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

2024-02-19Prediction

Abstract

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).

Results

Task	Dataset	Metric	Value	Model
Prediction	QM9	Edit Distance	2.13	ANY2GRAPH + FD
Prediction	QM9	Edit Distance	2.84	FGWBARY-ILE + FD
Prediction	QM9	Edit Distance	3.44	ANY2GRAPH
Prediction	QM9	Edit Distance	9.15	RELATIONFORMER

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