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Solving inexact graph isomorphism problems using neural networks
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DOI：10.1016/j.neucom.2004.01.189.png)
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Abstract
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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 this approach is based on a neural refinement procedure to reduce the search space followed by an energy-minimizing matching process. Experiments on random weighted graphs in the range of 100-5000 vertices and on chemical molecular structures are presented and discussed. (C) 2004 Elsevier B.V. All rights reserved.
Keywords:
graph isomorphism
association graph
maximum clique
Hopfield network
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