Abstract

We present a neural approach for approximating the automorphism partitioning problem of a given graph. This approach combines the energy minimization process of neural networks for combinatorial optimization problems with simple group-theoretic properties. Neural networks are applied to rapidly find relevant automorphisms while group-theoretic information guides the search for these automorphisms.

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