References

1. K. Borgwardt, Graph Kernels, PhD thesis, Faculty for Mathematics, Informatics and Statistics, Ludwig-Maximilians-University Munich, Germany, 2007.

2. D. Bonchev, D. Rouvray, Chemical Graph Theory. Introduction and Fundamentals, Taylor & Francis, London, 1991.

3. J. Gasteiger, T. Engel, Chemoinformatics, Wiley-VCH, Weinheim, 2003.

4. R. Sharan, T. Ideker, Modeling cellular machinery through biological network comparison. Nat. Biotechnol. 24(4), 427–433 (2006).

5. S. Wasserman, K. Faust, Social network analysis, in Structural Analysis in the Social Sciences, Vol. 8, Cambridge University Press, 1995.

6. R. Kumar, J. Novak, A. Tomkins, Structure and evolution of online social networks, in Proceedings of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD 2006), Philadelphia, USA, August 20–23, pp. 611–617, 2006.

7. Z. Harchaoui, F. Bach, Image classification with segmentation graph kernels, in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2007), Minneapolis, Minnesota, USA, June 18–23, IEEE Computer Society, 2007.

8. M. Collins, N. Duffy, Convolution kernels for natural language, in (T. Dietterich, S. Becker, Z. Ghahramani, eds.), Advances in Neural Information Processing Systems 14 (NIPS 2001), Vancouver, British Columbia, Canada, December 8–3, MIT Press, pp. 625–632, 2002.

9. F. Emmert-Streib, M. Dehmer, Networks for systems biology: conceptual connection of data and function, ...

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