1. 1. R.R. Dipert, The mathematical structure of the world: The world as graph, J. Philos. 94 (7) (1997) 329–358.
   2. H. Bunke, Graph-based tools for data mining and machine learning, in: International Workshop on Machine Learning and Data Mining in Pattern Recognition, Springer, 2003, pp. 7–19.
   3. D. Conte, P. Foggia, C. Sansone, M. Vento, Thirty years of graph matching in pattern recognition, Int. J. Pattern Recognit. Artif. Intell. 18 (03) (2004) 265– 298.
   4. T. Gärtner, A survey of kernels for structured data, ACM SIGKDD Explor. Newslett. 5 (1) (2003) 49–58.
   5. S.V.N. Vishwanathan, N.N. Schraudolph, R. Kondor, K.M. Borgwardt, Graph kernels, J. Mach. Learn. Res. 11 (Apr) (2010) 1201–1242.
   6. N. Shervashidze, Scalable Graph Kernels (Ph.D. thesis), Universität Tübingen, 2012.
   7. K.M. Borgwardt, Graph Kernels (Ph.D. thesis), lmu, 2007.
   8. http://dx.doi.org/10.1198/jasa.2003.s269
   9. N. Aronszajn, Theory of reproducing kernels, Trans. Amer. Math. Soc. 68 (3) (1950) 337–404.
   10. B. Scholkopf, A.J. Smola, Learning With Kernels: Support Vector Machines, Regularization, Optimization, and Beyond, 2002, arXiv: arXiv:1011.1669v3, http://dx.doi.org/10.1198/jasa.2003.s269.
   11. I. Tsochantaridis, T. Joachims, T. Hofmann, Y. Altun, A.-C. Org, Large margin methods for structured and interdependent output variables, J. Mach. Learn. Res. 6 (2005) 1453–1484. http://dx.doi.org/10.1007/s10994-008- 5071-9.