16 citations · 28 across the 2 of their papers we have counts for
8 papers
Optimization of Graph Neural Networks: Implicit Acceleration by Skip Connections and More Depth
Keyulu Xu, Mozhi Zhang, Stefanie Jegelka +1
Graph Neural Networks (GNNs) have been studied through the lens of expressive power and generalization. However, their optimization properties are less well understood. We take the…
How Neural Networks Extrapolate: From Feedforward to Graph Neural Networks
Keyulu Xu, Mozhi Zhang, Jingling Li +3
We study how neural networks trained by gradient descent extrapolate, i.e., what they learn outside the support of the training distribution. Previous works report mixed empirical…
Are Girls Neko or Shōjo? Cross-Lingual Alignment of Non-Isomorphic Embeddings with Iterative Normalization
Mozhi Zhang, Keyulu Xu, Ken-ichi Kawarabayashi +2
Cross-lingual word embeddings (CLWE) underlie many multilingual natural language processing systems, often through orthogonal transformations of pre-trained monolingual embeddings.…
What Can Neural Networks Reason About?
Keyulu Xu, Jingling Li, Mozhi Zhang +3
Neural networks have succeeded in many reasoning tasks. Empirically, these tasks require specialized network structures, e.g., Graph Neural Networks (GNNs) perform well on many suc…
Graph Neural Tangent Kernel: Fusing Graph Neural Networks with Graph Kernels
Simon S. Du, Kangcheng Hou, Barnabás Póczos +3
While graph kernels (GKs) are easy to train and enjoy provable theoretical guarantees, their practical performances are limited by their expressive power, as the kernel function of…
How Powerful are Graph Neural Networks?
Keyulu Xu, Weihua Hu, Jure Leskovec +1
Graph Neural Networks (GNNs) are an effective framework for representation learning of graphs. GNNs follow a neighborhood aggregation scheme, where the representation vector of a n…