{"id":"https://openalex.org/W4281401915","doi":"https://doi.org/10.1145/3534540.3534691","title":"DynaGraph","display_name":"DynaGraph","publication_year":2022,"publication_date":"2022-05-23","ids":{"openalex":"https://openalex.org/W4281401915","doi":"https://doi.org/10.1145/3534540.3534691"},"language":"en","primary_location":{"id":"doi:10.1145/3534540.3534691","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3534540.3534691","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3534540.3534691","source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 5th ACM SIGMOD Joint International Workshop on Graph Data Management Experiences &amp; Systems (GRADES) and Network Data Analytics (NDA)","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://dl.acm.org/doi/pdf/10.1145/3534540.3534691","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5111538674","display_name":"Mingyu Guan","orcid":"https://orcid.org/0000-0001-6294-7978"},"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":"Mingyu Guan","raw_affiliation_strings":["Georgia Institute of Technology"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Georgia Institute of Technology","institution_ids":["https://openalex.org/I130701444"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5090733623","display_name":"Anand Iyer","orcid":"https://orcid.org/0000-0002-5952-3346"},"institutions":[{"id":"https://openalex.org/I4210164937","display_name":"Microsoft Research (United Kingdom)","ror":"https://ror.org/05k87vq12","country_code":"GB","type":"company","lineage":["https://openalex.org/I1290206253","https://openalex.org/I4210164937"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Anand Padmanabha Iyer","raw_affiliation_strings":["Microsoft Research"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Microsoft Research","institution_ids":["https://openalex.org/I4210164937"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100743709","display_name":"Taesoo Kim","orcid":"https://orcid.org/0000-0002-7440-2067"},"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":"Taesoo Kim","raw_affiliation_strings":["Georgia Institute of Technology"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Georgia Institute of Technology","institution_ids":["https://openalex.org/I130701444"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":true,"cited_by_count":33,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"10"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11273","display_name":"Advanced Graph Neural Networks","score":0.9998999834060669,"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":0.9998999834060669,"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":0.9993000030517578,"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/T10036","display_name":"Advanced Neural Network Applications","score":0.989300012588501,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8943129777908325},{"id":"https://openalex.org/keywords/scalability","display_name":"Scalability","score":0.7372632622718811},{"id":"https://openalex.org/keywords/speedup","display_name":"Speedup","score":0.7313421368598938},{"id":"https://openalex.org/keywords/overhead","display_name":"Overhead (engineering)","score":0.5977014303207397},{"id":"https://openalex.org/keywords/computation","display_name":"Computation","score":0.5515629053115845},{"id":"https://openalex.org/keywords/cache","display_name":"Cache","score":0.4908052384853363},{"id":"https://openalex.org/keywords/distributed-computing","display_name":"Distributed computing","score":0.473939448595047},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.4660877585411072},{"id":"https://openalex.org/keywords/encoding","display_name":"Encoding (memory)","score":0.4346276819705963},{"id":"https://openalex.org/keywords/parallel-computing","display_name":"Parallel computing","score":0.3979836702346802},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.37424784898757935},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.2564390301704407},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.15420451760292053},{"id":"https://openalex.org/keywords/database","display_name":"Database","score":0.11600896716117859},{"id":"https://openalex.org/keywords/programming-language","display_name":"Programming language","score":0.11456727981567383}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8943129777908325},{"id":"https://openalex.org/C48044578","wikidata":"https://www.wikidata.org/wiki/Q727490","display_name":"Scalability","level":2,"score":0.7372632622718811},{"id":"https://openalex.org/C68339613","wikidata":"https://www.wikidata.org/wiki/Q1549489","display_name":"Speedup","level":2,"score":0.7313421368598938},{"id":"https://openalex.org/C2779960059","wikidata":"https://www.wikidata.org/wiki/Q7113681","display_name":"Overhead (engineering)","level":2,"score":0.5977014303207397},{"id":"https://openalex.org/C45374587","wikidata":"https://www.wikidata.org/wiki/Q12525525","display_name":"Computation","level":2,"score":0.5515629053115845},{"id":"https://openalex.org/C115537543","wikidata":"https://www.wikidata.org/wiki/Q165596","display_name":"Cache","level":2,"score":0.4908052384853363},{"id":"https://openalex.org/C120314980","wikidata":"https://www.wikidata.org/wiki/Q180634","display_name":"Distributed computing","level":1,"score":0.473939448595047},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.4660877585411072},{"id":"https://openalex.org/C125411270","wikidata":"https://www.wikidata.org/wiki/Q18653","display_name":"Encoding (memory)","level":2,"score":0.4346276819705963},{"id":"https://openalex.org/C173608175","wikidata":"https://www.wikidata.org/wiki/Q232661","display_name":"Parallel computing","level":1,"score":0.3979836702346802},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.37424784898757935},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.2564390301704407},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.15420451760292053},{"id":"https://openalex.org/C77088390","wikidata":"https://www.wikidata.org/wiki/Q8513","display_name":"Database","level":1,"score":0.11600896716117859},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.11456727981567383}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3534540.3534691","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3534540.3534691","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3534540.3534691","source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 5th ACM SIGMOD Joint International Workshop on Graph Data Management Experiences &amp; Systems (GRADES) and Network Data Analytics (NDA)","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.1145/3534540.3534691","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3534540.3534691","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3534540.3534691","source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 5th ACM SIGMOD Joint International Workshop on Graph Data Management Experiences &amp; Systems (GRADES) and Network Data Analytics (NDA)","raw_type":"proceedings-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":25,"referenced_works":["https://openalex.org/W1965830721","https://openalex.org/W1986603225","https://openalex.org/W2064675550","https://openalex.org/W2080731889","https://openalex.org/W2296407087","https://openalex.org/W2565684601","https://openalex.org/W2610034660","https://openalex.org/W2801991413","https://openalex.org/W2901504064","https://openalex.org/W2914209329","https://openalex.org/W2928052000","https://openalex.org/W2945594170","https://openalex.org/W2969996838","https://openalex.org/W3007309629","https://openalex.org/W3040478789","https://openalex.org/W3096566397","https://openalex.org/W3104307750","https://openalex.org/W3106460253","https://openalex.org/W3157805807","https://openalex.org/W3171903345","https://openalex.org/W3204508881","https://openalex.org/W3208881055","https://openalex.org/W3210299018","https://openalex.org/W3210361503","https://openalex.org/W4293651439"],"related_works":["https://openalex.org/W2058965144","https://openalex.org/W2164382479","https://openalex.org/W98480971","https://openalex.org/W2150291671","https://openalex.org/W2027972911","https://openalex.org/W2146343568","https://openalex.org/W2499279132","https://openalex.org/W1974690493","https://openalex.org/W1966837078","https://openalex.org/W2081416538"],"abstract_inverted_index":{"In":[0],"this":[1],"paper,":[2],"we":[3],"present":[4],"DynaGraph,":[5],"a":[6,68,87,97],"system":[7],"that":[8,20,40,76],"supports":[9],"dynamic":[10,24,62,72,79,90],"Graph":[11],"Neural":[12],"Networks":[13],"(GNNs)":[14],"efficiently.":[15],"Based":[16],"on":[17,86],"the":[18,58],"observation":[19],"existing":[21,105],"proposals":[22],"for":[23,29],"GNN":[25,63,80,91],"architectures":[26,92],"combine":[27],"techniques":[28,39],"structural":[30],"and":[31,52,93],"temporal":[32],"information":[33],"encoding":[34],"independently,":[35],"DynaGraph":[36,85],"proposes":[37,67],"novel":[38],"enable":[41],"cross":[42],"optimizations":[43],"across":[44],"these":[45],"tasks.":[46],"It":[47,65],"uses":[48],"cached":[49],"message":[50],"passing":[51],"timestep":[53],"fusion":[54],"to":[55,101,104],"significantly":[56],"reduce":[57],"overhead":[59],"associated":[60],"with":[61],"processing.":[64],"further":[66],"simple":[69],"distributed":[70],"data-parallel":[71],"graph":[73],"processing":[74],"strategy":[75],"enables":[77],"scalable":[78],"computation.":[81],"Our":[82],"evaluation":[83],"of":[84,89,99],"variety":[88],"use":[94],"cases":[95],"shows":[96],"speedup":[98],"up":[100],"2.7X":[102],"compared":[103],"state-of-the-art":[106],"frameworks.":[107]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":14},{"year":2024,"cited_by_count":7},{"year":2023,"cited_by_count":10},{"year":2022,"cited_by_count":1}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2022-05-25T00:00:00"}
