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

• URL: http://arxiv.org/abs/2408.06039v1
• Date: Mon, 12 Aug 2024 10:13:45 GMT
• Title: Spacetime $E(n)$-Transformer: Equivariant Attention for Spatio-temporal Graphs
• Authors: Sergio G. Charles,
• Abstract summary: We show that the Spacetime $E(n)$-Transformer (SET) outperforms purely spatial and temporal models without symmetry properties. We demonstrate that leveraging underlying domain symmetries yields considerable improvements for modeling systems on graphs.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• 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 charged $N$-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.

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