The graphlet spectrum

Proceedings of the 26th International Conference On Machine Learning, ICML 2009

Volumn , Issue , 2009, Pages 529-536

  Kondor, Risi ^a   Shervashidze, Nino ^b   Borgwardt, Karsten M ^b  

^a UNIVERSITY COLLEGE LONDON   (United Kingdom)

^b MAX PLANCK INSTITUTE FOR BIOLOGICAL CYBERNETICS   (Germany)

Author keywords

[No Author keywords available]

Indexed keywords

ALGEBRAIC METHOD; EXPERIMENTAL EVALUATION; GRAPH INVARIANT; GRAPH KERNELS; GROUP REPRESENTATION THEORY; RELATIVE POSITIONS; SUBGRAPHS;

FLUORINE CONTAINING POLYMERS;

ROBOT LEARNING;

EID: 71149089562     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (18)

1. Bach, F. (2008). Graph kernels between point clouds. Proc. Intl. Conf. Machine Learning (pp. 25-32).
2. Borgwardt, K. M., & Kriegel, H.-P. (2005). Shortest-path kernels on graphs. Proc. Intl. Conf. Data Mining (pp. 74-81).
3. Borgwardt, K. M., Ong, C. S., Schonauer, S., Vish-wanathan, S. V. N., Smola, A. J., & Kriegel, H. P. (2005). Protein function prediction via graph kernels. Bioinformatics, 21, i47-i56.
4. Chang, C.-C., & Lin, C.-J. (2001). LIBSVM: a library for support vector machines. Software available at http://www.csie.ntu.edu.tw/~cjlin/ libsvm.
5. Clausen, M. (1989). Fast generalized Fourier transforms. Theor. Comput. Sci., 55-63.
6. Debnath, A. K., Lopez de Compadre, R. L., Deb-nath, G., Shusterman, A. J., & Hansch, C. (1991). Structure-activity relationship of mutagenic aromatic and heteroaromatic nitro compounds. correlation with molecular orbital energies and hydrophobicity. J. Med Chem, 34, 786-797.
7. Gärtner, T., Flach, P., & Wrobel, S. (2003). On graph kernels: Hardness results and efficient alternatives. Proc. Annual Conf. Computational Learning Theory (pp. 129-143). Springer.
8. Kashima, H., Tsuda, K., & Inokuchi, A. (2003). Marginalized kernels between labeled graphs. Proc. Intl. Conf. Machine Learning (pp. 321-328). San Francisco, CA: Morgan Kaufmann.
9. nob: a C++ library for fast Fourier transforms on the symmetric group. Available at http://www.gatsby.ucl.ac.uk/ ~risi/Snob/.
10. Kondor, R., & Borgwardt, K. (2008). The skew spectrum of graphs. Proc. Intl. Conf. Machine Learning (pp. 496-503).
11. Maslen, D. K. (1997). The computation of Fourier transforms on the symmetric group. Math. Comp, 67, 1121-1147.
12. Mikkonen, T. (2007). The ring of graph invariants-graphic values. arXiv: 0712/0146.
13. Rockmore, D. N. (1997). Some applications of generalized FFTs. Proceedings of the DIMACS workshop on groups and computation.
14. Schomburg, I., Chang, A., Ebeling, C., Gremse, M., Heldt, C., Huhn, G., & Schomburg, D. (2004). Brenda, the enzyme database: updates and major new developments. Nucleic Acids Research, 32D, 431-433.
15. Serre, J.-P. (1977). Linear representations of finite groups, vol. 42 of Graduate Texts in Mathamatics. Springer-Verlag.
16. Shervashidze, N., Vishwanathan, S. V. N., Petri, T., Mehlhorn, K., & Borgwardt, K. (2009). Efficient graphlet kernels for large graph comparison. Proceedings of International Conference on Artificial Intelligence and Statistics.
17. Tsuda, K. (2007). Entire regularization paths for graph data. Proc. Intl. Conf. Machine Learning (pp. 919-926).
18. Wale, N., & Karypis, G. (2006). Comparison of descriptor spaces for chemical compound retrieval and classification. Proc. Intl. Conf. Data Mining (pp. 678-689).