72 citations · 107 across the 7 of their papers we have counts for
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cs.LG2023★ 2 cited
Communication-Efficient Graph Neural Networks with Probabilistic Neighborhood Expansion Analysis and Caching
Tim Kaler, Alexandros-Stavros Iliopoulos, Philip Murzynowski +3
Training and inference with graph neural networks (GNNs) on massive graphs has been actively studied since the inception of GNNs, owing to the widespread use and success of GNNs in…
cs.LG2021★ 21 cited
Accelerating Training and Inference of Graph Neural Networks with Fast Sampling and Pipelining
Tim Kaler, Nickolas Stathas, Anne Ouyang +4
Improving the training and inference performance of graph neural networks (GNNs) is faced with a challenge uncommon in general neural networks: creating mini-batches requires a lot…