arXiv stat.ML
· Papers
Universality and Approximation Rates of Graph Neural Networks with Random Features
arXiv:2607.26699v1 Announce Type: cross Abstract: 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 pe