17 citations · 17 across the 1 of their papers we have counts for
2 papers
cs.LG2021★ 17 cited
On Provable Benefits of Depth in Training Graph Convolutional Networks
Weilin Cong, Morteza Ramezani, Mehrdad Mahdavi
Graph Convolutional Networks (GCNs) are known to suffer from performance degradation as the number of layers increases, which is usually attributed to over-smoothing. Despite the a…
cs.LG2021
On the Importance of Sampling in Training GCNs: Tighter Analysis and Variance Reduction
Weilin Cong, Morteza Ramezani, Mehrdad Mahdavi
Graph Convolutional Networks (GCNs) have achieved impressive empirical advancement across a wide variety of semi-supervised node classification tasks. Despite their great success,…