{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,28]],"date-time":"2026-02-28T01:13:04Z","timestamp":1772241184437,"version":"3.50.1"},"reference-count":52,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100003725","name":"National Research Foundation of Korea","doi-asserted-by":"publisher","award":["NRF-2021R1C1C1012172"],"award-info":[{"award-number":["NRF-2021R1C1C1012172"]}],"id":[{"id":"10.13039\/501100003725","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Access"],"published-print":{"date-parts":[[2022]]},"DOI":"10.1109\/access.2022.3217222","type":"journal-article","created":{"date-parts":[[2022,11,3]],"date-time":"2022-11-03T23:22:33Z","timestamp":1667517753000},"page":"113046-113060","source":"Crossref","is-referenced-by-count":5,"title":["Analyzing GCN Aggregation on GPU"],"prefix":"10.1109","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9977-5476","authenticated-orcid":false,"given":"Inje","family":"Kim","sequence":"first","affiliation":[{"name":"Department of Computer Science and Engineering, Korea University, Seoul, South Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jonghyun","family":"Jeong","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, Korea University, Seoul, South Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yunho","family":"Oh","sequence":"additional","affiliation":[{"name":"School of Electrical Engineering, Korea University, Seoul, South Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9332-0251","authenticated-orcid":false,"given":"Myung Kuk","family":"Yoon","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, Ewha Womans University, Seoul, South Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1706-6850","authenticated-orcid":false,"given":"Gunjae","family":"Koo","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, Korea University, Seoul, South Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"key":"ref39","first-page":"1","article-title":"Geom-GCN: Geometric graph convolutional networks","author":"pei","year":"2020","journal-title":"Proc Int Conf Learn Represent"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1145\/3097983.3098061"},{"key":"ref33","article-title":"Inductive representation learning on large graphs","author":"hamilton","year":"2017","journal-title":"arXiv 1706 02216"},{"key":"ref32","article-title":"Deep Gaussian embedding of graphs: Unsupervised inductive learning via ranking","author":"bojchevski","year":"2018","journal-title":"Proc Int Conf Learn Represent"},{"key":"ref31","article-title":"GraphSAINT: Graph sampling based inductive learning method","author":"zeng","year":"2019","journal-title":"arXiv 1907 04931"},{"key":"ref30","first-page":"40","article-title":"Revisiting semi-supervised learning with graph embeddings","volume":"48","author":"yang","year":"2016","journal-title":"Proc 33rd Int Conf Mach Learn"},{"key":"ref37","article-title":"Large scale learning on non-homophilous graphs: New benchmarks and strong simple methods","author":"lim","year":"2021","journal-title":"arXiv 2110 14446"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.14778\/3421424.3421430"},{"key":"ref35","article-title":"Pitfalls of graph neural network evaluation","author":"shchur","year":"2018","journal-title":"arXiv 1811 05868"},{"key":"ref34","first-page":"1","article-title":"Pitfalls of graph neural network evaluation","author":"shchur","year":"2018","journal-title":"Proc Relational Represent Learn Workshop (NeurIPS)"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1109\/IISWC.2012.6402918"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-540-77220-0_21"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1109\/IISWC.2014.6983052"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1145\/3357384.3357924"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2020.3033673"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1109\/PACT.2015.15"},{"key":"ref22","year":"2022","journal-title":"Nvidia tesla v100 gpu architecture the world’s most advanced data center gpu"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1109\/IPDPS.2014.49"},{"key":"ref24","year":"2022","journal-title":"NVIDIA Turing GPU Architecture Graphics reinvented"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1145\/3079856.3080239"},{"key":"ref26","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":"ref25","doi-asserted-by":"publisher","DOI":"10.1145\/1498765.1498785"},{"key":"ref50","doi-asserted-by":"publisher","DOI":"10.1109\/IPDPS47924.2020.00100"},{"key":"ref51","doi-asserted-by":"publisher","DOI":"10.1109\/MICRO50266.2020.00079"},{"key":"ref52","doi-asserted-by":"publisher","DOI":"10.1109\/HPCA51647.2021.00070"},{"key":"ref10","article-title":"Variational graph auto-encoders","author":"kipf","year":"2016","journal-title":"arXiv 1611 07308"},{"key":"ref11","article-title":"A systematic survey on deep generative models for graph generation","author":"guo","year":"2020","journal-title":"arXiv 2007 06686"},{"key":"ref40","article-title":"Multi-scale attributed node embedding","author":"rozemberczki","year":"2019","journal-title":"arXiv 1909 13021"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1109\/HPCA47549.2020.00012"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1109\/IISWC.2014.6983053"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1109\/IISWC.2013.6704684"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1109\/IISWC.2015.23"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1145\/3128571"},{"key":"ref17","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3450440","article-title":"GraphPEG: Accelerating graph processing on GPUs","volume":"18","author":"l\u00fc","year":"2021","journal-title":"ACM Trans Archit Code Optim"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1109\/TPDS.2013.111"},{"key":"ref19","first-page":"195","article-title":"Garaph: Efficient GPU-accelerated graph processing on a single machine with balanced replication","author":"ma","year":"2017","journal-title":"Proc USENIX Annu Tech Conf (USENIX ATC)"},{"key":"ref4","article-title":"Deeply learning molecular structure-property relationships using attention- and gate-augmented graph convolutional network","author":"ryu","year":"2018","journal-title":"arXiv 1805 10988"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/ICDE48307.2020.00019"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1145\/3065386"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1109\/BIBM47256.2019.8983191"},{"key":"ref8","article-title":"FastGCN: Fast learning with graph convolutional networks via importance sampling","author":"chen","year":"2018","journal-title":"arXiv 1801 10247"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.21437\/Interspeech.2010-343"},{"key":"ref49","doi-asserted-by":"publisher","DOI":"10.1109\/SC41405.2020.00076"},{"key":"ref9","article-title":"Graph attention networks","author":"veli?kovi?","year":"2017","journal-title":"arXiv 1710 10903"},{"key":"ref46","doi-asserted-by":"publisher","DOI":"10.1145\/3307650.3322222"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.1109\/HPCA.2015.7056024"},{"key":"ref48","doi-asserted-by":"publisher","DOI":"10.1109\/ISPASS51385.2021.00013"},{"key":"ref47","doi-asserted-by":"publisher","DOI":"10.1109\/LCA.2020.2970395"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1093\/biomet\/66.3.605"},{"key":"ref41","first-page":"22118","article-title":"Open graph benchmark: Datasets for machine learning on graphs","volume":"33","author":"hu","year":"2020","journal-title":"Proc 34th Conf Neural Inf Process Syst"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.1109\/MICRO.2012.16"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.1109\/ISCA45697.2020.00047"}],"container-title":["IEEE Access"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/6287639\/9668973\/09930519.pdf?arnumber=9930519","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,11,28]],"date-time":"2022-11-28T20:17:41Z","timestamp":1669666661000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9930519\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"references-count":52,"URL":"https:\/\/doi.org\/10.1109\/access.2022.3217222","relation":{},"ISSN":["2169-3536"],"issn-type":[{"value":"2169-3536","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022]]}}}