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, 2005.
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. Gaüzère 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, 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

É. 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, R. L. Lopez-de-compadre, G. Debnath, A. 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, 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, Proceedings of the 28th International Conference on Neural Information Processing Systems, pp.2224-2232, 2015.

J. Edmonds, Optimum branchings, Journal of Research of the National Bureau of Standards Section B Mathematics and Mathematical Physics, vol.71, issue.4, pp.233-240, 1966.
DOI : 10.6028/jres.071B.032
URL : http://doi.org/10.6028/jres.071b.032

P. Diederik, J. Kingma, and . Ba, Adam : A method for stochastic optimization. CoRR, abs/1412 [13] Thomas Kipf and Max Welling. Semi-supervised classification with graph convolutional networks, Proceedings of ICLR'17, 2014.

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

M. Niepert, M. Ahmed, and K. Kutzkov, Learning convolutional neural networks for graphs, Proceedings of the 33rd International Conference on International Conference on Machine Learning, pp.2014-2023, 2016.

K. Riesen and H. Bunke, Approximate graph edit distance computation by means of bipartite graph matching, Image and Vision Computing, vol.27, issue.7, pp.950-959, 2007.
DOI : 10.1016/j.imavis.2008.04.004

A. Sankar, X. Zhang, and K. Chang, Motif-based convolutional neural network on graphs, 2017.

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, . Srinivasan, . King, C. Kramer, and . Helma, Statistical evaluation of the predictive toxicology challenge, 2018.
DOI : 10.1093/bioinformatics/btg130
URL : https://academic.oup.com/bioinformatics/article-pdf/19/10/1183/448860/btg130.pdf

N. Verma, E. Boyer, and J. Verbeek, Dynamic Filters in Graph Convolutional Networks. working paper or preprint, 2017.

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.347-375, 2008.
DOI : 10.1007/s10115-007-0103-5
URL : http://glaros.dtc.umn.edu/gkhome/fetch/papers/dscKAIS07.pdf