  Graph neural architecture search has received a lot of attention as Graph
Neural Networks (GNNs) has been successfully applied on the non-Euclidean data
recently. However, exploring all possible GNNs architectures in the huge search
space is too time-consuming or impossible for big graph data. In this paper, we
propose a parallel graph architecture search (GraphPAS) framework for graph
neural networks. In GraphPAS, we explore the search space in parallel by
designing a sharing-based evolution learning, which can improve the search
efficiency without losing the accuracy. Additionally, architecture information
entropy is adopted dynamically for mutation selection probability, which can
reduce space exploration. The experimental result shows that GraphPAS
outperforms state-of-art models with efficiency and accuracy simultaneously.
