@inproceedings{ac65f0dab44f4bbd970cbf1c2b630622,
title = "Physics-Informed Graph Learning",
abstract = "An expeditious development of graph learning in recent years has found innumerable applications in several di-versified 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.",
keywords = "graph learning, graph neural networks, network embedding, network representation learning, physics",
author = "Ciyuan Peng and Feng Xia and Vidya Saikrishna and Huan Liu",
note = "Publisher Copyright: {\textcopyright} 2022 IEEE.; 22nd IEEE International Conference on Data Mining Workshops, ICDMW 2022 ; Conference date: 28-11-2022 Through 01-12-2022",
year = "2022",
doi = "10.1109/ICDMW58026.2022.00100",
language = "English (US)",
series = "IEEE International Conference on Data Mining Workshops, ICDMW",
publisher = "IEEE Computer Society",
pages = "732--739",
editor = "Candan, {K. Selcuk} and Dinh, {Thang N.} and Thai, {My T.} and Takashi Washio",
booktitle = "Proceedings - 22nd IEEE International Conference on Data Mining Workshops, ICDMW 2022",
}