J. Yan, W. Xu-cheng-yin, C. Lin, H. Deng, X. Zha et al., A short survey of recent advances in graph matching, vol.06, pp.167-174, 2016.

M. Zaslavskiy, F. Bach, and J. Vert, Manyto-many graph matching: A continuous relaxation approach, Machine Learning and Knowledge Discovery in Databases, pp.515-530, 2010.

G. Peyré, M. Cuturi, and J. Solomon, GromovWasserstein Averaging of Kernel and Distance Matrices, Proc. 33rd International Conference on Machine Learning, 2016.

F. Mémoli, Gromov-wasserstein distances and the metric approach to object matching, Foundations of Computational Mathematics, vol.11, issue.4, pp.417-487, 2011.

E. Van-obberghen-schilling, R. P. Tucker, F. Saupe, I. Gasser, B. Cseh et al., Fibronectin and tenascin-c: Accomplices in vascular morphogenesis during development and tumor growth, Int J Dev Biol, vol.55, pp.511-536, 2011.
URL : https://hal.archives-ouvertes.fr/hal-00638899

Y. Rubner, C. Tomasi, and L. J. Guibas, The earth mover's distance as a metric for image retrieval, International Journal of Computer Vision, vol.40, issue.2, pp.99-121, 2000.

T. Vayer, L. Chapel, R. Flamary, R. Tavenard, and N. Courty, Optimal Transport for structured data with application on graphs, 2018.
URL : https://hal.archives-ouvertes.fr/hal-02174322

D. Alvarez-melis, T. S. Jaakkola, and S. Jegelka, , 2017.

N. Courty and R. Flamary, Devis Tuia, and Alain Rakotomamonjy, Optimal Transport for Domain Adaptation. arXiv e-prints, 2015.