PSP: Progressive Space Pruning for Efficient Graph Neural Architecture Search

Recently, graph neural network (GNN) has achieved great success in many graph learning tasks such as node classifi-cation and graph classification. However, there is no single GNN architecture that can fit different graph datasets. Designing an effective GNN for a specific graph dataset requires considerable expert experience and huge computational costs. Inspired by the success of neural architecture search (NAS), searching the GNN architectures automatically has attracted more and more attention. Motivated by the fact that the search space plays a critical role in the NAS, we propose a novel and effective graph neural architecture search method called PSP from the perspective of search space design in this paper. We first propose an expressive search space composed of multiple cells. Instead of searching the entire architecture, we focus on searching the architecture of the cell. Then, we propose a progressive space pruning-based algorithm to search the architectures efficiently. Moreover, the data-specific search spaces and architectures ob-tained by PSP can be transferred to new graph datasets based on meta-learning. Extensive experimental results on different types of graph datasets reveal that PSP outperforms the state-of-the-art handcrafted architectures and the existing NAS methods in terms of effectiveness and efficiency.