Graph neural networks (GNNs) are powerful tools for performing data science
tasks in various domains. Although we use GNNs in wide application scenarios,
it is a laborious task for researchers and practitioners to design/select
optimal GNN rchitectures in diverse graphs. To save human efforts and
computational costs, graph neural architecture search (Graph NAS) has been used
to search for a sub-optimal GNN architecture that combines existing components.
However, there are no existing Graph NAS methods that satisfy explainability,
efficiency, and adaptability to various graphs. Therefore, we propose an
efficient and explainable Graph NAS method, called ExGNAS, which consists of
(i) a simple search space that can adapt to various graphs and (ii) a search
algorithm that makes the decision process explainable. The search space
includes only fundamental functions that can handle homophilic and heterophilic
graphs. The search algorithm efficiently searches for the best GNN architecture
via Monte-Carlo tree search without neural models. The combination of our
search space and algorithm achieves finding accurate GNN models and the
important functions within the search space. We comprehensively evaluate our
method compared with twelve hand-crafted GNN architectures and three Graph NAS
methods in four graphs. Our experimental results show that ExGNAS increases AUC
up to 3.6 and reduces run time up to 78\% compared with the state-of-the-art
Graph NAS methods. Furthermore, we show ExGNAS is effective in analyzing the
difference between GNN architectures in homophilic and heterophilic graphs.

图神经网络（GNN）广泛应用于各个领域的数据科学任务，但为了设计 / 选择最佳的 GNN 结构，研究人员和实践者需要付出大量的努力和计算成本。为了节省人力努力和计算成本，我们提出了一种高效且可解释的图神经架构搜索方法（ExGNAS），它包括适用于各类图的简单搜索空间和能够解释决策过程的搜索算法。通过与现有方法进行综合评估，实验结果表明 ExGNAS 在准确性和运行时间方面超过当前的图神经架构搜索方法，并有效分析同质图和异质图中 GNN 架构的差异。