Hub Laplacian Operators for Directional GNNs

This is the GitHub repository for our CS4350 project, "Hub Laplacian Operators for Directional GNNs."

📄 Full Report

The complete project report, Report_GNN_project.pdf, can be found here.

🚀 Running the Models

This repository contains implementations of two models: our custom Graph Convolutional Neural Network (GCNN) and a modified version of Graph Attention Diffusion (GAD), whose original implementation can be found here.

1. Graph Convolutional Neural Network (GCNN)

To run the implemented GCNN:

1. Navigate to the GCNN/ directory.
2. Open main.py and modify the script to choose your desired hyperparameters.
3. Execute the script.

2. Modified Graph Attention Diffusion (GAD)

To run the modified GAD model:

1. Go to GAD/experiments/QM9/.
2. Refer to the README.md file within this directory for detailed instructions and ready-to-use sample commands.

3. Dataset spectral analysis + F matrices cosine similarity

To run the spectral analysis of the dataset (or a subset of it) and obtain the cosine similarity between the different F matrices corresponding to each Hub Operator:

1. Go to GAD/experiments/QM9/.
2. Modify the hyperparameters in dataset/evaluation.py and run.