This repository contains the code supporting the research presented in the report.
In this work, we explore methods to sparsify graph-based datasets for efficient training and inference in graph neural networks (GNNs) without compromising performance. We propose an improved algorithm for finding graph lottery tickets that yield competitive results with fewer edges.
To set up and run the code, ensure you have the following installed:
- Python 3.13
- Libraries: PyTorch and other dependencies listed in
requirements.txt
- Clone the repository:
git clone https://github.com/YBaumann/Python_Graphs_Test.git cd Python_Graphs_Test - Install requirements:
pip install -r requirements.txt
- Create a new folder:
mkdir output_csv/