https://github.com/whdhdyt21/graph-transformation-dijkstra-python
Determining the Shortest Route, Transforming Graph to Adjacency Matrix and Incidence Matrix using Dijkstra's Algorithm in Python with NetworkX
https://github.com/whdhdyt21/graph-transformation-dijkstra-python
dijkstra-algorithm graph networkx-graph networkx-library shortest-path-algorithm
Last synced: 3 months ago
JSON representation
Determining the Shortest Route, Transforming Graph to Adjacency Matrix and Incidence Matrix using Dijkstra's Algorithm in Python with NetworkX
- Host: GitHub
- URL: https://github.com/whdhdyt21/graph-transformation-dijkstra-python
- Owner: whdhdyt21
- Created: 2023-12-16T08:09:38.000Z (almost 2 years ago)
- Default Branch: main
- Last Pushed: 2024-05-21T01:30:55.000Z (over 1 year ago)
- Last Synced: 2025-02-10T13:43:40.012Z (8 months ago)
- Topics: dijkstra-algorithm, graph, networkx-graph, networkx-library, shortest-path-algorithm
- Language: Python
- Homepage:
- Size: 15.6 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# Graph-Transformation-Dijkstra-Python
Determining the Shortest Route, Transforming Graph to Adjacency Matrix and Incidence Matrix using Dijkstra's Algorithm in Python with NetworkX
## Overview
This project demonstrates how to create a graph, transform it into an adjacency matrix and incidence matrix, and find the shortest path using Dijkstra's algorithm. The implementation is done in Python using the NetworkX library.
## Getting Started
### Prerequisites
- Python 3.x
- NetworkX
- Matplotlib
Install the necessary libraries using pip:
```sh
pip install networkx matplotlib
```
### Running the Code
1. Clone the repository:
```sh
git clone https://github.com/whdhdyt21/Graph-Transformation-Dijkstra-Python.git
cd Graph-Transformation-Dijkstra-Python
```
2. Run the `graph.ipynb` notebook in Google Colab or any local Jupyter Notebook environment.
### Features
1. **Shortest Path Calculation**:
- Use Dijkstra's algorithm to find the shortest path between two nodes.
2. **Graph Transformation**:
- Convert the graph into an adjacency matrix.
- Convert the graph into an incidence matrix.
3. **Edge Weight Modification**:
- Modify the weight of an edge in the graph.
4. **Graph Visualization**:
- Visualize the graph with nodes and edges labeled with distances.
### Usage
The code prompts the user for input to define the graph's nodes and edges along with their distances. After setting up the graph, a menu is displayed to perform various operations:
1. **Find Shortest Path**:
- Enter the start and end nodes to find the shortest path and distance.
2. **Change Edge Weight**:
- Specify an edge and its new weight to update the graph.
3. **Graph Information**:
- Display the number of nodes and edges in the graph.
4. **Display Adjacency Matrix**:
- Show the adjacency matrix of the graph.
5. **Display Incidence Matrix**:
- Show the incidence matrix of the graph.
6. **Exit**:
- Exit the program.
### Notes
- Ensure you input valid numbers when prompted for nodes, edges, and weights.
- The visualization is generated using Matplotlib and may not display properly in some environments.
## Acknowledgements
- [NetworkX Documentation](https://networkx.github.io/documentation/stable/)
- [Matplotlib Documentation](https://matplotlib.org/stable/contents.html)
## Contact
For any issues or inquiries, please open an issue in this repository or contact the maintainer at [your email].
Enjoy exploring graph transformations and shortest path algorithms!