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Learning Graph Edit Distance by Graph Neural Networks
[article]
2020
arXiv
pre-print
The emergence of geometric deep learning as a novel framework to deal with graph-based representations has faded away traditional approaches in favor of completely new methodologies. In this paper, we propose a new framework able to combine the advances on deep metric learning with traditional approximations of the graph edit distance. Hence, we propose an efficient graph distance based on the novel field of geometric deep learning. Our method employs a message passing neural network to capture
arXiv:2008.07641v1
fatcat:gqq6fmwkqbf4zlkzk263cyahsm