Approximation of Graph Edit Distance in Quadratic Time

  • Kaspar RiesenEmail author
  • Miquel Ferrer
  • Andreas Fischer
  • Horst Bunke
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9069)


The basic idea of a recent graph matching framework is to reduce the problem of graph edit distance (GED) to an instance of a linear sum assignment problem (LSAP). The optimal solution for this simplified GED problem can be computed in cubic time and is eventually used to derive a suboptimal solution for the original GED problem. Yet, for large scale graphs and/or large scale graph sets the cubic time complexity remains a severe handicap of this procedure. Therefore, we propose to use suboptimal algorithms – with quadratic rather than cubic time complexity – for solving the underlying LSAP. In particular, we introduce several greedy assignment algorithms for approximating GED. In an experimental evaluation we show that there is great potential for further speeding up the GED computation. Moreover, we empirically confirm that the distances obtained by this procedure remain sufficiently accurate for graph based pattern classification.


Greedy Algorithm Cost Matrix Greedy Approach Quadratic Time Assignment Cost 
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 Switzerland 2015

Authors and Affiliations

  • Kaspar Riesen
    • 1
    • 4
    Email author
  • Miquel Ferrer
    • 1
  • Andreas Fischer
    • 2
    • 3
  • Horst Bunke
    • 4
  1. 1.Institute for Information SystemsUniversity of Applied Sciences and Arts Northwestern SwitzerlandOltenSwitzerland
  2. 2.DIUF DepartmentUniversity of FribourgFribourgSwitzerland
  3. 3.iCoSys InstituteUniversity of Applied Sciences and Arts Western SwitzerlandFribourgSwitzerland
  4. 4.Institute of Computer Science and Applied MathematicsUniversity of BernBernSwitzerland

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