Equivariant Graph Hierarchy-Based Neural Networks

• URL: http://arxiv.org/abs/2202.10643v1
• Date: Tue, 22 Feb 2022 03:11:47 GMT
• Title: Equivariant Graph Hierarchy-Based Neural Networks
• Authors: Jiaqi Han, Yu Rong, Tingyang Xu, Fuchun Sun, Wenbing Huang
• Abstract summary: We propose Equivariant Hierarchy-based Graph Networks (EGHNs) EGHNs consist of the three key components: generalized Equivariant Matrix Message Passing (EMMP), E-Pool and E-UpPool. Considerable experimental evaluations verify the effectiveness of our EGHN on several applications including multi-object dynamics simulation, motion capture, and protein dynamics modeling.
• Score: 53.60804845045526
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Equivariant Graph neural Networks (EGNs) are powerful in characterizing the dynamics of multi-body physical systems. Existing EGNs conduct flat message passing, which, yet, is unable to capture the spatial/dynamical hierarchy for complex systems particularly, limiting substructure discovery and global information fusion. In this paper, we propose Equivariant Hierarchy-based Graph Networks (EGHNs) which consist of the three key components: generalized Equivariant Matrix Message Passing (EMMP) , E-Pool and E-UpPool. In particular, EMMP is able to improve the expressivity of conventional equivariant message passing, E-Pool assigns the quantities of the low-level nodes into high-level clusters, while E-UpPool leverages the high-level information to update the dynamics of the low-level nodes. As their names imply, both E-Pool and E-UpPool are guaranteed to be equivariant to meet physic symmetry. Considerable experimental evaluations verify the effectiveness of our EGHN on several applications including multi-object dynamics simulation, motion capture, and protein dynamics modeling.

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