{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,19]],"date-time":"2026-03-19T09:01:16Z","timestamp":1773910876542,"version":"3.50.1"},"reference-count":42,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"12","license":[{"start":{"date-parts":[[2023,12,1]],"date-time":"2023-12-01T00:00:00Z","timestamp":1701388800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2023,12,1]],"date-time":"2023-12-01T00:00:00Z","timestamp":1701388800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2023,12,1]],"date-time":"2023-12-01T00:00:00Z","timestamp":1701388800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"DOI":"10.13039\/501100012166","name":"National Key R&#x0026;D Program of China","doi-asserted-by":"publisher","award":["2021YFB0301200"],"award-info":[{"award-number":["2021YFB0301200"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62025208"],"award-info":[{"award-number":["62025208"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61932001"],"award-info":[{"award-number":["61932001"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61972409"],"award-info":[{"award-number":["61972409"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Comput."],"published-print":{"date-parts":[[2023,12]]},"DOI":"10.1109\/tc.2023.3305077","type":"journal-article","created":{"date-parts":[[2023,8,14]],"date-time":"2023-08-14T17:48:07Z","timestamp":1692035287000},"page":"3473-3488","source":"Crossref","is-referenced-by-count":5,"title":["Accelerating GNN Training by Adapting Large Graphs to Distributed Heterogeneous Architectures"],"prefix":"10.1109","volume":"72","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6727-1962","authenticated-orcid":false,"given":"Lizhi","family":"Zhang","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6378-7002","authenticated-orcid":false,"given":"Kai","family":"Lu","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3458-4732","authenticated-orcid":false,"given":"Zhiquan","family":"Lai","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7564-5239","authenticated-orcid":false,"given":"Yongquan","family":"Fu","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8595-1547","authenticated-orcid":false,"given":"Yu","family":"Tang","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9743-2034","authenticated-orcid":false,"given":"Dongsheng","family":"Li","sequence":"additional","affiliation":[]}],"member":"263","reference":[{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.14778\/3415478.3415539"},{"key":"ref35","article-title":"PyTorch-BigGraph: A large-scale graph embedding system","author":"lerer","year":"2019"},{"key":"ref12","article-title":"Fast graph representation learning with PyTorch geometric","author":"fey","year":"2019"},{"key":"ref34","article-title":"Nsight compute","year":"2019"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1145\/3020078.3021739"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1109\/IA351965.2020.00011"},{"key":"ref14","article-title":"Deep graph library: A graph-centric, highly-performant package for graph neural networks","author":"wang","year":"2019"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1145\/3292500.3340404"},{"key":"ref31","article-title":"Graph attention networks","author":"veli?kovi?","year":"2017"},{"key":"ref30","article-title":"Semi-supervised classification with graph convolutional networks","author":"kipf","year":"2016"},{"key":"ref11","first-page":"1025","article-title":"Inductive representation learning on large graphs","author":"hamilton","year":"2017","journal-title":"Proc 31st Int Conf Neural Inf Process Syst"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1016\/S1571-0661(04)81042-9"},{"key":"ref10","article-title":"FastGCN: Fast learning with graph convolutional networks via importance sampling","author":"chen","year":"2018"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-96983-1_48"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2020.2981333"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1016\/j.aiopen.2021.01.001"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1145\/3419111.3421281"},{"key":"ref39","first-page":"443","article-title":"NeuGraph: Parallel deep neural network computation on large graphs","author":"ma","year":"2019","journal-title":"Proc USENIX Annu Tech Conf (USENIX ATC)"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1145\/3292500.3330925"},{"key":"ref38","article-title":"Towards efficient large-scale graph neural network computing","author":"ma","year":"2018"},{"key":"ref19","article-title":"PyTorch-direct: Enabling GPU centric data access for very large graph neural network training with irregular accesses","author":"min","year":"2021"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1109\/PACT.2017.41"},{"key":"ref24","article-title":"Layer-dependent importance sampling for training deep and large graph convolutional networks","author":"zou","year":"2019"},{"key":"ref23","first-page":"515","article-title":"GNNAdvisor: An adaptive and efficient runtime system for GNN acceleration on GPUs","author":"wang","year":"2021","journal-title":"Proc 15th USENIX Symp Operating Syst Des Implementation (OSDI)"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1137\/S1064827595287997"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1145\/3437801.3441585"},{"key":"ref20","first-page":"61","article-title":"The graph neural network model","volume":"20","author":"scarselli","year":"2008"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1145\/2807591.2807655"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1109\/ISPA-BDCloud-SocialCom-SustainCom52081.2021.00048"},{"key":"ref22","article-title":"Open graph benchmark: Datasets for machine learning on graphs","author":"hu","year":"2020"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2020.2978386"},{"key":"ref28","article-title":"GraphSAINT: Graph sampling based inductive learning method","author":"zeng","year":"2019"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2007.1115"},{"key":"ref29","first-page":"8026","article-title":"PyTorch: An imperative style, high-performance deep learning library","volume":"32","author":"paszke","year":"0","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1145\/3219819.3219869"},{"key":"ref7","article-title":"Interaction networks for learning about objects, relations and physics","year":"2016"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1162\/qss_a_00021"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/MSP.2017.2693418"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2020\/679"},{"key":"ref6","article-title":"Knowledge transfer for out-of-knowledge-base entities: A graph neural network approach","year":"2017"},{"key":"ref5","article-title":"Representation learning on graphs: Methods and applications","author":"hamilton","year":"2017"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1109\/ICDCS.2019.00173"}],"container-title":["IEEE Transactions on Computers"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/12\/10311055\/10217071.pdf?arnumber=10217071","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,12,11]],"date-time":"2023-12-11T19:55:54Z","timestamp":1702324554000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10217071\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,12]]},"references-count":42,"journal-issue":{"issue":"12"},"URL":"https:\/\/doi.org\/10.1109\/tc.2023.3305077","relation":{},"ISSN":["0018-9340","1557-9956","2326-3814"],"issn-type":[{"value":"0018-9340","type":"print"},{"value":"1557-9956","type":"electronic"},{"value":"2326-3814","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,12]]}}}