On the Correlation of Graph Edit Distance and L1 Distance in the Attribute Statistics Embedding Space

  • Jaume Gibert
  • Ernest Valveny
  • Horst Bunke
  • Alicia Fornés
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7626)

Abstract

Graph embeddings in vector spaces aim at assigning a pattern vector to every graph so that the problems of graph classification and clustering can be solved by using data processing algorithms originally developed for statistical feature vectors. An important requirement graph features should fulfil is that they reproduce as much as possible the properties among objects in the graph domain. In particular, it is usually desired that distances between pairs of graphs in the graph domain closely resemble those between their corresponding vectorial representations. In this work, we analyse relations between the edit distance in the graph domain and the L 1 distance of the attribute statistics based embedding, for which good classification performance has been reported on various datasets. We show that there is actually a high correlation between the two kinds of distances provided that the corresponding parameter values that account for balancing the weight between node and edge based features are properly selected.

Keywords

Edit Distance Graph Match Node Label Edit Operation Node Information 
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

  • Jaume Gibert
    • 1
  • Ernest Valveny
    • 1
  • Horst Bunke
    • 2
  • Alicia Fornés
    • 1
  1. 1.Computer Vision CenterUniversitat Autònoma de Barcelona, Edifici O Campus UABSpain
  2. 2.Institute for Computer Science and Applied MathematicsUniversity of BernBernSwitzerland

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