124 citations · 264 across the 8 of their papers we have counts for
10 papers
Generative Modelling of Structurally Constrained Graphs
Manuel Madeira, Clement Vignac, Dorina Thanou +1
Graph diffusion models have emerged as state-of-the-art techniques in graph generation; yet, integrating domain knowledge into these models remains challenging. Domain knowledge is…
Sparse Training of Discrete Diffusion Models for Graph Generation
Yiming Qin, Clement Vignac, Pascal Frossard
Generative graph models struggle to scale due to the need to predict the existence or type of edges between all node pairs. To address the resulting quadratic complexity, existing…
MiDi: Mixed Graph and 3D Denoising Diffusion for Molecule Generation
Clement Vignac, Nagham Osman, Laura Toni +1
This work introduces MiDi, a novel diffusion model for jointly generating molecular graphs and their corresponding 3D arrangement of atoms. Unlike existing methods that rely on pre…
Equivariant 3D-Conditional Diffusion Models for Molecular Linker Design
Ilia Igashov, Hannes Stärk, Clément Vignac +5
Fragment-based drug discovery has been an effective paradigm in early-stage drug development. An open challenge in this area is designing linkers between disconnected molecular fra…
DiGress: Discrete Denoising diffusion for graph generation
Clement Vignac, Igor Krawczuk, Antoine Siraudin +3
This work introduces DiGress, a discrete denoising diffusion model for generating graphs with categorical node and edge attributes. Our model utilizes a discrete diffusion process…
Equivariant Diffusion for Molecule Generation in 3D
Emiel Hoogeboom, Victor Garcia Satorras, Clément Vignac +1
This work introduces a diffusion model for molecule generation in 3D that is equivariant to Euclidean transformations. Our E(3) Equivariant Diffusion Model (EDM) learns to denoise…
Top-N: Equivariant set and graph generation without exchangeability
Clement Vignac, Pascal Frossard
This work addresses one-shot set and graph generation, and, more specifically, the parametrization of probabilistic decoders that map a vector-shaped prior to a distribution over s…
Modurec: Recommender Systems with Feature and Time Modulation
Javier Maroto, Clément Vignac, Pascal Frossard
Current state of the art algorithms for recommender systems are mainly based on collaborative filtering, which exploits user ratings to discover latent factors in the data. These a…
Building powerful and equivariant graph neural networks with structural message-passing
Clement Vignac, Andreas Loukas, Pascal Frossard
Message-passing has proved to be an effective way to design graph neural networks, as it is able to leverage both permutation equivariance and an inductive bias towards learning lo…
On the choice of graph neural network architectures
Clément Vignac, Guillermo Ortiz-Jiménez, Pascal Frossard
Seminal works on graph neural networks have primarily targeted semi-supervised node classification problems with few observed labels and high-dimensional signals. With the developm…