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Communication dans un congrès

Optimization of the Diffusion Time in Graph Diffused-Wasserstein Distances: Application to Domain Adaptation

Amélie Barbe 1 Paulo Gonçalves 1 Marc Sebban 2 Pierre Borgnat 3 Rémi Gribonval 1 Titouan Vayer 1
1 DANTE - Dynamic Networks : Temporal and Structural Capture Approach
Inria Grenoble - Rhône-Alpes, LIP - Laboratoire de l'Informatique du Parallélisme, IXXI - Institut Rhône-Alpin des systèmes complexes
Abstract : The use of the heat kernel on graphs has recently given rise to a family of so-called Diffusion-Wasserstein distances which resort to the Optimal Transport theory for comparing attributed graphs. In this paper, we address the open problem of optimizing the diffusion time used in these distances and which plays a key role in several machine learning settings, including graph domain adaptation or graph classification. Inspired from the notion of triplet-based constraints used, e.g., in metric learning, we design a loss function that aims at bringing two graphs closer together while keeping an impostor away, this latter taking the form of a Wasserstein barycenter. After a thorough analysis of the properties of this function, we show on synthetic and real-world data that the resulting Diffusion-Wasserstein distances outperforms the Gromov and Fused-Gromov Wasserstein distances on unsupervised graph domain adaptation tasks. Additionally, we give evidence in such a setting that our method for optimizing the diffusion parameter allows to overcome the limitation of the widely used circular validation strategy.
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Communication dans un congrès
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Contributeur : Paulo Gonçalves Connectez-vous pour contacter le contributeur
Soumis le : vendredi 24 septembre 2021 - 11:05:04
Dernière modification le : mercredi 6 octobre 2021 - 15:05:30


ICTAI-2021(long version).pdf
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  • HAL Id : hal-03353622, version 1


Amélie Barbe, Paulo Gonçalves, Marc Sebban, Pierre Borgnat, Rémi Gribonval, et al.. Optimization of the Diffusion Time in Graph Diffused-Wasserstein Distances: Application to Domain Adaptation. ICTAI 2021 - 33rd IEEE International Conference on Tools with Artificial Intelligence, Nov 2021, Virtual conference, France. pp.1-8. ⟨hal-03353622⟩



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