display_name":"Amazon (United States)","ror":"https://ror.org/04mv4n011","country_code":"US","type":"company","lineage":["https://openalex.org/I1311688040"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Da Zheng","raw_affiliation_strings":["Amazon, Santa Clara, CA, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Amazon, Santa Clara, CA, USA","institution_ids":["https://openalex.org/I1311688040"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5063948428","display_name":"Israt Nisa","orcid":"https://orcid.org/0000-0001-5022-5716"},"institutions":[{"id":"https://openalex.org/I1311688040","display_name":"Amazon (United States)","ror":"https://ror.org/04mv4n011","country_code":"US","type":"company","lineage":["https://openalex.org/I1311688040"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Israt Nisa","raw_affiliation_strings":["Amazon, Santa Clara, CA, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Amazon, Santa Clara, CA, USA","institution_ids":["https://openalex.org/I1311688040"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5035605036","display_name":"Christos Faloutsos","orcid":"https://orcid.org/0000-0003-2996-9790"},"institutions":[{"id":"https://openalex.org/I1311688040","display_name":"Amazon (United States)","ror":"https://ror.org/04mv4n011","country_code":"US","type":"company","lineage":["https://openalex.org/I1311688040"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Christos Faloutsos","raw_affiliation_strings":["Amazon, Santa Clara, CA, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Amazon, Santa Clara, CA, USA","institution_ids":["https://openalex.org/I1311688040"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5082384108","display_name":"George Karypis","orcid":"https://orcid.org/0000-0003-2753-1437"},"institutions":[{"id":"https://openalex.org/I1311688040","display_name":"Amazon (United States)","ror":"https://ror.org/04mv4n011","country_code":"US","type":"company","lineage":["https://openalex.org/I1311688040"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"George Karypis","raw_affiliation_strings":["Amazon, Santa Clara, CA, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Amazon, Santa Clara, CA, USA","institution_ids":["https://openalex.org/I1311688040"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5016738770","display_name":"Richard Vuduc","orcid":"https://orcid.org/0000-0003-2178-138X"},"institutions":[{"id":"https://openalex.org/I130701444","display_name":"Georgia Institute of Technology","ror":"https://ror.org/01zkghx44","country_code":"US","type":"education","lineage":["https://openalex.org/I130701444"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Richard Vuduc","raw_affiliation_strings":["Georgia Institute of Technology, Atlanta, GA, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Georgia Institute of Technology, Atlanta, GA, USA","institution_ids":["https://openalex.org/I130701444"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":4.8706,"has_fulltext":true,"cited_by_count":17,"citation_normalized_percentile":{"value":0.97474747,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":94,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"2327","last_page":"2335"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12303","display_name":"Tensor decomposition and applications","score":0.9991000294685364,"subfield":{"id":"https://openalex.org/subfields/2605","display_name":"Computational Mathematics"},"field":{"id":"https://openalex.org/fields/26","display_name":"Mathematics"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T12303","display_name":"Tensor decomposition and applications","score":0.9991000294685364,"subfield":{"id":"https://openalex.org/subfields/2605","display_name":"Computational Mathematics"},"field":{"id":"https://openalex.org/fields/26","display_name":"Mathematics"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11273","display_name":"Advanced Graph Neural Networks","score":0.994700014591217,"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/T11612","display_name":"Stochastic Gradient Optimization Techniques","score":0.9598000049591064,"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":0.8296197652816772},{"id":"https://openalex.org/keywords/embedding","display_name":"Embedding","score":0.7497928142547607},{"id":"https://openalex.org/keywords/exploit","display_name":"Exploit","score":0.6793901920318604},{"id":"https://openalex.org/keywords/initialization","display_name":"Initialization","score":0.6481199264526367},{"id":"https://openalex.org/keywords/rendering","display_name":"Rendering (computer graphics)","score":0.6402214765548706},{"id":"https://openalex.org/keywords/tensor-decomposition","display_name":"Tensor decomposition","score":0.6197987794876099},{"id":"https://openalex.org/keywords/parallel-computing","display_name":"Parallel computing","score":0.5306336283683777},{"id":"https://openalex.org/keywords/bottleneck","display_name":"Bottleneck","score":0.48701220750808716},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.4810600280761719},{"id":"https://openalex.org/keywords/node","display_name":"Node (physics)","score":0.4708031415939331},{"id":"https://openalex.org/keywords/graph-embedding","display_name":"Graph embedding","score":0.4399423599243164},{"id":"https://openalex.org/keywords/tensor","display_name":"Tensor (intrinsic definition)","score":0.4332273006439209},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.43154042959213257},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.42513716220855713},{"id":"https://openalex.org/keywords/curse-of-dimensionality","display_name":"Curse of dimensionality","score":0.41523921489715576},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.35596704483032227},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.23877236247062683},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.09033030271530151},{"id":"https://openalex.org/keywords/embedded-system","display_name":"Embedded system","score":0.07828673720359802}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8296197652816772},{"id":"https://openalex.org/C41608201","wikidata":"https://www.wikidata.org/wiki/Q980509","display_name":"Embedding","level":2,"score":0.7497928142547607},{"id":"https://openalex.org/C165696696","wikidata":"https://www.wikidata.org/wiki/Q11287","display_name":"Exploit","level":2,"score":0.6793901920318604},{"id":"https://openalex.org/C114466953","wikidata":"https://www.wikidata.org/wiki/Q6034165","display_name":"Initialization","level":2,"score":0.6481199264526367},{"id":"https://openalex.org/C205711294","wikidata":"https://www.wikidata.org/wiki/Q176953","display_name":"Rendering (computer graphics)","level":2,"score":0.6402214765548706},{"id":"https://openalex.org/C2986737658","wikidata":"https://www.wikidata.org/wiki/Q30103009","display_name":"Tensor decomposition","level":3,"score":0.6197987794876099},{"id":"https://openalex.org/C173608175","wikidata":"https://www.wikidata.org/wiki/Q232661","display_name":"Parallel computing","level":1,"score":0.5306336283683777},{"id":"https://openalex.org/C2780513914","wikidata":"https://www.wikidata.org/wiki/Q18210350","display_name":"Bottleneck","level":2,"score":0.48701220750808716},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.4810600280761719},{"id":"https://openalex.org/C62611344","wikidata":"https://www.wikidata.org/wiki/Q1062658","display_name":"Node (physics)","level":2,"score":0.4708031415939331},{"id":"https://openalex.org/C75564084","wikidata":"https://www.wikidata.org/wiki/Q5597085","display_name":"Graph embedding","level":3,"score":0.4399423599243164},{"id":"https://openalex.org/C155281189","wikidata":"https://www.wikidata.org/wiki/Q3518150","display_name":"Tensor (intrinsic definition)","level":2,"score":0.4332273006439209},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.43154042959213257},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.42513716220855713},{"id":"https://openalex.org/C111030470","wikidata":"https://www.wikidata.org/wiki/Q1430460","display_name":"Curse of dimensionality","level":2,"score":0.41523921489715576},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.35596704483032227},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.23877236247062683},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.09033030271530151},{"id":"https://openalex.org/C149635348","wikidata":"https://www.wikidata.org/wiki/Q193040","display_name":"Embedded system","level":1,"score":0.07828673720359802},{"id":"https://openalex.org/C66938386","wikidata":"https://www.wikidata.org/wiki/Q633538","display_name":"Structural engineering","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},{"id":"https://openalex.org/C202444582","wikidata":"https://www.wikidata.org/wiki/Q837863","display_name":"Pure mathematics","level":1,"score":0.0},{"id":"https://openalex.org/C13280743","wikidata":"https://www.wikidata.org/wiki/Q131089","display_name":"Geodesy","level":1,"score":0.0},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.0},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.0},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3534678.3539423","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3534678.3539423","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3534678.3539423","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":{"id":"doi:10.1145/3534678.3539423","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3534678.3539423","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3534678.3539423","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"},"sustainable_development_goals":[{"score":0.44999998807907104,"id":"https://metadata.un.org/sdg/16","display_name":"Peace, Justice and strong institutions"}],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4283326779.pdf","grobid_xml":"https://content.openalex.org/works/W4283326779.grobid-xml"},"referenced_works_count":12,"referenced_works":["https://openalex.org/W1993482030","https://openalex.org/W2070996757","https://openalex.org/W2094728533","https://openalex.org/W2130354913","https://openalex.org/W2626376796","https://openalex.org/W3003257820","https://openalex.org/W3010969086","https://openalex.org/W3104001151","https://openalex.org/W3121395088","https://openalex.org/W3138787737","https://openalex.org/W3159953606","https://openalex.org/W4236965008"],"related_works":["https://openalex.org/W4287763734","https://openalex.org/W3131062520","https://openalex.org/W3035116611","https://openalex.org/W3062237905","https://openalex.org/W3094605108","https://openalex.org/W3044354590","https://openalex.org/W2923818335","https://openalex.org/W4212923699","https://openalex.org/W2893186803","https://openalex.org/W4310879833"],"abstract_inverted_index":{"This":[0],"paper":[1],"describes":[2],"a":[3,68],"new":[4],"method":[5],"for":[6,60,85,93],"representing":[7],"embedding":[8,107],"tables":[9,52],"of":[10,38,65,71,105,148],"graph":[11,28,97],"neural":[12],"networks":[13],"(GNNs)":[14],"more":[15],"compactly":[16],"via":[17],"tensor-train":[18],"(TT)":[19],"decomposition.":[20],"We":[21],"consider":[22],"the":[23,27,36,72,103,146],"scenario":[24],"where":[25,50],"(a)":[26],"data":[29],"that":[30,150],"lack":[31],"node":[32,106,139,152],"features,":[33],"thereby":[34],"requiring":[35],"learning":[37],"embeddings":[39],"during":[40],"training;":[41],"and":[42,95,126],"(b)":[43],"we":[44],"wish":[45],"to":[46,55,78,112],"exploit":[47],"GPU":[48],"platforms,":[49],"smaller":[51],"are":[53],"needed":[54],"reduce":[56,102],"host-to-GPU":[57],"communication":[58],"even":[59,84,144],"large-memory":[61],"GPUs.":[62],"The":[63],"use":[64,151],"TT":[66],"enables":[67],"compact":[69],"parameterization":[70],"embedding,":[73],"rendering":[74],"it":[75],"small":[76],"enough":[77],"fit":[79],"entirely":[80],"on":[81,115,129,141],"modern":[82],"GPUs":[83],"massive":[86],"graphs.":[87],"When":[88],"combined":[89],"with":[90],"judicious":[91],"schemes":[92],"initialization":[94],"hierarchical":[96],"partitioning,":[98],"this":[99],"approach":[100],"can":[101,143],"size":[104],"vectors":[108],"by":[109],"1,659":[110],"times":[111,114],"81,362":[113],"large":[116],"publicly":[117],"available":[118],"benchmark":[119],"datasets,":[120],"achieving":[121],"comparable":[122],"or":[123],"better":[124],"accuracy":[125,147],"significant":[127],"speedups":[128],"multi-GPU":[130],"systems.":[131],"In":[132],"some":[133],"cases,":[134],"our":[135],"model":[136],"without":[137],"explicit":[138],"features":[140],"input":[142],"match":[145],"models":[149],"features.":[153]},"counts_by_year":[{"year":2026,"cited_by_count":3},{"year":2025,"cited_by_count":8},{"year":2024,"cited_by_count":4},{"year":2023,"cited_by_count":2}],"updated_date":"2026-08-08T01:25:22.217667","created_date":"2025-10-10T00:00:00"}
