caio-freitas/GraphARM
An implementation of the Autoregressive Diffusion Model for Graph Generation from [Kong et al. 2023]
This project helps researchers and data scientists working with complex network structures to create new synthetic graphs that share characteristics with existing ones. You provide a set of example graphs, and it learns their underlying patterns to generate novel graphs that can be used for simulations or further analysis. This is valuable for anyone studying systems represented as graphs, such as social networks, molecular structures, or transportation networks.
Use this if you need to generate realistic, new graph datasets for research, simulations, or as augmented data when real-world graph data is scarce or sensitive.
Not ideal if you need to analyze or transform existing graphs, or if your primary goal is simple visualization or query of graph data rather than generation.
Stars
44
Forks
11
Language
Python
License
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Category
Last pushed
Mar 16, 2026
Commits (30d)
0
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