K. Bajema and R. Merlin, Raman scattering by acoustic phonons in fibonacci gaas-aias superlattices, Physical Review B, vol.36, p.4555, 1987.

K. M. Borgwardt and H. P. Kriegel, Shortest-path kernels on graphs, Fifth IEEE International Conference on, IEEE, p.8, 2005.

K. M. Borgwardt, C. S. Ong, S. Schönauer, S. Vishwanathan, A. J. Smola et al., Protein function prediction via graph kernels, Bioinformatics, vol.21, pp.47-56, 2005.

B. E. Boser, I. M. Guyon, and V. N. Vapnik, A training algorithm for optimal margin classifiers, Proceedings of the fifth annual workshop on Computational learning theory, ACM, pp.144-152, 1992.

L. Brun, Greyc chemistry dataset, 2018.

D. Cherqaoui and D. Villemin, Use of a neural network to determine the boiling point of alkanes, Journal of the Chemical Society, vol.90, pp.97-102, 1994.

D. Cherqaoui, D. Villemin, A. Mesbah, J. M. Cense, and V. Kvasnicka, Use of a neural network to determine the normal boiling points of acyclic ethers, peroxides, acetals and their sulfur analogues, Journal of the Chemical Society, vol.90, pp.2015-2019, 1994.

D. Conte, P. Foggia, C. Sansone, and M. Vento, Thirty years of graph matching in pattern recognition, International journal of pattern recognition and artificial intelligence, vol.18, pp.265-298, 2004.
URL : https://hal.archives-ouvertes.fr/hal-01408706

C. Cortes, P. Haffner, and M. Mohri, Rational kernels: Theory and algorithms, Journal of Machine Learning Research, vol.5, pp.1035-1062, 2004.

A. K. Debnath, R. L. Lopez-de-compadre, G. Debnath, A. J. Shusterman, and C. Hansch, Structure-activity relationship of mutagenic aromatic and heteroaromatic nitro compounds. correlation with molecular orbital energies and hydrophobicity, Journal of medicinal chemistry, vol.34, pp.786-797, 1991.

E. W. Dijkstra, A note on two problems in connexion with graphs, Numerische mathematik, vol.1, pp.269-271, 1959.

R. W. Floyd, Algorithm 97: shortest path, Communications of the ACM, vol.5, pp.490-499, 1960.

T. Gärtner, P. Flach, and S. Wrobel, On graph kernels: Hardness results and efficient alternatives. Learning Theory and Kernel Machines, pp.129-143, 2003.

B. Gaüzere, L. Brun, and D. Villemin, Two new graphs kernels in chemoinformatics, Pattern Recognition Letters, vol.33, pp.2038-2047, 2012.

D. Haussler, Convolution kernels on discrete structures, 1999.

P. Honeine, Z. Noumir, and C. Richard, Multiclass classification machines with the complexity of a single binary classifier, Signal Processing, vol.93, pp.1013-1026, 2013.
URL : https://hal.archives-ouvertes.fr/hal-01965575

M. A. Johnson and G. M. Maggiora, Concepts and applications of molecular similarity, 1990.

H. Kashima, K. Tsuda, and A. Inokuchi, Marginalized kernels between labeled graphs, Proceedings of the 20th international conference on machine learning (ICML-03), pp.321-328, 2003.

J. Kobler, U. Schöning, and J. Torán, The graph isomorphism problem: its structural complexity, 2012.

P. Mahé, N. Ueda, T. Akutsu, J. L. Perret, and J. P. Vert, Extensions of marginalized graph kernels, Proceedings of the twenty-first international conference on Machine learning, p.70, 2004.

B. Mercer, Xvi. functions of positive and negative type, and their connection the theory of integral equations, Phil. Trans. R. Soc. Lond. A, vol.209, pp.415-446, 1909.

K. P. Murphy, Machine Learning: A Probabilistic Perspective, 2012.

R. M. Murray, Python Control Systems Library, 2018.

L. Ralaivola, S. J. Swamidass, H. Saigo, and P. Baldi, Graph kernels for chemical informatics, Neural networks, vol.18, pp.1093-1110, 2005.
DOI : 10.1016/j.neunet.2005.07.009

K. Riesen and H. Bunke, Iam graph database repository for graph based pattern recognition and machine learning, Joint IAPR International Workshops on Statistical Techniques in Pattern Recognition (SPR) and Structural and Syntactic Pattern Recognition (SSPR), pp.287-297, 2008.
DOI : 10.1007/978-3-540-89689-0_33
URL : https://link.springer.com/content/pdf/10.1007%2F978-3-540-89689-0_33.pdf

K. Riesen, M. Neuhaus, and H. Bunke, Graph embedding in vector spaces by means of prototype selection, in: International Workshop on Graph-Based Representations in Pattern Recognition, pp.383-393, 2007.

B. Schölkopf, R. Herbrich, and A. J. Smola, A generalized representer theorem, Proc. 14th Annual Conference on Computational Learning Theory and 5th European Conference on Computational Learning Theory, pp.416-426, 2001.

B. Schölkopf and A. J. Smola, Learning with kernels: support vector machines, regularization, optimization, and beyond, 2002.

I. Schomburg, A. Chang, C. Ebeling, M. Gremse, C. Heldt et al., Brenda, the enzyme database: updates and major new developments, Nucleic acids research, vol.32, pp.431-433, 2004.
DOI : 10.1093/nar/gkh081
URL : https://academic.oup.com/nar/article-pdf/32/suppl_1/D431/7621720/gkh081.pdf

J. Shawe-taylor and N. Cristianini, Kernel methods for pattern analysis, 2004.

F. Suard, A. Rakotomamonjy, and A. Bensrhair, Kernel on bag of paths for measuring similarity of shapes, pp.355-360, 2007.

M. Sugiyama and K. Borgwardt, Halting in random walk kernels, Advances in neural information processing systems, pp.1639-1647, 2015.

S. V. Vishwanathan, N. N. Schraudolph, R. Kondor, and K. M. Borgwardt, Graph kernels, Journal of Machine Learning Research, vol.11, pp.1201-1242, 2010.

D. B. West, Introduction to graph theory, vol.2, 2001.

L. Xu, W. Wang, M. Alvarez, J. Cavazos, and D. Zhang, Parallelization of shortest path graph kernels on multi-core cpus and gpus, Proceedings of the Programmability Issues for Heterogeneous Multicores (MultiProg), 2014.