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Approximating Graph Edit Distance Using GNCCP

  • Benoît GaüzèreEmail author
  • Sébastien Bougleux
  • Luc Brun
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10029)

Abstract

The graph edit distance (GED) is a flexible and widely used dissimilarity measure between graphs. Computing the GED between two graphs can be performed by solving a quadratic assignment problem (QAP). However, the problem is NP complete hence forbidding the computation of the optimal GED on large graphs. To tackle this drawback, recent heuristics are based on a linear approximation of the initial QAP formulation. In this paper, we propose a method providing a better local minimum of the QAP formulation than our previous proposition based on IPFP. We adapt a convex concave regularization scheme initially designed for graph matching which allows to reach better local minimum and avoids the need of an initialization step. Several experiments demonstrate that our method outperforms previous methods in terms of accuracy, with a time still much lower than the computation of a GED.

Keywords

Permutation Matrix Graph Match Quadratic Assignment Problem Edit Operation Projection Step 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  • Benoît Gaüzère
    • 1
    Email author
  • Sébastien Bougleux
    • 2
  • Luc Brun
    • 3
  1. 1.Normandie Univ, INSA Rouen, LITISRouenFrance
  2. 2.Normandie Univ, UNICAEN, CNRS, GREYCRouenFrance
  3. 3.Normandie Univ, ENSICAEN, CNRS, GREYCRouenFrance

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