Graph Kernels: Crossing Information from Different Patterns Using Graph Edit Distance

  • Benoit Gaüzère
  • Luc Brun
  • Didier Villemin
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7626)


Graph kernels allow to define metrics on graph space and constitute thus an efficient tool to combine advantages of structural and statistical pattern recognition fields. Within the chemoinformatics framework, kernels are usually defined by comparing the number of occurences of patterns extracted from two different graphs. Such a graph kernel construction scheme neglects the fact that similar but not identical patterns may lead to close properties. We propose in this paper to overcome this drawback by defining our kernel as a weighted sum of comparisons between all couples of patterns. In addition, we propose an efficient computation of the optimal edit distance on a limited set of finite trees. This extension has been tested on two chemoinformatics problems.


Linear Pattern Edit Operation Statistical Pattern Recognition Graph Kernel Graph Edit Distance 
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-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Benoit Gaüzère
    • 1
  • Luc Brun
    • 1
  • Didier Villemin
    • 2
  1. 1.GREYC CNRS UMR 6072CaenFrance
  2. 2.LCMT CNRS UMR 6507CaenFrance

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