🚀 Improved Parallel Algorithm for Finding Minimum Cuts in Stochastic Flow Networks

This repository provides all the necessary code and resources to reproduce the results presented in the manuscript: "Efficient Minimum Cut Detection in Stochastic Networks."

The research introduces the Dynamic Parallel Graph Cutting Algorithm (DPGCA) and includes comparative analyses against traditional minimum cut algorithms.

🔧 Setup & Usage

1. Download the Project Files

Clone or download the repository to your local machine.

Using Git:

git clone https://github.com/bluetuka/Merging-Method-Review.git
cd Merging-Method-Review

Or click Download ZIP, extract it, then open the extracted folder.

2. Install Python & Required Libraries

Make sure you have Python 3.x installed. Install the necessary libraries:

pip install numpy matplotlib pandas psutil

3. Run the Main Script

python main.py

This will execute the project and generate outputs (plots, logs, etc.) in the console or in designated output files.

🛠 Troubleshooting

• Python version: Confirm you’re using Python 3.x:

python --version

• Running in virtual environment (recommended):

python -m venv venv
source venv/bin/activate  # Linux/macOS
venv\Scripts\activate     # Windows
pip install -r requirements.txt

Citation

If you use this repository, please cite:

@article{mohammad2024,
  title={Improved Parallel Algorithm for Finding Minimum Cuts in Stochastic Flow Networks},Add commentMore actions
  author={Mohammad, Joshan},
  journal={IEEE Latin America},
  year={2024}
}

ContactAdd commentMore actions

For questions or collaborations, feel free to reach out: mohammad@estudante.ufscar.br