This project is for educational purposes and provides easy implementations of basic graph implementation and traversal algorithms.
To use the DFS or BFS algorithm, you'll need to create an instance of the Graph class from the graph.py file. You can then call the dfs or bfs method on the graph object to traverse the graph.
Here's an example of how to create a graph and use the DFS algorithm to find a path between two nodes:
from graph import Graph
from dfs import DFS
# Create a new graph
graph = Graph()
# Add nodes and edges to the graph
graph.add_node(1, 2)
graph.add_node(1, 3)
graph.add_node(2, 4)
graph.add_node(3, 5)
# Create a new DFS object and run the algorithm
dfs = DFS(graph)
visited = dfs.run_dfs(3)
# Print the nodes in order of dfs traversal
print(visited) # Output: [5, 3]You can use the same process to create and use the BFS algorithm.
The graph is implemented using adjacency list representation. Each node in the graph is represented by a dictionary, with the node value as the key and a list of its adjacent nodes as the value. Here's an example of how to create a graph using adjacency list representation:
from graph import Graph
# Create a new graph
graph = Graph()
# Add nodes and edges to the graph
graph.add_node(1, 2)
graph.add_node(1, 3)
graph.add_node(2, 4)
graph.add_node(3, 5)The DFS algorithm is a recursive algorithm that traverses the graph depth-first, exploring as far as possible along each branch before backtracking. In this project, the DFS algorithm is implemented in the dfs.py file using a stack to keep track of the nodes to be visited.
The BFS algorithm is an iterative algorithm that traverses the graph breadth-first, visiting all the nodes at a given depth before moving on to the next level. In this way, BFS can be used to find the shortest path between any two nodes of an unweighted graph.
In this project, the BFS algorithm is implemented in the bfs.py file using a queue to keep track of the nodes to be visited.