  Graph Neural Networks (GNNs) have been popularly used for analyzing
non-Euclidean data such as social network data and biological data. Despite
their success, the design of graph neural networks requires a lot of manual
work and domain knowledge. In this paper, we propose a Graph Neural
Architecture Search method (GraphNAS for short) that enables automatic search
of the best graph neural architecture based on reinforcement learning.
Specifically, GraphNAS first uses a recurrent network to generate
variable-length strings that describe the architectures of graph neural
networks, and then trains the recurrent network with reinforcement learning to
maximize the expected accuracy of the generated architectures on a validation
data set. Extensive experimental results on node classification tasks in both
transductive and inductive learning settings demonstrate that GraphNAS can
achieve consistently better performance on the Cora, Citeseer, Pubmed citation
network, and protein-protein interaction network. On node classification tasks,
GraphNAS can design a novel network architecture that rivals the best
human-invented architecture in terms of test set accuracy.
