16 citations · 17 across the 3 of their papers we have counts for
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cs.LG2022★ 16 cited
FedGCN: Convergence-Communication Tradeoffs in Federated Training of Graph Convolutional Networks
Yuhang Yao, Weizhao Jin, Srivatsan Ravi +1
Methods for training models on graphs distributed across multiple clients have recently grown in popularity, due to the size of these graphs as well as regulations on keeping data…
cs.LG2021★ 1 cited
GCN-SE: Attention as Explainability for Node Classification in Dynamic Graphs
Yucai Fan, Yuhang Yao, Carlee Joe-Wong
Graph Convolutional Networks (GCNs) are a popular method from graph representation learning that have proved effective for tasks like node classification tasks. Although typical GC…