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Found 2 papers, 1 papers with code
Micro and Macro Level Graph Modeling for Graph Variational Auto-Encoders
We utilize the micro-macro objective to improve graph generation with a GraphVAE, a well-established model based on graph-level latent variables, that provides fast training and generation time for medium-sized graphs.
Generating the Graph Gestalt: Kernel-Regularized Graph Representation Learning
Our experiments demonstrate a significant improvement in the realism of the generated graph structures, typically by 1-2 orders of magnitude of graph structure metrics, compared to leading graph VAEand GAN models.
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