How Do You Write a Function for Depth First Search in Graph Traversal?

[Henry R]

Hello... how to write the function of depth first search?

Thank you.

 

[evinda]

Hello... how to write the function of depth first search?

Thank you.

Hi! (Wave)

That's the algorithm of depth-first-search:

Depthfirstsearch(G)
   for each v ∈ V
        color[v]=white
        p[v]=NIL
   time=0
   for each v ∈ V
       if color[v]=white then
          Visit(v)

Visit(u)
  color[u]=gray
  time=time+1
  d[u]=time
  for each v ∈ Adj[u]
       if color[v]=white then
          p[v]=u
          Visit(v)
  color[u]=black
  time=time+1
  f[u]=time

 

[Henry R]

Hi! (Wave)

That's the algorithm of depth-first-search:

Depthfirstsearch(G)
   for each v ∈ V
        color[v]=white
        p[v]=NIL
   time=0
   for each v ∈ V
       if color[v]=white then
          Visit(v)

Visit(u)
  color[u]=gray
  time=time+1
  d[u]=time
  for each v ∈ Adj[u]
       if color[v]=white then
          p[v]=u
          Visit(v)
  color[u]=black
  time=time+1
  f[u]=time

Hey! Thanks. But, did u get the code by any websites? Oh okay, I understand. Thanks for helping.

 

[evinda]

Hey! Thanks. But, did u get the code by any websites? Oh okay, I understand. Thanks for helping.

I found it in my notes.. (Smile)

 

[LudusRex]

Depth First Search (DFS) is a popular graph traversal algorithm used in computer science and data structures. It is used to traverse a graph or tree data structure, starting from a given source vertex and visiting all of its adjacent vertices before moving on to the next level of vertices.

To write the function of depth first search, we first need to define the data structure for representing a graph. This can be done using an adjacency list or an adjacency matrix. Once we have the graph represented, we can then write the DFS function.

The basic steps for implementing DFS are as follows:

1. Create a visited array to keep track of the visited vertices.

2. Initialize all vertices as not visited.

3. Create a stack to keep track of the vertices to be visited.

4. Push the source vertex onto the stack.

5. While the stack is not empty, pop a vertex from the stack.

6. If the popped vertex has not been visited, mark it as visited and process it.

7. Find all the adjacent vertices of the popped vertex and push them onto the stack.

8. Repeat steps 5 to 7 until the stack is empty.

The pseudo-code for the DFS function can be written as:

DFS(graph, source):

// Initialize the visited array
visited = [False]*graph.num_vertices

// Create a stack and push the source vertex onto it
stack = [source]

// Loop until the stack is empty
while stack:

// Pop a vertex from the stack
vertex = stack.pop()

// If the vertex has not been visited
if not visited[vertex]:

// Mark it as visited and process it
visited[vertex] = True
process(vertex)

// Find all the adjacent vertices and push them onto the stack
for adjacent_vertex in graph.adjacent_vertices(vertex):
stack.push(adjacent_vertex)

The time complexity of DFS is O(V+E), where V is the number of vertices and E is the number of edges in the graph. This is because we visit each vertex and each edge only once.

In conclusion, DFS is a simple yet powerful algorithm for traversing a graph data structure. By implementing the above steps, we can successfully traverse a graph using DFS.