A novel neural network approach to solve exact and inexact graph isomorphism problems

Abstract

We present a neural network approach to solve exact and inexact graph isomorphism problems for weighted graphs. In contrast to other neural heuristics or related methods our approach is based on approximating the automorphism partition of a graph to reduce the search space followed by an energy-minimizing matching process. Experiments on random graphs with 100-5000 vertices are presented and discussed. © Springer-Verlag Berlin Heidelberg 2003.