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Found 5 papers, 5 papers with code
Sparse Training of Discrete Diffusion Models for Graph Generation
Generative graph models struggle to scale due to the need to predict the existence or type of edges between all node pairs.
MiDi: Mixed Graph and 3D Denoising Diffusion for Molecule Generation
This work introduces MiDi, a novel diffusion model for jointly generating molecular graphs and their corresponding 3D arrangement of atoms.
DiGress: Discrete Denoising diffusion for graph generation
This work introduces DiGress, a discrete denoising diffusion model for generating graphs with categorical node and edge attributes.
Top-N: Equivariant set and graph generation without exchangeability
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 sets or graphs.
Building powerful and equivariant graph neural networks with structural message-passing
We address this problem and propose a powerful and equivariant message-passing framework based on two ideas: first, we propagate a one-hot encoding of the nodes, in addition to the features, in order to learn a local context matrix around each node.
