Matching Hierarchical Structures Using Association Graphs
IEEE Transactions on Pattern Analysis and Machine Intelligence
Replicator equations, maximal cliques, and graph isomorphism
Neural Computation
Fast Winner-Takes-All Networks for the Maximum Clique Problem
KI '02 Proceedings of the 25th Annual German Conference on AI: Advances in Artificial Intelligence
Neural Networks for Combinatorial Optimization: a Review of More Than a Decade of Research
INFORMS Journal on Computing
Approximating maximum clique with a Hopfield network
IEEE Transactions on Neural Networks
Towards the unification of structural and statistical pattern recognition
Pattern Recognition Letters
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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.