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Graph neural networks (GNN) have shown great success in learning from graph-structured data.
G. Karypis and V. Kumar, “A fast and high quality multilevel scheme for partitioning irregular graphs,” SIAM Journal on Scientific Computing , vol. 20, no. 1, pp. 359–392, 1998
1998
Earlier work this paper cites.
G. Karypis and V. Kumar, “Multilevel algorithms for multi-constraint graph partitioning,” in Proceedings of the 1998 ACM/IEEE Conference on Supercomputing , USA, 1998, p. 1–13
1998
Earlier work this paper cites.
G. Malewicz, M. H. Austern, A. J. Bik, J. C. Dehnert, I. Horn, N. Leiser, and G. Czajkowski, “Pregel: A system for large-scale graph processing,” in Proceedings of the 2010 ACM SIGMOD International Conference on Management of Data , ser. SIGMOD ’10, New York, NY, USA, 2010, p. 135–146
2010
Earlier work this paper cites.
2011
Earlier work this paper cites.
J. E. Gonzalez, Y. Low, H. Gu, D. Bickson, and C. Guestrin, “Powergraph: Distributed graph-parallel computation on natural graphs,” in 10th USENIX Symposium on Operating Systems Design and Implementation (OSDI 12) , Nov. 2012
2012
Earlier work this paper cites.
J. Shun and G. E. Blelloch, “Ligra: A lightweight graph processing framework for shared memory,” SIGPLAN Not. , vol. 48, no. 8, p. 135–146, Feb. 2013
2013
Earlier work this paper cites.
Q. Ho, J. Cipar, H. Cui, S. Lee, J. K. Kim, P. B. Gibbons, G. A. Gibson, G. Ganger, and E. P. Xing, “More effective distributed ml via a stale synchronous parallel parameter server,” in Advances in neural information processing systems , 2013, pp. 1223–1231
2013
Earlier work this paper cites.
M. Li, D. G. Andersen, J. W. Park, A. J. Smola, A. Ahmed, V. Josifovski, J. Long, E. J. Shekita, and B.-Y. Su, “Scaling distributed machine learning with the parameter server,” in Proceedings of the 11th USENIX Conference on Operating Systems Design and Implementation , ser. OSDI’14. USA: USENIX Association, 2014, p. 583–598
2014
Earlier work this paper cites.
T. Chilimbi, Y. Suzue, J. Apacible, and K. Kalyanaraman, “Project adam: Building an efficient and scalable deep learning training system,” in 11th USENIX Symposium on Operating Systems Design and Implementation (OSDI 14) , Broomfield, CO, Oct. 2014, pp. 571–582
2014
Earlier work this paper cites.
F. Seide, H. Fu, J. Droppo, G. Li, and D. Yu, “1-bit stochastic gradient descent and its application to data-parallel distributed training of speech dnns,” in Fifteenth Annual Conference of the International Speech Communication Association , 2014
2014
Earlier work this paper cites.
X. Zhu, W. Chen, W. Zheng, and X. Ma, “Gemini: A computation-centric distributed graph processing system,” in 12th USENIX Symposium on Operating Systems Design and Implementation (OSDI 16) , Nov. 2016
2016
Earlier work this paper cites.
W. L. Hamilton, R. Ying, and J. Leskovec, “Inductive representation learning on large graphs,” in Proceedings of the 31st International Conference on Neural Information Processing Systems , ser. NIPS’17, 2017, p. 1025–1035
2017
Earlier work this paper cites.
J. Gilmer, S. S. Schoenholz, P. F. Riley, O. Vinyals, and G. E. Dahl, “Neural message passing for quantum chemistry,” in Proceedings of the 34th International Conference on Machine Learning - Volume 70 , 2017
2017
Earlier work this paper cites.
J. Chen, J. Zhu, and L. Song, “Stochastic training of graph convolutional networks with variance reduction,” ser. Proceedings of Machine Learning Research, J. Dy and A. Krause, Eds., vol. 80. Stockholmsmässan, Stockholm Sweden: PMLR, 10–15 Jul 2018, pp. 942–950
2018
Cited alongside, same era.
J. Chen, T. Ma, and C. Xiao, “FastGCN: Fast learning with graph convolutional networks via importance sampling,” in International Conference on Learning Representations , 2018
2018
Cited alongside, same era.
2018
Cited alongside, same era.
2018
Cited alongside, same era.
Y. Peng, Y. Zhu, Y. Chen, Y. Bao, B. Yi, C. Lan, C. Wu, and C. Guo, “A generic communication scheduler for distributed dnn training acceleration,” in Proceedings of the 27th ACM Symposium on Operating Systems Principles , ser. SOSP ’19, New York, NY, USA, 2019, p. 16–29
2019
Later among the works it cites.
2019
Later among the works it cites.
W.-L. Chiang, X. Liu, S. Si, Y. Li, S. Bengio, and C.-J. Hsieh, “Cluster-gcn,” Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining , Jul 2019
2019
Later among the works it cites.
2019
Later among the works it cites.
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2018
Cited alongside, same era.
2018
Cited alongside, same era.
S. Eyerman, W. Heirman, K. D. Bois, J. B. Fryman, and I. Hur, “Many-core graph workload analysis,” in Proceedings of the International Conference for High Performance Computing, Networking, Storage, and Analysis , ser. SC ’18. IEEE Press, 2018
2018
Cited alongside, same era.
2018
Cited alongside, same era.
2019
Cited alongside, same era.
2019
Cited alongside, same era.
L. Ma, Z. Yang, Y. Miao, J. Xue, M. Wu, L. Zhou, and Y. Dai, “Neugraph: Parallel deep neural network computation on large graphs,” in 2019 USENIX Annual Technical Conference (USENIX ATC 19) , Renton, WA, Jul. 2019, pp. 443–458
2019
Cited alongside, same era.
2019
Cited alongside, same era.
Q. Luo, J. Lin, Y. Zhuo, and X. Qian, “Hop: Heterogeneity-aware decentralized training,” in Proceedings of the Twenty-Fourth International Conference on Architectural Support for Programming Languages and Operating Systems , 2019, pp. 893–907
2019
Later among the works it cites.
Z. Jia, S. Lin, M. Gao, M. Zaharia, and A. Aiken, “Improving the accuracy, scalability, and performance of graph neural networks with roc,” in Proceedings of Machine Learning and Systems , I. Dhillon, D. Papailiopoulos, and V. Sze, Eds., 2020, vol. 2, pp. 187–198
2020
Closest in time.
2020
Closest in time.
2020
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“Euler github,” https://github.com/alibaba/euler, 2020
2020
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2020
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2020
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