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Found 5 papers, 5 papers with code
Sparse Training of Discrete Diffusion Models for Graph Generation
In this work, we introduce SparseDiff, a denoising diffusion model for graph generation that is able to exploit sparsity during its training phase.
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.
