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Found 5 papers, 3 papers with code
Discrete Latent Graph Generative Modeling with Diffusion Bridges
Learning graph generative models over latent spaces has received less attention compared to models that operate on the original data space and has so far demonstrated lacklustre performance.
Discrete Graph Auto-Encoder
Despite advances in generative methods, accurately modeling the distribution of graphs remains a challenging task primarily because of the absence of predefined or inherent unique graph representation.
GrannGAN: Graph annotation generative adversarial networks
The model we propose tackles the problem of generating the data features constrained by the specific graph structure of each data point by splitting the task into two phases.
Permutation Equivariant Generative Adversarial Networks for Graphs
One solution consists of using equivariant generative functions, which ensure the ordering invariance.
Adversarial Regression. Generative Adversarial Networks for Non-Linear Regression: Theory and Assessment
By sampling $z$, we can therefore obtain samples following approximately $p(x|y)$, which is the predictive distribution of $x$ for a new $y$.
