Skip to main content

Advertisement

SpringerLink
Log in
Menu
Find a journal Publish with us
Search
Cart
Book cover

Joint IAPR International Workshops on Statistical Techniques in Pattern Recognition (SPR) and Structural and Syntactic Pattern Recognition (SSPR)

SSPR /SPR 2012: Structural, Syntactic, and Statistical Pattern Recognition pp 42–50Cite as

  1. Home
  2. Structural, Syntactic, and Statistical Pattern Recognition
  3. Conference paper
Graph Kernels: Crossing Information from Different Patterns Using Graph Edit Distance

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

  • Benoit Gaüzère24,
  • Luc Brun24 &
  • Didier Villemin25 
  • Conference paper
  • 2391 Accesses

  • 1 Citations

Part of the Lecture Notes in Computer Science book series (LNIP,volume 7626)

Abstract

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.

Keywords

  • 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.

Download conference paper PDF

References

  1. Brun, L., Gaüzére, B., Fourey, S.: Relationships between graph edit distance and maximal common unlabeled subgraph. Technical report, CNRS UMR 6072 GREYC (2012), http://hal.archives-ouvertes.fr/hal-00714879

  2. Bunke, H.: Error correcting graph matching: On the influence of the underlying cost function. IEEE Transactions on Pattern Analysis and Machine Intelligence 21(9), 917–922 (1999)

    CrossRef  Google Scholar 

  3. Fankhauser, S., Riesen, K., Bunke, H.: Speeding Up Graph Edit Distance Computation through Fast Bipartite Matching. In: Jiang, X., Ferrer, M., Torsello, A. (eds.) GbRPR 2011. LNCS, vol. 6658, pp. 102–111. Springer, Heidelberg (2011)

    CrossRef  Google Scholar 

  4. Gaüzère, B., Brun, L., Villemin, D.: Two new graph kernels and applications to chemoinformatics. Pattern Recognition Lett. (in Press, 2012)

    Google Scholar 

  5. Haussler, D.: Convolution kernels on discrete structures. Technical report, Dept. of Computer Science, University of California at Santa Cruz (1999)

    Google Scholar 

  6. Kashima, H., Tsuda, K., Inokuchi, A.: Kernels for graphs, ch. 7, pp. 155–170. MIT Press (2004)

    Google Scholar 

  7. Mahé, P., Vert, J.-P.: Graph kernels based on tree patterns for molecules. Machine Learning 75(1), 3–35 (2009)

    CrossRef  Google Scholar 

  8. Neuhaus, M., Bunke, H.: Bridging the gap between graph edit distance and kernel machines. World Scientific Pub. Co. Inc. (2007)

    Google Scholar 

  9. Zhang, K., Statman, R., Shasha, D.: On the editing distance between unordered labeled trees. Information Processing Letters 42(3), 133–139 (1992)

    CrossRef  MathSciNet  MATH  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. GREYC CNRS UMR 6072, Caen, France

    Benoit Gaüzère & Luc Brun

  2. LCMT CNRS UMR 6507, Caen, France

    Didier Villemin

Authors
  1. Benoit Gaüzère
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Luc Brun
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Didier Villemin
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Department of Computer Science, University of Auckland, Private Bag 92019, 1142, Auckland, New Zealand

    Georgy Gimel’farb

  2. Department of Computer Science, University of York, Deramore Lane, YO10 5GH, York, UK

    Edwin Hancock

  3. Institute of Media and Information Technology, Chiba University, Yayoi-cho 1-33, 263-8522, Inage-ku, Chiba, Japan

    Atsushi Imiya

  4. Technische Universität/Fraunhofer IGD, Fraunhoferstraße 5, 64283, Darmstadt, Germany

    Arjan Kuijper

  5. Graduate School of Information Science and Technology, Hokkaido University, 060-0814, Sapporo, Japan

    Mineichi Kudo

  6. Graduate School of Engineering, Tohoku University, 6-6-05 Aoba, Aramaki, Aoba-ku, 980-8579, Sendai, Miyagi, Japan

    Shinichiro Omachi

  7. Centre for Vision, Speech and Signal Processing, University of Surrey, GU2 7XH, Guildford, Surrey, UK

    Terry Windeatt

  8. C&C Innovation Research Laboratories, NEC Corporation, 8916-47 Takayama-cho, Ikoma-Shi, Nara, Japan

    Keiji Yamada

Rights and permissions

Reprints and Permissions

Copyright information

© 2012 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Gaüzère, B., Brun, L., Villemin, D. (2012). Graph Kernels: Crossing Information from Different Patterns Using Graph Edit Distance. In: Gimel’farb, G., et al. Structural, Syntactic, and Statistical Pattern Recognition. SSPR /SPR 2012. Lecture Notes in Computer Science, vol 7626. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-34166-3_5

Download citation

  • .RIS
  • .ENW
  • .BIB
  • DOI: https://doi.org/10.1007/978-3-642-34166-3_5

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-34165-6

  • Online ISBN: 978-3-642-34166-3

  • eBook Packages: Computer ScienceComputer Science (R0)

Share this paper

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

  • The International Association for Pattern Recognition

    Published in cooperation with

    http://www.iapr.org/

Search

Navigation

  • Find a journal
  • Publish with us

Discover content

  • Journals A-Z
  • Books A-Z

Publish with us

  • Publish your research
  • Open access publishing

Products and services

  • Our products
  • Librarians
  • Societies
  • Partners and advertisers

Our imprints

  • Springer
  • Nature Portfolio
  • BMC
  • Palgrave Macmillan
  • Apress
  • Your US state privacy rights
  • Accessibility statement
  • Terms and conditions
  • Privacy policy
  • Help and support

167.114.118.210

Not affiliated

Springer Nature

© 2023 Springer Nature