
Graph Adversarial Networks: Protecting Information against Adversarial Attacks
We study the problem of protecting information when learning with graph ...
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How Neural Networks Extrapolate: From Feedforward to Graph Neural Networks
We study how neural networks trained by gradient descent extrapolate, i....
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GraphNorm: A Principled Approach to Accelerating Graph Neural Network Training
Normalization plays an important role in the optimization of deep neural...
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Are Girls Neko or Shōjo? CrossLingual Alignment of NonIsomorphic Embeddings with Iterative Normalization
Crosslingual word embeddings (CLWE) underlie many multilingual natural ...
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What Can Neural Networks Reason About?
Neural networks have successfully been applied to solving reasoning task...
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Graph Neural Tangent Kernel: Fusing Graph Neural Networks with Graph Kernels
While graph kernels (GKs) are easy to train and enjoy provable theoretic...
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How Powerful are Graph Neural Networks?
Graph Neural Networks (GNNs) for representation learning of graphs broad...
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Representation Learning on Graphs with Jumping Knowledge Networks
Recent deep learning approaches for representation learning on graphs fo...
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Keyulu Xu
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