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.
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Jain, B.J., Wysotzki, F. (2003). A Novel Neural Network Approach to Solve Exact and Inexact Graph Isomorphism Problems. In: Kaynak, O., Alpaydin, E., Oja, E., Xu, L. (eds) Artificial Neural Networks and Neural Information Processing — ICANN/ICONIP 2003. ICANN ICONIP 2003 2003. Lecture Notes in Computer Science, vol 2714. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44989-2_36
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DOI: https://doi.org/10.1007/3-540-44989-2_36
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