论文标题
物理信息的图形学习
Physics-Informed Graph Learning
论文作者
论文摘要
近年来，迅速发展的图形学习发现了在几个多元化领域的无数应用程序。主要相关的挑战是图数据的体积和复杂性。图形学习模型无法有效地学习图形信息。为了赔偿这种效率低下，物理信息图（PIGL）正在出现。 PigL在执行图形学习时结合了物理规则，这具有巨大的好处。本文介绍了对PIGL方法的系统评价。我们首先引入图形学习模型的统一框架，然后检查与统一框架有关的现有PIGL方法。我们还讨论了PIGL的一些未来挑战。预计该调查文件将刺激与PIGL有关的创新研发活动。
An expeditious development of graph learning in recent years has found innumerable applications in several diversified fields. Of the main associated challenges are the volume and complexity of graph data. The graph learning models suffer from the inability to efficiently learn graph information. In order to indemnify this inefficacy, physics-informed graph learning (PIGL) is emerging. PIGL incorporates physics rules while performing graph learning, which has enormous benefits. This paper presents a systematic review of PIGL methods. We begin with introducing a unified framework of graph learning models followed by examining existing PIGL methods in relation to the unified framework. We also discuss several future challenges for PIGL. This survey paper is expected to stimulate innovative research and development activities pertaining to PIGL.
