Implicit and Explicit Graph Embedding: Comparison of Both Approaches on Chemoinformatics Applications

  • Benoit Gaüzère
  • Makoto Hasegawa
  • Luc Brun
  • Salvatore Tabbone
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7626)


Defining similarities or distances between graphs is one of the bases of the structural pattern recognition field. An important trend within this field consists in going beyond the simple formulation of similarity measures by studying properties of graph’s spaces induced by such distance or similarity measures . Such a problematic is closely related to the graph embedding problem. In this article, we investigate two types of similarity measures. The first one is based on the notion of graph edit distance which aims to catch a global dissimilarity between graphs. The second family is based on comparisons of bags of patterns extracted from graphs to be compared. Both approaches are detailed and their performances are evaluated on different chemoinformatics problems.


Tree Pattern Edit Distance Input Graph Graph Match Krein Space 
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
  • Makoto Hasegawa
    • 2
  • Luc Brun
    • 1
  • Salvatore Tabbone
    • 2
  1. 1.Université de Caen - Basse Normandie-GREYC CNRS UMR 6072CaenFrance
  2. 2.Université de Lorraine-LORIA CNRS UMR 7503NancyFrance

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