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This work proposes a Siamese Graph Neural Network (Siamese GNN) for subgroup prediction using Cayley graph representations of finite groups, which provides a unified approach for learning subgroup relations from finite groups.
Graph neural networks (GNNs) have recently been shown to learn algebraic properties of finite groups from their Cayley graphs [1,2]. In this work, we investigate whether such models generalize to infinite finitely generated groups. Motivated by Gromov's theorem [3], a GNN is trained and validated exclusively on finite...
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