1 citations · 1 across the 5 of their papers we have counts for
4 papers · 1 filter
Graph Neural Networks for Predicting Solvability of Finite Groups
Tal Weissblat
We present a Graph Neural Network (GNN) framework for the classification of finite groups according to their solvability. Using undirected Cayley graph representations, the propose…
A General Framework for Learning Algebraic Properties from Cayley Graphs using Graph Neural Networks
Tal Weissblat
In this work, we present a general Graph Neural Network (GNN) framework for learning algebraic properties of finite groups from their Cayley graph representations. The framework pr…
From Finite Cayley Graphs to Growth of Infinite Groups
Tal Weissblat
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 model…
Learning Subgroup Relations Using Siamese Graph Neural Networks
Tal Weissblat
Determining whether one finite group is isomorphic to a subgroup of another is a fundamental problem in computational group theory. In this work, we propose a Siamese Graph Neural…