E(n) Equivariant Message Passing Simplicial Networks

TL;DR

This paper introduces E(n) Equivariant Message Passing Simplicial Networks (EMPSNs), a novel method that leverages geometric simplices and equivariance to improve learning on geometric graphs and point clouds.

Contribution

EMPSNs extend equivariant graph neural networks to higher-dimensional simplices, integrating geometric information into message passing for enhanced performance and robustness.

Findings

EMPSNs outperform existing methods on geometric graph tasks.

Incorporating geometric info reduces over-smoothing effects.

EMPSNs achieve state-of-the-art results comparable to leading approaches.

Abstract

This paper presents $\mathrm{E}(n)$ Equivariant Message Passing Simplicial Networks (EMPSNs), a novel approach to learning on geometric graphs and point clouds that is equivariant to rotations, translations, and reflections. EMPSNs can learn high-dimensional simplex features in graphs (e.g. triangles), and use the increase of geometric information of higher-dimensional simplices in an $\mathrm{E}(n)$ equivariant fashion. EMPSNs simultaneously generalize $\mathrm{E}(n)$ Equivariant Graph Neural Networks to a topologically more elaborate counterpart and provide an approach for including geometric information in Message Passing Simplicial Networks. The results indicate that EMPSNs can leverage the benefits of both approaches, leading to a general increase in performance when compared to either method. Furthermore, the results suggest that incorporating geometric information serves as an effective measure against over-smoothing in message passing networks, especially when operating on high-dimensional simplicial structures. Last, we show that EMPSNs are on par with state-of-the-art approaches for learning on geometric graphs.