1 INTRODUCTION
2 THE BASIC NOTION OF SYMMETRY
2.1 Transformation and Group
2.2 Group Representation
2.3 Equivariance and Invariance
3 DATA STRUCTURE: FROM GRAPH TO GEOMETRIC GRAPH
3.1 Graph
3.2 Geometric Graph
4 MODEL: GEOMETRIC GNNS
4.1 Message Passing Neural Networks
4.2 Invariant Graph Neural Networks
4.3 Equivariant Graph Neural Networks
4.4 Geometric Graph Transformers
4.5 Theoretical Analysis on Expressivity
5 APPLICATIONS
5.1 Tasks on Particles
5.2 Tasks on Small Molecules
5.3 Tasks on Proteins
5.4 Tasks on Mol+Mol
5.5 Tasks on Mol+Protein
5.6 Tasks on Protein+Protein
5.7 Tasks on Other Domains
6 DISCUSSION AND FUTURE PROSPECT
7 CONCLUSION