Minimal Variance Sampling with Provable Guarantees for Fast Training of Graph Neural Networks

24 June 2020

Papers citing "Minimal Variance Sampling with Provable Guarantees for Fast Training of Graph Neural Networks"

9 / 9 papers shown

Title
LazyGNN: Large-Scale Graph Neural Networks via Lazy Propagation Rui Xue Haoyu Han MohamadAli Torkamani Jian Pei Xiaorui Liu GNN 31 19 0 03 Feb 2023
RSC: Accelerating Graph Neural Networks Training via Randomized Sparse Computations Zirui Liu Sheng-Wei Chen Kaixiong Zhou Daochen Zha Xiao Huang Xia Hu 32 14 0 19 Oct 2022
A Comprehensive Study on Large-Scale Graph Training: Benchmarking and Rethinking Keyu Duan Zirui Liu Peihao Wang Wenqing Zheng Kaixiong Zhou Tianlong Chen Xia Hu Zhangyang Wang GNN 41 57 0 14 Oct 2022
BNS-GCN: Efficient Full-Graph Training of Graph Convolutional Networks with Partition-Parallelism and Random Boundary Node Sampling Cheng Wan Youjie Li Ang Li Namjae Kim Yingyan Lin GNN 31 75 0 21 Mar 2022
Decoupling the Depth and Scope of Graph Neural Networks Hanqing Zeng Muhan Zhang Yinglong Xia Ajitesh Srivastava Andrey Malevich Rajgopal Kannan Viktor Prasanna Long Jin Ren Chen GNN 33 142 0 19 Jan 2022
On Provable Benefits of Depth in Training Graph Convolutional Networks Weilin Cong M. Ramezani M. Mahdavi 27 73 0 28 Oct 2021
IGLU: Efficient GCN Training via Lazy Updates S. Narayanan Aditya Sinha Prateek Jain Purushottam Kar Sundararajan Sellamanickam BDL 52 10 0 28 Sep 2021
Scalable Graph Neural Network Training: The Case for Sampling Marco Serafini Hui Guan GNN 41 23 0 05 May 2021
Sampling methods for efficient training of graph convolutional networks: A survey Xin Liu Yurui Lai Lei Deng Guoqi Li Xiaochun Ye Xiaochun Ye GNN 29 100 0 10 Mar 2021