Graph Automorphism Group Equivariant Neural Networks

• URL: http://arxiv.org/abs/2307.07810v2
• Date: Tue, 28 May 2024 12:25:00 GMT
• Title: Graph Automorphism Group Equivariant Neural Networks
• Authors: Edward Pearce-Crump, William J. Knottenbelt,
• Abstract summary: Permutation equivariant neural networks are typically used to learn from data that lives on a graph. We show how to construct neural networks that are equivariant to Aut$(G)$ by obtaining a full characterisation of the learnable, linear, Aut$(G)$-equivariant functions.
• Score: 1.9643748953805935
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Permutation equivariant neural networks are typically used to learn from data that lives on a graph. However, for any graph $G$ that has $n$ vertices, using the symmetric group $S_n$ as its group of symmetries does not take into account the relations that exist between the vertices. Given that the actual group of symmetries is the automorphism group Aut$(G)$, we show how to construct neural networks that are equivariant to Aut$(G)$ by obtaining a full characterisation of the learnable, linear, Aut$(G)$-equivariant functions between layers that are some tensor power of $\mathbb{R}^{n}$. In particular, we find a spanning set of matrices for these layer functions in the standard basis of $\mathbb{R}^{n}$. This result has important consequences for learning from data whose group of symmetries is a finite group because a theorem by Frucht (1938) showed that any finite group is isomorphic to the automorphism group of a graph.

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