Spacetime $E(n)$-Transformer: Equivariant Attention for Spatio-temporal Graphs

Sergio Charles

Keywords: graph neural networks, graph representation learning

TL;DR: Equivariant Transformer architecture for spatio-temporal graph data.

Abstract: We introduce an $E(n)$-equivariant Transformer architecture for spatio-temporal graph data. By imposing rotation, translation, and permutation equivariance inductive biases in both space and time, we show that the Spacetime $E(n)$-Transformer (SET) outperforms purely spatial and temporal models without symmetry-preserving properties. We benchmark SET against said models on the $N$-body problem, a simple physical system with complex dynamics. While existing spatio-temporal graph neural networks focus on sequential modeling, we empirically demonstrate that leveraging underlying domain symmetries yields considerable improvements for modeling dynamical systems on graphs.

Primary Area: learning on graphs and other geometries & topologies

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Submission Number: 1075