GRAPH-0/GraphGDP
Implementation for the paper: GraphGDP: Generative Diffusion Processes for Permutation Invariant Graph Generation
This project offers an implementation for generating new graph structures based on existing ones. It takes a configuration describing graph properties and outputs synthetically generated graphs. This is primarily a tool for researchers and machine learning engineers working on graph generation tasks, providing code to reproduce the methods from the GraphGDP paper.
No commits in the last 6 months.
Use this if you are a researcher or machine learning engineer looking to implement or evaluate advanced graph generation models based on diffusion processes.
Not ideal if you need an out-of-the-box solution for graph generation without deep technical involvement in model configuration and training.
Stars
32
Forks
8
Language
Python
License
MIT
Category
Last pushed
Dec 10, 2022
Commits (30d)
0
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