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Approximating GED Using a Stochastic Generator and Multistart IPFP

  • Nicolas Boria
  • Sébastien Bougleux
  • Luc Brun
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11004)

Abstract

The Graph Edit Distance defines the minimal cost of a sequence of elementary operations transforming a graph into another graph. This versatile concept with an intuitive interpretation is a fundamental tool in structural pattern recognition. However, the exact computation of the Graph Edit Distance is \(\mathcal {NP}\)-complete. Iterative algorithms such as the ones based on Franck-Wolfe method provide a good approximation of true edit distance with low execution times. However, underlying cost function to optimize being neither concave nor convex, the accuracy of such algorithms highly depends on the initialization. In this paper, we propose a smart random initializer using promising parts of previously computed solutions.

Keywords

Graph edit distance Parallel gradient descents Multistart Stochastic warm start 

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.Normandie Univ, UNICAEN, ENSICAEN, CNRS, GREYCCaenFrance

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