A Survey of Geometric Graph Neural Networks: Data Structures, Models and
Applications
- URL: http://arxiv.org/abs/2403.00485v1
- Date: Fri, 1 Mar 2024 12:13:04 GMT
- Title: A Survey of Geometric Graph Neural Networks: Data Structures, Models and
Applications
- Authors: Jiaqi Han, Jiacheng Cen, Liming Wu, Zongzhao Li, Xiangzhe Kong, Rui
Jiao, Ziyang Yu, Tingyang Xu, Fandi Wu, Zihe Wang, Hongteng Xu, Zhewei Wei,
Yang Liu, Yu Rong, Wenbing Huang
- Abstract summary: This paper presents a survey of data structures, models, and applications related to geometric GNNs.
We provide a unified view of existing models from the geometric message passing perspective.
We also summarize the applications as well as the related datasets to facilitate later research for methodology development and experimental evaluation.
- Score: 67.33002207179923
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Geometric graph is a special kind of graph with geometric features, which is
vital to model many scientific problems. Unlike generic graphs, geometric
graphs often exhibit physical symmetries of translations, rotations, and
reflections, making them ineffectively processed by current Graph Neural
Networks (GNNs). To tackle this issue, researchers proposed a variety of
Geometric Graph Neural Networks equipped with invariant/equivariant properties
to better characterize the geometry and topology of geometric graphs. Given the
current progress in this field, it is imperative to conduct a comprehensive
survey of data structures, models, and applications related to geometric GNNs.
In this paper, based on the necessary but concise mathematical preliminaries,
we provide a unified view of existing models from the geometric message passing
perspective. Additionally, we summarize the applications as well as the related
datasets to facilitate later research for methodology development and
experimental evaluation. We also discuss the challenges and future potential
directions of Geometric GNNs at the end of this survey.
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