- Title
- Physics-informed graph learning
- Creator
- Peng, Ciyuan; Xia, Feng; Saikrishna, Vidya; Liu, Huan
- Date
- 2022
- Type
- Text; Conference paper
- Identifier
- http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/191458
- Identifier
- vital:17812
- Identifier
-
https://doi.org/10.1109/ICDMW58026.2022.00100
- Identifier
- ISBN:2375-9232 (ISSN); 9798350346091 (ISBN)
- 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. © 2022 IEEE.
- Publisher
- IEEE Computer Society
- Relation
- 22nd IEEE International Conference on Data Mining Workshops, ICDMW 2022, Orlando, Florida, 28 November to 1 December 2022, Proceedings: IEEE International Conference on Data Mining Workshops, ICDMW Vol. 2022-November, p. 732-739
- Rights
- All metadata describing materials held in, or linked to, the repository is freely available under a CC0 licence
- Rights
- Copyright © 2022 IEEE
- Subject
- Graph learning; Graph neural networks; Network embedding; Network representation learning; Physics
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