MariusGNN: Resource-Efficient Out-of-Core Training of Graph Neural Networks-Reference-Cited by-同舟云学术

MariusGNN: Resource-Efficient Out-of-Core Training of Graph Neural Networks

Published:2023-05-08 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the Eighteenth European Conference on Computer Systems
language:
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Author:

Waleffe Roger¹^ORCID,Mohoney Jason²^ORCID,Rekatsinas Theodoros³^ORCID,Venkataraman Shivaram¹^ORCID

Affiliation:

1. University of Wisconsin-Madison, Madison, Wisconsin, United States of America

2. University of Wisconsin-Madison, Madison, Wisconsin, USA

3. ETH Zurich, Zurich, Switzerland

Funder

NSF (National Science Foundation)

Defense Advanced Research Projects Agency

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3552326.3567501

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3. Ines Chami Sami Abu-El-Haija Bryan Perozzi Christopher Ré and Kevin Murphy. 2021. Machine Learning on Graphs: A Model and Comprehensive Taxonomy. arXiv:2005.03675 [cs.LG] Ines Chami Sami Abu-El-Haija Bryan Perozzi Christopher Ré and Kevin Murphy. 2021. Machine Learning on Graphs: A Model and Comprehensive Taxonomy. arXiv:2005.03675 [cs.LG]

4. Jie Chen , Tengfei Ma , and Cao Xiao . 2018. Fastgcn: fast learning with graph convolutional networks via importance sampling. arXiv preprint arXiv:1801.10247 ( 2018 ). Jie Chen, Tengfei Ma, and Cao Xiao. 2018. Fastgcn: fast learning with graph convolutional networks via importance sampling. arXiv preprint arXiv:1801.10247 (2018).

5. Cluster-GCN

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