87 citations · 87 across the 2 of their papers we have counts for
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cs.LG2023
A Survey on Graph Neural Network Acceleration: Algorithms, Systems, and Customized Hardware
Shichang Zhang, Atefeh Sohrabizadeh, Cheng Wan +7
Graph neural networks (GNNs) are emerging for machine learning research on graph-structured data. GNNs achieve state-of-the-art performance on many tasks, but they face scalability…
cs.LG2022
Dissimilar Nodes Improve Graph Active Learning
Zhicheng Ren, Yifu Yuan, Yuxin Wu +3
Training labels for graph embedding algorithms could be costly to obtain in many practical scenarios. Active learning (AL) algorithms are very helpful to obtain the most useful lab…
cs.LG2019★ 87 cited
Layer-Dependent Importance Sampling for Training Deep and Large Graph Convolutional Networks
Difan Zou, Ziniu Hu, Yewen Wang +3
Graph convolutional networks (GCNs) have recently received wide attentions, due to their successful applications in different graph tasks and different domains. Training GCNs for a…