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Metric Learning in Graph Matching Problems

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One of the important and computationally complex problems in graph theory is graph matching, i.e., finding a correspondence between the vertices of a pair of graphs. Modern rapidly developing methods are largely based on machine learning. We propose a new approach based on deep learning of a graph neuron network combining convolutional and Siamese techniques. This involves learning the metrics between graph vertices and then using these metrics for matching. Experiments on sets of real data demonstrate the usefulness of the proposed approach.

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Authors and Affiliations

  1. Faculty of Computational Mathematics and Cybernetics, Lomonosov Moscow State University, Moscow, Russia

    V. D. Kozlov & A. I. Maisuradze

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  1. V. D. Kozlov
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  2. A. I. Maisuradze
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Correspondence to V. D. Kozlov.

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Translated from Prikladnaya Matematika i Informatika, No. 64, 2020, pp. 55–63.

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Kozlov, V.D., Maisuradze, A.I. Metric Learning in Graph Matching Problems. Comput Math Model 31, 477–483 (2020). https://doi.org/10.1007/s10598-021-09509-y

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