This project combines Graph Neural Networks (GNNs) with Large Language Models (LLMs) to explore advanced machine learning techniques for analyzing and processing structured and unstructured data.
- Integration of GNNs and LLMs: Develop a unified approach to harness the power of both models.
- Flexible Framework: Adaptable to various datasets and applications.
- Graph Analysis: Leverage GNNs for structured data insights.
- Text Processing: Utilize LLMs for advanced natural language understanding.
- End-to-End Pipeline: Comprehensive processing from data loading to model evaluation.
To run this project, ensure the following dependencies are installed:
numpypandasmatplotlibnetworkxtorch(PyTorch)transformers(Hugging Face Transformers)scikit-learn
Install the dependencies using:
pip install -r requirements.txt- Clone the Repository:
git clone https://github.com/OlfaHal/Master-Thesis.git
cd Master-Thesis- Set Up the Environment: Create a virtual environment and install the required packages.
python -m venv venv
source venv/bin/activate # On Windows: venv\\Scripts\\activate
pip install -r requirements.txt- Open the Notebook: Launch the Jupyter Notebook.
jupyter notebook "GNN+LLM.ipynb"- Run the Cells: Execute the notebook cells sequentially to reproduce the results.