@inproceedings{
krzakala2024anygraph,
title={Any2Graph: Deep End-To-End Supervised Graph Prediction With An Optimal Transport Loss},
author={Paul KRZAKALA and Junjie Yang and R{\'e}mi Flamary and Florence d'Alch{\'e}-Buc and Charlotte Laclau and Matthieu Labeau},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=tPgagXpvcV}
}
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).
Any2Graph: Deep End-To-End Supervised Graph Prediction With An Optimal Transport Loss
Paul KRZAKALA Junjie Yang Rémi Flamary Florence d'Alché-Buc Charlotte Laclau Matthieu Labeau