Automatic Design of Deep Graph Neural Networks With Decoupled Mode

Graph neural networks (GNNs), a class of deep learning models designed for performing information interaction on non-Euclidean graph data, have been successfully applied to node classification tasks in various applications such as citation networks, recommender systems, and natural language processing. Graph node classification is an important research field for node-level tasks in graph data mining. Recently, due to the limitations of shallow GNNs, many researchers have focused on designing deep graph learning models. Previous GNN architecture search works only solve shallow networks (e.g., less than four layers). It is challenging and nonefficient to manually design deep GNNs for challenges like over-smoothing and information squeezing, which greatly limits their capabilities on large-scale graph data. In this article, we propose a novel neural architecture search (NAS) method for designing deep GNNs automatically and further exploit the application potential on various node classification tasks. Our innovations lie in two aspects, where we first redesign the deep GNNs search space for architecture search with a decoupled mode based on propagation and transformation processes, and we then formulate and solve the problem as a multiobjective optimization to balance accuracy and computational efficiency. Experiments on benchmark graph datasets show that our method performs very well on various node classification tasks, and exploiting large-scale graph datasets further validates that our proposed method is scalable.