{"id":"https://openalex.org/W4281689849","doi":"https://doi.org/10.1145/3534678.3539352","title":"Instant Graph Neural Networks for Dynamic Graphs","display_name":"Instant Graph Neural Networks for Dynamic Graphs","publication_year":2022,"publication_date":"2022-08-12","ids":{"openalex":"https://openalex.org/W4281689849","doi":"https://doi.org/10.1145/3534678.3539352"},"language":"en","primary_location":{"id":"doi:10.1145/3534678.3539352","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3534678.3539352","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":"article","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/A5100532319","display_name":"Yanping Zheng","orcid":null},"institutions":[{"id":"https://openalex.org/I78988378","display_name":"Renmin University of China","ror":"https://ror.org/041pakw92","country_code":"CN","type":"education","lineage":["https://openalex.org/I78988378"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Yanping Zheng","raw_affiliation_strings":["Renmin University of China, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Renmin University of China, Beijing, China","institution_ids":["https://openalex.org/I78988378"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101787393","display_name":"Hanzhi Wang","orcid":"https://orcid.org/0000-0002-0995-5546"},"institutions":[{"id":"https://openalex.org/I78988378","display_name":"Renmin University of China","ror":"https://ror.org/041pakw92","country_code":"CN","type":"education","lineage":["https://openalex.org/I78988378"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Hanzhi Wang","raw_affiliation_strings":["Renmin University of China, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Renmin University of China, Beijing, China","institution_ids":["https://openalex.org/I78988378"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5074858555","display_name":"Zhewei Wei","orcid":"https://orcid.org/0000-0003-3620-5086"},"institutions":[{"id":"https://openalex.org/I78988378","display_name":"Renmin University of China","ror":"https://ror.org/041pakw92","country_code":"CN","type":"education","lineage":["https://openalex.org/I78988378"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhewei Wei","raw_affiliation_strings":["Renmin University of China, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Renmin University of China, Beijing, China","institution_ids":["https://openalex.org/I78988378"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100661975","display_name":"Jiajun Liu","orcid":"https://orcid.org/0000-0002-0604-9580"},"institutions":[{"id":"https://openalex.org/I1292875679","display_name":"Commonwealth Scientific and Industrial Research Organisation","ror":"https://ror.org/03qn8fb07","country_code":"AU","type":"funder","lineage":["https://openalex.org/I1292875679","https://openalex.org/I2801453606","https://openalex.org/I4387156119"]},{"id":"https://openalex.org/I42894916","display_name":"Data61","ror":"https://ror.org/03q397159","country_code":"AU","type":"other","lineage":["https://openalex.org/I1292875679","https://openalex.org/I2801453606","https://openalex.org/I42894916","https://openalex.org/I4387156119"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Jiajun Liu","raw_affiliation_strings":["Data 61, CSIRO, Queensland, Australia"],"affiliations":[{"raw_affiliation_string":"Data 61, CSIRO, Queensland, Australia","institution_ids":["https://openalex.org/I42894916","https://openalex.org/I1292875679"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100736050","display_name":"Sibo Wang","orcid":"https://orcid.org/0000-0003-1892-6971"},"institutions":[{"id":"https://openalex.org/I177725633","display_name":"Chinese University of Hong Kong","ror":"https://ror.org/00t33hh48","country_code":"CN","type":"education","lineage":["https://openalex.org/I177725633"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Sibo Wang","raw_affiliation_strings":["The Chinese University of Hong Kong, Hong Kong, China"],"affiliations":[{"raw_affiliation_string":"The Chinese University of Hong Kong, Hong Kong, China","institution_ids":["https://openalex.org/I177725633"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5100532319"],"corresponding_institution_ids":["https://openalex.org/I78988378"],"apc_list":null,"apc_paid":null,"fwci":6.4049,"has_fulltext":false,"cited_by_count":20,"citation_normalized_percentile":{"value":0.9799509,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":97,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"2605","last_page":"2615"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11344","display_name":"Traffic Prediction and Management Techniques","score":0.9991999864578247,"subfield":{"id":"https://openalex.org/subfields/2215","display_name":"Building and Construction"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T11344","display_name":"Traffic Prediction and Management Techniques","score":0.9991999864578247,"subfield":{"id":"https://openalex.org/subfields/2215","display_name":"Building and Construction"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11273","display_name":"Advanced Graph Neural Networks","score":0.9983999729156494,"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/T11980","display_name":"Human Mobility and Location-Based Analysis","score":0.9965000152587891,"subfield":{"id":"https://openalex.org/subfields/3313","display_name":"Transportation"},"field":{"id":"https://openalex.org/fields/33","display_name":"Social Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7964990139007568},{"id":"https://openalex.org/keywords/snapshot","display_name":"Snapshot (computer storage)","score":0.7091021537780762},{"id":"https://openalex.org/keywords/scalability","display_name":"Scalability","score":0.6253938674926758},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.5117921233177185},{"id":"https://openalex.org/keywords/computation","display_name":"Computation","score":0.5087037682533264},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.45728984475135803},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.34380286931991577}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7964990139007568},{"id":"https://openalex.org/C55282118","wikidata":"https://www.wikidata.org/wiki/Q252683","display_name":"Snapshot (computer storage)","level":2,"score":0.7091021537780762},{"id":"https://openalex.org/C48044578","wikidata":"https://www.wikidata.org/wiki/Q727490","display_name":"Scalability","level":2,"score":0.6253938674926758},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.5117921233177185},{"id":"https://openalex.org/C45374587","wikidata":"https://www.wikidata.org/wiki/Q12525525","display_name":"Computation","level":2,"score":0.5087037682533264},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.45728984475135803},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.34380286931991577},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0},{"id":"https://openalex.org/C77088390","wikidata":"https://www.wikidata.org/wiki/Q8513","display_name":"Database","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3534678.3539352","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3534678.3539352","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":[],"awards":[{"id":"https://openalex.org/G410270154","display_name":null,"funder_award_id":"61972401, 61932001, 61832017, U1936205","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":41,"referenced_works":["https://openalex.org/W19684845","https://openalex.org/W2069153192","https://openalex.org/W2114244473","https://openalex.org/W2116405614","https://openalex.org/W2168878667","https://openalex.org/W2311742723","https://openalex.org/W2519887557","https://openalex.org/W2610034660","https://openalex.org/W2624431344","https://openalex.org/W2786915849","https://openalex.org/W2787927827","https://openalex.org/W2790814121","https://openalex.org/W2798918712","https://openalex.org/W2804057010","https://openalex.org/W2805177834","https://openalex.org/W2808908091","https://openalex.org/W2900763475","https://openalex.org/W2901504064","https://openalex.org/W2907192581","https://openalex.org/W2944833253","https://openalex.org/W2945827377","https://openalex.org/W2954691982","https://openalex.org/W2961295589","https://openalex.org/W2965683718","https://openalex.org/W2971220558","https://openalex.org/W2984488829","https://openalex.org/W2998116985","https://openalex.org/W2998313947","https://openalex.org/W3019863187","https://openalex.org/W3026076535","https://openalex.org/W3039500550","https://openalex.org/W3096200195","https://openalex.org/W3101543043","https://openalex.org/W3101553402","https://openalex.org/W3152560880","https://openalex.org/W3169353485","https://openalex.org/W3175110359","https://openalex.org/W6639055396","https://openalex.org/W6758918355","https://openalex.org/W6760001035","https://openalex.org/W6766892243"],"related_works":["https://openalex.org/W2542847180","https://openalex.org/W3034994054","https://openalex.org/W2805712290","https://openalex.org/W2909129499","https://openalex.org/W2761598930","https://openalex.org/W2392087771","https://openalex.org/W2356600124","https://openalex.org/W4245782888","https://openalex.org/W3181327668","https://openalex.org/W2374624607"],"abstract_inverted_index":{"Graph":[0,124],"Neural":[1,125],"Networks":[2],"(GNNs)":[3],"have":[4,14],"been":[5,15],"widely":[6],"used":[7],"for":[8,77,132],"modeling":[9,54],"graph-structured":[10],"data.":[11],"Recent":[12],"breakthroughs":[13],"made":[16],"in":[17,80,86,91,107],"improving":[18],"the":[19,55,78,81,87,94,100,104,116,133,147,161,198,208],"scalability":[20],"of":[21,29,38,58,137,179,182],"GNNs":[22,43,51],"to":[23,33,83,141,200],"work":[24,142],"on":[25,53,61,93,103,160,216],"graphs":[26,41,145],"with":[27,42,143,146,167],"millions":[28],"nodes.":[30],"However,":[31],"how":[32],"instantly":[34],"represent":[35],"continuous":[36],"changes":[37,79],"large-scale":[39],"dynamic":[40,50,138,144,168,171],"is":[44,73,110],"still":[45],"an":[46,128,192],"open":[47],"problem.":[48],"Existing":[49],"focus":[52],"periodic":[56],"evolution":[57],"graphs,":[59],"often":[60],"a":[62,74],"snapshot":[63,109],"basis.":[64],"Such":[65],"methods":[66],"suffer":[67],"from":[68],"two":[69],"drawbacks:":[70],"first,":[71],"there":[72],"substantial":[75],"delay":[76],"graph":[82,88,106,134],"be":[84],"reflected":[85],"representations,":[89],"resulting":[90],"losses":[92],"model's":[95],"accuracy;":[96],"second,":[97],"repeatedly":[98],"calculating":[99],"representation":[101,135,162],"matrix":[102,136],"entire":[105],"each":[108],"predominantly":[111],"time-consuming":[112],"and":[113,156,163,170,219],"severely":[114],"limits":[115],"scalability.":[117],"In":[118],"this":[119],"paper,":[120],"we":[121],"propose":[122],"Instant":[123],"Network":[126],"(InstantGNN),":[127],"incremental":[129],"computation":[130],"approach":[131],"graphs.":[139],"Set":[140],"edge-arrival":[148],"model,":[149],"our":[150,189,226],"method":[151,190],"avoids":[152],"time-consuming,":[153],"repetitive":[154],"computations":[155],"allows":[157],"instant":[158,164],"updates":[159,184],"predictions.":[165],"Graphs":[166],"structures":[169],"attributes":[172],"are":[173,185],"both":[174],"supported.":[175],"The":[176],"upper":[177],"bounds":[178],"time":[180],"complexity":[181],"those":[183],"also":[186],"provided.":[187],"Furthermore,":[188],"provides":[191],"adaptive":[193],"training":[194],"strategy,":[195],"which":[196],"guides":[197],"model":[199,227],"retrain":[201],"at":[202],"moments":[203],"when":[204],"it":[205],"can":[206],"make":[207],"greatest":[209],"performance":[210],"gains.":[211],"We":[212],"conduct":[213],"extensive":[214],"experiments":[215],"several":[217],"real-world":[218],"synthetic":[220],"datasets.":[221],"Empirical":[222],"results":[223],"demonstrate":[224],"that":[225],"achieves":[228],"state-of-the-art":[229],"accuracy":[230],"while":[231],"having":[232],"orders-of-magnitude":[233],"higher":[234],"efficiency":[235],"than":[236],"existing":[237],"methods.":[238]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":6},{"year":2024,"cited_by_count":5},{"year":2023,"cited_by_count":8}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
