J. Atwood and D. Towsley, Diffusion-convolutional neural networks, Advances in Neural Information Processing Systems 29, 2001.

K. M. Borgwardt, C. S. Ong, S. Schönauer, S. V. Vishwanathan, A. J. Smola et al., Protein function prediction via graph kernels, Bioinformatics, vol.21, issue.Suppl 1, pp.47-56, 1007.
DOI : 10.1093/bioinformatics/bti1007
URL : https://academic.oup.com/bioinformatics/article-pdf/21/suppl_1/i47/524364/bti1007.pdf

S. Bougleux, L. Brun, V. Carletti, P. Foggia, B. Gazre et al., Graph edit distance as a quadratic assignment problem, Pattern Recognition Letters, vol.87, pp.38-46, 2017.
DOI : 10.1016/j.patrec.2016.10.001
URL : https://hal.archives-ouvertes.fr/hal-01613964

M. M. Bronstein, J. Bruna, Y. Lecun, A. Szlam, and P. Vandergheynst, Geometric Deep Learning: Going beyond Euclidean data, IEEE Signal Processing Magazine, vol.34, issue.4, pp.18-42, 2017.
DOI : 10.1109/MSP.2017.2693418
URL : http://arxiv.org/pdf/1611.08097

J. Bruna, W. Zaremba, A. Szlam, and Y. Lecun, Spectral networks and deep locally connected networks on graphs, Tech. rep, 2014.

H. Bunke, X. Jiang, and A. Kandel, On the minimum common supergraph of two graphs, Computing, vol.65, issue.1, pp.13-25, 2000.
DOI : 10.1109/icip.2003.1246607
URL : https://zenodo.org/record/1270845/files/article.pdf

´. E. Daller, S. Bougleux, B. Gaüzère, and L. Brun, Approximate Graph Edit Distance by Several Local Searches in Parallel, Proceedings of the 7th International Conference on Pattern Recognition Applications and Methods, 2018.
DOI : 10.5220/0006599901490158
URL : https://hal.archives-ouvertes.fr/hal-01664529

A. Debnath, L. Lopez-de-compadre, R. Debnath, G. Shusterman, A. Hansch et al., Structure-activity relationship of mutagenic aromatic and heteroaromatic nitro compounds. Correlation with molecular orbital energies and hydrophobicity, Journal of Medicinal Chemistry, vol.34, issue.2, pp.786-797, 1991.
DOI : 10.1021/jm00106a046

M. Defferrard, X. Bresson, and P. Vandergheynst, Convolutional neural networks on graphs with fast localized spectral filtering, Advances in Neural Information Processing Systems 29, pp.3844-3852, 2016.

D. Duvenaud, D. Maclaurin, J. Aguilera-iparraguirre, R. Gómez-bombarelli, T. Hirzel et al., Convolutional networks on graphs for learning molecular fingerprints. In: Advances in Neural Information Processing Systems 28, pp.2224-2232, 2015.

D. P. Kingma and J. Ba, Adam: A method for stochastic optimization, p.6980, 2014.

T. Kipf and M. Welling, Semi-supervised classification with graph convolutional networks, International Conference on Learning Representations, 2017.

Y. Lecun and Y. Bengio, The handbook of brain theory and neural networks. chap, Convolutional Networks for Images, Speech, and Time Series, pp.255-258, 1998.

K. Riesen and H. Bunke, Approximate graph edit distance computation by means of bipartite graph matching, Image and Vision Computing, vol.27, issue.7, 2009.
DOI : 10.1016/j.imavis.2008.04.004

M. Simonovsky and N. Komodakis, Dynamic Edge-Conditioned Filters in Convolutional Neural Networks on Graphs, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017.
DOI : 10.1109/CVPR.2017.11
URL : https://hal.archives-ouvertes.fr/hal-01576919

H. Toivonen, A. Srinivasan, R. King, S. Kramer, and C. Helma, Statistical evaluation of the predictive toxicology challenge, Bioinformatics, vol.19, pp.1179-1182, 2000.

N. Verma, E. Boyer, and J. Verbeek, FeaStNet: Feature-Steered Graph Convolutions for 3D Shape Analysis, IEEE Conference on Computer Vision and Pattern Recognition, 2018.
URL : https://hal.archives-ouvertes.fr/hal-01540389

N. Wale, I. A. Watson, and G. Karypis, Comparison of descriptor spaces for chemical compound retrieval and classification, Knowledge and Information Systems, vol.38, issue.6, pp.347-37539, 2006.
DOI : 10.1007/s10115-007-0103-5