IAM Graph Database Repository for Graph Based Pattern Recognition and Machine Learning

  • Kaspar Riesen
  • Horst Bunke
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5342)

Abstract

In recent years the use of graph based representation has gained popularity in pattern recognition and machine learning. As a matter of fact, object representation by means of graphs has a number of advantages over feature vectors. Therefore, various algorithms for graph based machine learning have been proposed in the literature. However, in contrast with the emerging interest in graph based representation, a lack of standardized graph data sets for benchmarking can be observed. Common practice is that researchers use their own data sets, and this behavior cumbers the objective evaluation of the proposed methods. In order to make the different approaches in graph based machine learning better comparable, the present paper aims at introducing a repository of graph data sets and corresponding benchmarks, covering a wide spectrum of different applications.

Keywords

Undirected Edge Handwritten Digit Graph Kernel Distortion Level 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.

References

  1. 1.
    Duda, R., Hart, P., Stork, D.: Pattern Classification, 2nd edn. Wiley Interscience, Hoboken (2000)MATHGoogle Scholar
  2. 2.
    Conte, D., Foggia, P., Sansone, C., Vento, M.: Thirty years of graph matching in pattern recognition. Int. Journal of Pattern Recognition and Artificial Intelligence 18(3), 265–298 (2004)CrossRefGoogle Scholar
  3. 3.
    Borgwardt, K., Ong, C., Schönauer, S., Vishwanathan, S., Smola, A., Kriegel, H.P.: Protein function prediction via graph kernels. Bioinformatics 21(1), 47–56 (2005)CrossRefGoogle Scholar
  4. 4.
    Mahé, P., Ueda, N., Akutsu, T.: Graph kernels for molecular structures – activity relationship analysis with support vector machines. Journal of Chemical Information and Modeling 45(4), 939–951 (2005)CrossRefGoogle Scholar
  5. 5.
    Ralaivola, L., Swamidass, S., Saigo, H., Baldi, P.: Graph kernels for chemical informatics. Neural Networks 18(8), 1093–1110 (2005)CrossRefGoogle Scholar
  6. 6.
    Schenker, A., Bunke, H., Last, M., Kandel, A.: Graph-Theoretic Techniques for Web Content Mining. World Scientific, Singapore (2005)CrossRefMATHGoogle Scholar
  7. 7.
    Le Saux, B., Bunke, H.: Feature selection for graph-based image classifiers. In: Marques, J.S., Pérez de la Blanca, N., Pina, P. (eds.) IbPRIA 2005. LNCS, vol. 3523, pp. 147–154. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  8. 8.
    Harchaoui, Z., Bach, F.: Image classification with segmentation graph kernels. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–8 (2007)Google Scholar
  9. 9.
    Luo, B., Wilson, R., Hancock, E.: Spectral embedding of graphs. Pattern Recognition 36(10), 2213–2223 (2003)CrossRefMATHGoogle Scholar
  10. 10.
    Neuhaus, M., Bunke, H.: An error-tolerant approximate matching algorithm for attributed planar graphs and its application to fingerprint classification. In: Fred, A., Caelli, T.M., Duin, R.P.W., Campilho, A.C., de Ridder, D. (eds.) SSPR&SPR 2004. LNCS, vol. 3138, pp. 180–189. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  11. 11.
    Bunke, H., Dickinson, P., Kraetzl, M., Wallis, W.: A Graph-Theoretic Approach to Enterprise Network Dynamics. Progress in Computer Science and Applied Logic (PCS), vol. 24. Birkhäuser, Basel (2007)MATHGoogle Scholar
  12. 12.
    Asuncion, A., Newman, D.: (UCI machine learning repository) University of California, Department of Information and Computer Science, http://www.ics.uci.edu/~mlearn/MLRepository.html
  13. 13.
    Bunke, H., Vento, M.: Benchmarking of graph matching algorithms. In: Proc. 2nd Int. Workshop on Graph Based Representations in Pattern Recognition, pp. 109–113 (1999)Google Scholar
  14. 14.
    Foggia, P., Sansone, C., Vento, M.: A database of graphs for isomorphism and subgraph isomorphism benchmarking. In: Proc. 3rd Int. Workshop on Graph Based Representations in Pattern Recognition, pp. 176–187 (2001)Google Scholar
  15. 15.
    Xu, K.: Bhoslib: Benchmarks with hidden optimum solutions for graph problems (maximum clique, maximum independent set, minimum vertex cover and vertex coloring), http://www.nlsde.buaa.edu.cn/~kexu/benchmarks/graph-benchmarks.htm
  16. 16.
    Neuhaus, M., Bunke, H.: Bridging the Gap Between Graph Edit Distance and Kernel Machines. World Scientific, Singapore (2007)CrossRefMATHGoogle Scholar
  17. 17.
    Bunke, H., Riesen, K.: A family of novel graph kernels for structural pattern recognition. In: Rueda, L., Mery, D., Kittler, J. (eds.) CIARP 2007. LNCS, vol. 4756, pp. 20–31. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  18. 18.
    Bunke, H., Allermann, G.: Inexact graph matching for structural pattern recognition. Pattern Recognition Letters 1, 245–253 (1983)CrossRefMATHGoogle Scholar
  19. 19.
    Alpaydin, E., Alimoglu, F.: Pen-Based Recognition of Handwritten Digits. Dept. of Computer Engineering, Bogazici University (1998)Google Scholar
  20. 20.
    Dosch, P., Valveny, E.: Report on the second symbol recognition contest. In: Wenyin, L., Lladós, J. (eds.) GREC 2005. LNCS, vol. 3926, pp. 381–397. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  21. 21.
    Neuhaus, M., Bunke, H.: A graph matching based approach to fingerprint classification using directional variance. In: Kanade, T., Jain, A., Ratha, N.K. (eds.) AVBPA 2005. LNCS, vol. 3546, pp. 191–200. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  22. 22.
    Watson, C., Wilson, C.: NIST Special Database 4, Fingerprint Database. National Institute of Standards and Technology (1992)Google Scholar
  23. 23.
    Nene, S., Nayar, S., Murase, H.: Columbia Object Image Library: COIL-100. Technical report, Department of Computer Science, Columbia University, New York (1996)Google Scholar
  24. 24.
    Comaniciu, D., Meer, P.: Robust analysis of feature spaces: Color image segmentation. In: IEEE Conf. on Comp. Vision and Pattern Recognition, pp. 750–755 (1997)Google Scholar
  25. 25.
    Harris, C., Stephens, M.: A combined corner and edge detection. In: Proceedings of The Fourth Alvey Vision Conference, pp. 147–151 (1988)Google Scholar
  26. 26.
    DTP, AIDS antiviral screen (2004), http://dtp.nci.nih.gov/docs/aids/aids_data.html
  27. 27.
    Kazius, J., McGuire, R., Bursi, R.: Derivation and validation of toxicophores for mutagenicity prediction. Journal of Medicinal Chemistry 48(1), 312–320 (2005)CrossRefGoogle Scholar
  28. 28.
    Berman, H., Westbrook, J., Feng, Z., Gilliland, G., Bhat, T., Weissig, H., Shidyalov, I., Bourne, P.: The protein data bank. Nucleic Acids Research 28, 235–242 (2000)CrossRefGoogle Scholar
  29. 29.
    Schomburg, I., Chang, A., Ebeling, C., Gremse, M., Heldt, C., Huhn, G., abd Schomburg, D.: Brenda, the enzyme database: updates and major new developments. Nucleic Acids Research 32 (2004) Database issue: D431–D433Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Kaspar Riesen
    • 1
  • Horst Bunke
    • 1
  1. 1.Institute of Computer Science and Applied MathematicsUniversity of BernBernSwitzerland

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