Edge-featured Graph Neural Architecture Search
This work addresses the need for automated GNN design to reduce human expertise, with incremental improvements by focusing on edge features.
The paper tackled the problem of neural architecture search for graph neural networks (GNNs) by incorporating edge features, which were previously ignored, to explore better architectures. The result was that EGNAS achieved higher performance than state-of-the-art methods on three graph tasks across six datasets.
Graph neural networks (GNNs) have been successfully applied to learning representation on graphs in many relational tasks. Recently, researchers study neural architecture search (NAS) to reduce the dependence of human expertise and explore better GNN architectures, but they over-emphasize entity features and ignore latent relation information concealed in the edges. To solve this problem, we incorporate edge features into graph search space and propose Edge-featured Graph Neural Architecture Search to find the optimal GNN architecture. Specifically, we design rich entity and edge updating operations to learn high-order representations, which convey more generic message passing mechanisms. Moreover, the architecture topology in our search space allows to explore complex feature dependence of both entities and edges, which can be efficiently optimized by differentiable search strategy. Experiments at three graph tasks on six datasets show EGNAS can search better GNNs with higher performance than current state-of-the-art human-designed and searched-based GNNs.