Sub-optimal Graph Matching by Node-to-node Assignment Classification

Abstract : In the recent years, Graph Edit Distance has awaken interest in the scientific community and some new graph-matching algorithms that compute it have been presented. Nevertheless, these algorithms usually cannot be used in real applications due to runtime restrictions. For this reason, other graph-matching algorithms have also been used that compute an approximation of the graph correspondence with lower run-time. Clearly, in a real application, there is a tradeoff between runtime and accuracy. One of the most costly part in these algorithms is the deduction of the node-to-node mapping. We present a new graph-matching algorithm that returns a graph correspondence without the explicit computation of the assignment problem. This is done thanks to a classification of the node-to-node assignment learned in a previous training stage.
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Submitted on : Friday, September 13, 2019 - 10:06:53 AM
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Xavier Cortés, Donatello Conte, Francesc Serratosa. Sub-optimal Graph Matching by Node-to-node Assignment Classification. Donatello Conte; Jean-Yves Ramel; Pasquale Foggia. Graph-Based Representations in Pattern Recognition, 11510, Springer, pp.35-44, 2019, Lecture Notes in Computer Science, 978-3-030-20080-0. ⟨10.1007/978-3-030-20081-7_4⟩. ⟨hal-02285797⟩

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