  We propose Any2graph, a generic framework for end-to-end Supervised Graph
Prediction (SGP) i.e. a deep learning model that predicts an entire graph for
any kind of input. The framework is built on a novel Optimal Transport loss,
the Partially-Masked Fused Gromov-Wasserstein, that exhibits all necessary
properties (permutation invariance, differentiability and scalability) and is
designed to handle any-sized graphs. Numerical experiments showcase the
versatility of the approach that outperform existing competitors on a novel
challenging synthetic dataset and a variety of real-world tasks such as map
construction from satellite image (Sat2Graph) or molecule prediction from
fingerprint (Fingerprint2Graph).
