A surrogate evolutionary neural architecture search algorithm for graph neural networks

Abstract

Due to the unique construction module and design of graph neural networks (GNNs), neural architecture search (NAS) methods specifically for GNNs have become a promising research hotspot in recent years. Among the existing methods, one class of methods microscopically searches for the constituent components of network layers. However, most of them ignore the topology connections between network layers or the feature fusion strategies. Another class of methods, called differentiable architecture search methods, has the advantage of searching topology connections and feature fusion strategies. However, constrained by the requirement of predefining all candidate operations, these methods can only sample a limited number of network layers. In this paper, we propose a surrogate evolutionary graph neural architecture search (GNAS) algorithm whose search space contains not only the microscopic network layer components but also topology connections and feature fusion strategies (called CTFGNAS). The GNN sampled in CTFGNAS is represented by a simple one-dimensional vector and does not fix the network depth. To address the problem that traditional crossover and mutation operators applied to GNAS may produce illegal solutions, we design a repair operation to guarantee the legitimacy of the solutions. The network depth is also increased with a large probability in the mutation operation to alleviate the oversmoothing problem. In addition, to cope with the challenge of computational resources due to the increased search space, we form a surrogate model with three classical regression models, where only a small number of solutions are truly evaluated for their fitness, and the remaining large number of solutions are predicted for their fitness by the surrogate model. Finally, experiments are executed on six widely used real-world datasets. The experimental results illustrate that CTFGNAS obtains more effective results than the state-of-the-art handcrafted GNNs and GNAS methods on all datasets. CTFGNAS is now available on the following website: https://github.com/chnyliu/CTFGNAS.