19 citations · 68 across the 11 of their papers we have counts for
4 papers · 1 filter
DyFormer: A Scalable Dynamic Graph Transformer with Provable Benefits on Generalization Ability
Weilin Cong, Yanhong Wu, Yuandong Tian +4
Transformers have achieved great success in several domains, including Natural Language Processing and Computer Vision. However, its application to real-world graphs is less explor…
Learn Locally, Correct Globally: A Distributed Algorithm for Training Graph Neural Networks
Morteza Ramezani, Weilin Cong, Mehrdad Mahdavi +2
Despite the recent success of Graph Neural Networks (GNNs), training GNNs on large graphs remains challenging. The limited resource capacities of the existing servers, the dependen…
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…
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,…