  Graph neural networks (GNNs) have been successfully applied to learning
representation on graphs in many relational tasks. Recently, researchers study
neural architecture search (NAS) to reduce the dependence of human expertise
and explore better GNN architectures, but they over-emphasize entity features
and ignore latent relation information concealed in the edges. To solve this
problem, we incorporate edge features into graph search space and propose
Edge-featured Graph Neural Architecture Search to find the optimal GNN
architecture. Specifically, we design rich entity and edge updating operations
to learn high-order representations, which convey more generic message passing
mechanisms. Moreover, the architecture topology in our search space allows to
explore complex feature dependence of both entities and edges, which can be
efficiently optimized by differentiable search strategy. Experiments at three
graph tasks on six datasets show EGNAS can search better GNNs with higher
performance than current state-of-the-art human-designed and searched-based
GNNs.
