GAUDI

By Mirja Granfors, Jesús Pineda, Blanca Zufiria Gerbolés, Joana B. Pereira, Carlo Manzo, and Giovanni Volpe.

GAUDI is an unsupervised geometric deep learning framework for analyzing complex graph-structured data. GAUDI's hourglass architecture, with multiple hierarchical pooling and upsampling steps, maps graphs into a structured latent space, capturing their underlying parameters.

This repository contains an implementation of GAUDI, introduced in Global graph features unveiled by unsupervised geometric deep learning.

Getting started

An example of how GAUDI is trained on Watts-Strogatz small-world graphs can be found here:

Training GAUDI on Watts-Strogatz Small-World Graphs

To run the example, first download or clone this GitHub repository, then run the notebook from within the repository folder.

Running the example takes about 5 minutes on a standard laptop.

Dependencies

To use this implementation, ensure you have the following dependencies installed:

• deeplay (tested with 0.1.3)
• torch (tested with 2.6.0 and 2.7.0)

You can install them using:

pip install deeplay

pip install torch

Additional dependencies for the example

If you also want to run the provided Watts-Strogatz example, you’ll need these extra packages:

• PyGSP (tested with 0.5.1)
• networkx (tested with 3.4.2)
• torch-geometric(tested with 2.5.2, 2.6.1)

You can install them using:

pip install PyGSP

pip install networkx

pip install torch-geometric