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International Workshop on Graph-Based Representations in Pattern Recognition

GbRPR 2017: Graph-Based Representations in Pattern Recognition pp 222–231Cite as

Improved Graph Edit Distance Approximation with Simulated Annealing

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Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 10310))

Abstract

The present paper is concerned with graph edit distance, which is widely accepted as one of the most flexible graph dissimilarity measures available. A recent algorithmic framework for approximating the graph edit distance overcomes the major drawback of this distance model, viz. its exponential time complexity. Yet, this particular approximation suffers from an overestimation of the true edit distance in general. Overall aim of the present paper is to improve the distance quality of this approximation by means of a post-processing search procedure. The employed search procedure is based on the idea of simulated annealing, which turns out to be particularly suitable for complex optimization problems. In an experimental evaluation on several graph data sets the benefit of this extension is empirically confirmed.

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Notes

  1. 1.

    A similar notation is used for edges.

  2. 2.

    Edit operations of the form \((\varepsilon \rightarrow \varepsilon )\) can be dismissed, of course.

  3. 3.

    www.iam.unibe.ch/fki/databases/iam-graph-database.

  4. 4.

    On the other data sets very similar plots can be observed.

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Acknowledgements

This work has been supported by the Hasler Foundation Switzerland.

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Authors and Affiliations

  1. Institute for Information Systems, University of Applied Sciences FHNW, Riggenbachstrasse 16, 4600, Olten, Switzerland

    Kaspar Riesen

  2. Department of Informatics, University of Fribourg and HES-SO, 1700, Fribourg, Switzerland

    Andreas Fischer

  3. Institute of Computer Science and Applied Mathematics, University of Bern, Neubrückstrasse 10, 3012, Bern, Switzerland

    Kaspar Riesen & Horst Bunke

Authors
  1. Kaspar Riesen
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  2. Andreas Fischer
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  3. Horst Bunke
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Corresponding author

Correspondence to Kaspar Riesen .

Editor information

Editors and Affiliations

  1. Università degli Studi di Salerno, Fisciano, Italy

    Pasquale Foggia

  2. Chinese Academy of Sciences, Beijing, China

    Cheng-Lin Liu

  3. Università degli Studi di Salerno, Fisciano, Italy

    Mario Vento

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Riesen, K., Fischer, A., Bunke, H. (2017). Improved Graph Edit Distance Approximation with Simulated Annealing. In: Foggia, P., Liu, CL., Vento, M. (eds) Graph-Based Representations in Pattern Recognition. GbRPR 2017. Lecture Notes in Computer Science(), vol 10310. Springer, Cham. https://doi.org/10.1007/978-3-319-58961-9_20

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  • DOI: https://doi.org/10.1007/978-3-319-58961-9_20

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-58960-2

  • Online ISBN: 978-3-319-58961-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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