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 architectures 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.

翻译：图神经网络（GNNs）是处理各领域数据科学任务的强大工具。尽管GNNs已广泛应用于多种场景，但研究者与从业者在不同图结构中设计/选择最优GNN架构仍是项艰巨任务。为节省人力与计算成本，图神经架构搜索（Graph NAS）已被用于搜索结合现有组件的次优GNN架构。然而，现有Graph NAS方法无法同时满足可解释性、高效性及对不同图结构的适应性。为此，我们提出一种高效可解释的Graph NAS方法ExGNAS，其包含：(i) 可适配多种图结构的简洁搜索空间，(ii) 使决策过程可解释的搜索算法。该搜索空间仅包含能处理同配性与异配性图的基础函数。搜索算法通过蒙特卡洛树搜索（无需神经模型）高效寻找最优GNN架构。搜索空间与算法的结合实现了精准GNN模型及关键功能函数的发现。我们在四种图结构上，将本方法与十二种手工设计GNN架构及三种Graph NAS方法进行综合评估。实验结果表明：与最先进的Graph NAS方法相比，ExGNAS在AUC指标上最高提升3.6，运行时间最高降低78%。此外，我们验证了ExGNAS在分析同配性与异配性图中GNN架构差异方面的有效性。