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. A lot of research has been evolving around the preservation of graph data in a low dimensional space. The graph learning models suffer from the inability to maintain original graph information. In order to compensate for this inability, physics-informed graph learning (PIGL) is emerging. PIGL incorporates physics rules while performing graph learning, which enables numerous potentials. This paper presents a systematic review of PIGL methods. We begin with introducing a unified framework of graph learning models, and then examine 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.

翻译：近年来,图表学习的快速发展在多个多样化领域发现了无数应用,其中主要的相关挑战是图形数据的数量和复杂性。围绕在低维空间保存图形数据,已经开展了许多研究。图表学习模型由于无法保持原始图形信息而受到影响。为了弥补这种无能,物理知情图形学习(PIGL)正在出现。PIGL在进行图学时纳入了物理规则,从而能够产生许多潜力。本文对PIGL方法进行了系统审查。我们首先采用一个统一的图表学习模型框架,然后研究与统一框架有关的现有PIGL方法。我们还讨论了PIGL今后面临的一些挑战。预计这份调查文件将促进与PIGL有关的创新研究和开发活动。