Lightweight graph neural network architecture search based on heuristic algorithms

A graph neural network is a deep learning model for processing graph data. In recent years, graph neural network architectures have become more and more complex as the research progresses, thus the design of graph neural networks has become an important task. Graph Neural Architecture Search aims to automate the design of graph neural network architectures. However, current methods require large computational resources, cannot be applied in lightweight scenarios, and the search process is not transparent. To address these challenges, this paper proposes a graph neural network architecture search method based on a heuristic algorithm combining tabu search and evolutionary strategies (Gnas-Te). Gnas-Te mainly consists of a tabu search algorithm module and an evolutionary strategy algorithm module. The tabu Search Algorithm Module designs and implements for the first time the tabu Search Algorithm suitable for the search of graph neural network architectures, and uses the maintenance of the tabu table to guide the search process. The evolutionary strategy Algorithm Module implements the evolutionary strategy Algorithm for the search of architectures with the design goal of being light-weight. After the reflection and implementation of Gnas-Te, in order to provide an accurate evaluation of the neural architecture search process, a new metric EASI is proposed. Gnas-Te searched architecture is comparable to the excellent human-designed graph neural network architecture. Experimental results on three real datasets show that Gnas-Te has a 1.37% improvement in search accuracy and a 37.7% reduction in search time to the state-of-the-art graph neural network architecture search method for an graph node classification task and can find high allround-performance architectures which are comparable to the excellent human-designed graph neural network architecture. Gnas-Te implements a lightweight and efficient search method that reduces the need of computational resources for searching graph neural network structures and meets the need for high-accuracy architecture search in the case of insufficient computational resources.