Graph neural architecture prediction

Graph neural networks (GNNs) have shown their superiority in the modeling of graph data. Recently, increasing attention has been paid to automatic graph neural architecture search, aiming to overcome the shortcomings of manually constructing GNN architectures that requires a lot of expert experience. However, existing graph neural architecture search (GraphNAS) methods can only select architecture from the partial evaluated GNN architectures. To solve the challenges, we propose a Graph N eural A rchitecture P rediction (GraphNAP) framework, which can select the optimal GNN architecture from the search space efficiently. To achieve this goal, a neural predictor is designed in GraphNAP. Firstly, the neural predictor is trained by a small number of sampled GNN architectures. Then, the trained neural predictor is used to predict all GNN architectures in the search space. In this way, GraphNAP can efficiently explore the performance of all GNN architectures in the search space and then select the optimal GNN architecture. The experimental results show that GraphNAP outperforms state-of-the-art both handcrafted and GraphNAS-based methods for both graph and node classification tasks. The python implementation of GraphNAP can be found at https://github.com/BeObm/GraphNAP .