Before revision: This paperpresents a novel neural architecture search (NAS)framework for graph neural networks (GNNs) . We design an expressive search space that focuses on a common and critical component of GNNs -- propagation model.
After revision: This paperpresents a novel neural architecture search (NAS)framework for graph neural networks (GNNs) propagate messages along different types of edges. We formalize the problem within the framework of neural architecture search (NAS) and then perform the search in a differentiable manner. We design an expressive search space that focuses on a common and critical component of GNNs -- propagation model.