Any2Graph: Deep End-To-End Supervised Graph Prediction With An Optimal Transport Loss
<|reference_start|>Any2Graph: Deep End-To-End Supervised Graph Prediction With An Optimal Transport Loss: 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).<|reference_end|>
@article{krzakala2024any2graph:,
    title={Any2Graph: Deep End-To-End Supervised Graph Prediction With An Optimal
  Transport Loss},
    author={Paul Krzakala, Junjie Yang, R'emi Flamary, Florence d'Alch'e-Buc,
  Charlotte Laclau, Matthieu Labeau},
    journal={arXiv preprint arXiv:2402.12269},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.12269},
    primaryClass={cs.LG}
}