Dec 4, 2022 · Experimental results also show that GraphGDP can generate high-quality graphs in only 24 function evaluations, much faster than previous ...

GraphGDP: Generative Diffusion Processes for Permutation Invariant Graph Generation. Official Code Repository for GraphGDP (ICDM 2022).

Graph generative models aim to capture the underlying distributions over a particular family of graphs and generate diverse novel graphs with high fidelity, ...

Experimental results also show that GraphGDP can generate high-quality graphs in only 24 function evaluations, much faster than previous autoregressive models.

Under the evaluation of comprehensive metrics, the proposed generative diffusion process achieves competitive performance in graph distribution learning and ...

GraphGDP: Generative Diffusion Processes for Permutation Invariant Graph Generation. record by Han Huang • GraphGDP: Generative Diffusion Processes for ...

The core concept involves a process of gradually adding noise to training data and subsequently removing it to generate new samples.

This repository serves as a comprehensive collection of resources related to diffusion models applied to graphs.

Graphgdp: Generative diffusion processes for permutation invariant graph generation · PriSTI: A Conditional Diffusion Framework for Spatiotemporal Imputation.

Graphgdp: Generative diffu- sion processes for permutation invariant graph generation. In IEEE ICDM, pages 201–210, 2022. [Huang et al., 2022b] Lei Huang ...