GraphSpanner Neural Network Project 🌳

Overview

The GraphSpanner Neural Network Project is dedicated to creating a machine learning model that generates minimal spanning trees for graphs with up to 20 nodes. Leveraging the synergy of graph theory and neural networks, this project offers a versatile solution for small-scale spanning tree generation.

Project Structure

Files

• Create_MST_Training_Data.py: Script to create training data to train the model.
• Train_MST_Model.py: Script for training the neural network model.
• Run.py: Script for run the created model.

Dependencies

These libraries are fundamental to the functionality and development of the GraphSpanner Neural Network Project:

• Python 3.x: The programming language used for development.
• NetworkX: A Python library for creating, analyzing, and visualizing complex networks.
• NumPy: A powerful library for numerical operations, essential for data manipulation.
• Matplotlib: A plotting library for creating visualizations, including graph visualizations.
• Joblib: Used for efficient parallel computing and data caching, contributing to improved performance.
• Scikit-learn: Provides tools for machine learning, including model selection and data preprocessing.
• TensorFlow: An open-source machine learning framework used for building and training neural network models.
• SciPy: Used for scientific and technical computing, particularly for the minimum spanning tree calculation.

Pre-trained Model

A pre-trained H5 model named mst_prediction_model.h5 is available for use.

Model Visualization

Example Output

To demonstrate the functionality of the GraphSpanner Neural Network, follow these steps:

1. Clone the Repository:

   git clone https://github.com/madfox99/GraphSpanner.git

2. Install Dependencies:

   pip install -r requirements.txt

3. Run the Example Script:

   python Run.py

4. Enter Graph Input: (Example)

   1-2=>3, 1-3=>1, 1-4=>2, 1-5=>4,2-3=>5, 2-4=>6, 2-5=>7,3-4=>8, 3-5=>9,4-5=>10

5. View Results: The script will visualize the original graph and the predicted minimum spanning tree.