This Python tool is designed to perform social network analysis on a dataset of job titles and skills. It constructs a graph representing the relationships between job titles and skills, calculates degree centrality and clustering coefficients for each node, and visualizes the graph for better understanding.
- Graph Construction: Construct a graph where nodes represent job titles and skills, and edges represent the relationship between them.
- Degree Centrality Calculation: Calculate the degree centrality for each node in the graph, representing the importance of a node based on the number of connections.
- Clustering Coefficients Calculation: Calculate the clustering coefficient for each node, representing the degree to which nodes in a graph tend to cluster together.
- Graph Visualization: Visualize the constructed graph using matplotlib for better understanding and analysis.
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Install Dependencies: Make sure you have Python installed on your system. Install the required dependencies using pip: pip install -r requirements.txt
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Prepare Dataset: Prepare your dataset in CSV format. Each row should contain a job title and a list of associated skills separated by commas.
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Run the Tool: Run the
main.pyscript and provide the path to your dataset as input:
python main.py --dataset <path_to_dataset.csv>
- Interpret Results: After running the tool, you will see visualizations of the constructed graph along with degree centrality and clustering coefficient values for each node.
You can use the provided jobs.csv file as a sample dataset to test the tool. This dataset contains a list of job titles and associated skills.
Contributions are welcome! If you find any bugs or have suggestions for improvements, please open an issue or submit a pull request on GitHub.
This project is licensed under the MIT License - see the LICENSE file for details.