T. Meyer‐Brandis

Universality and Approximation Rates of Graph Neural Networks with Random Features

We investigate message-passing graph neural networks with random node features. Random node features are known to enhance the expressiveness of graph neural networks (GNNs) both theoretically and empirically. Here, we establish a novel universality result focusing on permutation-equivariant neural networks (PENNs), a c...