  Recent years have witnessed the popularity of Graph Neural Networks (GNN) in
various scenarios. To obtain optimal data-specific GNN architectures,
researchers turn to neural architecture search (NAS) methods, which have made
impressive progress in discovering effective architectures in convolutional
neural networks. Two preliminary works, GraphNAS and Auto-GNN, have made first
attempt to apply NAS methods to GNN. Despite the promising results, there are
several drawbacks in expressive capability and search efficiency of GraphNAS
and Auto-GNN due to the designed search space. To overcome these drawbacks, we
propose the SNAG framework (Simplified Neural Architecture search for Graph
neural networks), consisting of a novel search space and a reinforcement
learning based search algorithm. Extensive experiments on real-world datasets
demonstrate the effectiveness of the SNAG framework compared to human-designed
GNNs and NAS methods, including GraphNAS and Auto-GNN.
