Summary of A Survey Of Geometric Graph Neural Networks: Data Structures, Models and Applications, by Jiaqi Han et al.
A Survey of Geometric Graph Neural Networks: Data Structures, Models and Applications
by Jiaqi Han, Jiacheng Cen, Liming Wu, Zongzhao Li, Xiangzhe Kong, Rui Jiao, Ziyang Yu, Tingyang Xu, Fandi Wu, Zihe Wang, Hongteng Xu, Zhewei Wei, Deli Zhao, Yang Liu, Yu Rong, Wenbing Huang
First submitted to arxiv on: 1 Mar 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper presents a comprehensive review of Geometric Graph Neural Networks (GNNs), which are designed to process graphs with geometric features, exhibiting physical symmetries like translations, rotations, and reflections. The authors highlight that current GNNs are ineffective in processing such graphs, so they propose various geometric GNNs with invariant/equivariant properties to better characterize their geometry and topology. The paper provides a unified view of existing models from the geometric message passing perspective, summarizes applications and related datasets, and discusses challenges and future directions for methodology development and experimental evaluation. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Geometric graphs are special types of graphs that have physical symmetries like translations, rotations, and reflections. Currently, Graph Neural Networks (GNNs) can’t process these graphs effectively. To solve this problem, researchers developed GNNs with special properties to understand the geometry and topology of geometric graphs. This paper reviews all the different models and applications related to these GNNs, which helps other scientists develop new methods and test them. |
