Depth-First Search is a fundamental algorithm used for traversing and exploring graphs. Below, I’ll outline the steps and provide a Python code implementation for DFS:
Step 1: Introduction to DFS and Graph Representation:
Briefly explain what DFS is and its purpose in graph traversal.
Introduce the concept of a graph and different ways to represent it (e.g., adjacency list, adjacency matrix).
Step 2: Implementing the Graph:
Code the graph representation using an adjacency list.
Show how to add vertices and edges to the graph.
class Graph:
def __init__(self):
self.graph = {}
def add_vertex(self, vertex):
self.graph[vertex] = []
def add_edge(self, u, v):
self.graph[u].append(v)
self.graph[v].append(u) # For an undirected graph
Step 3: Implementing Depth-First Search (DFS)
Code the DFS algorithm using a recursive approach.
Explain the importance of visited nodes to avoid infinite loops in graphs containing cycles.
def dfs_recursive(graph, start, visited):
if start not in visited:
print(start, end=' ')
visited.add(start)
for neighbor in graph[start]:
dfs_recursive(graph, neighbor, visited)
Step 4: Traversing the Graph with DFS
Create a graph instance and add vertices and edges to it.
Perform DFS on the graph and display the traversal order.
if __name__ == "__main__":
g = Graph()
# Add vertices and edges to the graph
g.add_vertex(0)
g.add_vertex(1)
g.add_vertex(2)
g.add_vertex(3)
g.add_edge(0, 1)
g.add_edge(0, 2)
g.add_edge(1, 2)
g.add_edge(2, 0)
g.add_edge(2, 3)
g.add_edge(3, 3)
print("DFS Traversal (Starting from vertex 2):")
visited_nodes = set()
dfs_recursive(g.graph, 2, visited_nodes)
Step 5: Walkthrough of the DFS Execution
Explain the step-by-step execution of DFS on the provided graph.
Highlight the visited nodes and the order in which they were visited.
Step 6: Conclusion and Recap
Summarize the key points of DFS and its importance in graph traversal.
Recap the steps of the DFS algorithm and the code implementation.