17 citations · 21 across the 2 of their papers we have counts for
2 papers
cs.LG2022★ 17 cited
BNS-GCN: Efficient Full-Graph Training of Graph Convolutional Networks with Partition-Parallelism and Random Boundary Node Sampling
Cheng Wan, Youjie Li, Ang Li +2
Graph Convolutional Networks (GCNs) have emerged as the state-of-the-art method for graph-based learning tasks. However, training GCNs at scale is still challenging, hindering both…
cs.LG2022★ 4 cited
PipeGCN: Efficient Full-Graph Training of Graph Convolutional Networks with Pipelined Feature Communication
Cheng Wan, Youjie Li, Cameron R. Wolfe +3
Graph Convolutional Networks (GCNs) is the state-of-the-art method for learning graph-structured data, and training large-scale GCNs requires distributed training across multiple a…