Depth First Search Algorithm | DFS Example

Depth First Search-

 

  • Depth First Search or DFS is a graph traversal algorithm.
  • It is used for traversing or searching a graph in a systematic fashion.
  • DFS uses a strategy that searches “deeper” in the graph whenever possible.
  • Stack data structure is used in the implementation of depth first search.

 

DFS Example-

 

Consider the following graph-

 

 

The depth first search traversal order of the above graph is-

A, B, E, F, C, D

 

Depth First Search Algorithm-

 

DFS (V,E)

 

for each vertex u in V[G]

do color[v] ← WHITE

π[v] ← NIL

time ← 0

for each vertex v in V[G]

do if color[v] ← WHITE

then Depth_First_Search(v)

 

Depth_First_Search (v)

 

color[v] ← GRAY

time ← time + 1

d[v] ← time

for each vertex u adjacent to v

do if color[u] ← WHITE

π[u] ← v

Depth_First_Search(u)

color[v] ← BLACK

time ← time + 1

f[v] ← time

 

Explanation-

 

The above depth first search algorithm is explained in the following steps-

 

Step-01

 

Create and maintain 4 variables for each vertex of the graph.

For any vertex ‘v’ of the graph, these 4 variables are-

 

1. color[v]-

 

  • This variable represents the color of the vertex ‘v’ at the given point of time.
  • The possible values of this variable are- WHITE, GREY and BLACK.
  • WHITE color of the vertex signifies that it has not been discovered yet.
  • GREY color of the vertex signifies that it has been discovered and it is being processed.
  • BLACK color of the vertex signifies that it has been completely processed.

 

2. Π[v]-

 

This variable represents the predecessor of vertex ‘v’.

 

3. d[v]-

 

This variable represents a timestamp when a vertex ‘v’ is discovered.

 

3. f[v]-

 

This variable represents a timestamp when the processing of vertex ‘v’ is completed.

 

Step-02

 

For each vertex of the graph, initialize the variables as-

  • color[v] = WHITE
  • π[v] = NIL
  • time = 0     (Global Variable acting as a timer)

 

Step-03

 

Repeat the following procedure until all the vertices of the graph become BLACK-

Consider any white vertex ‘v’ and call the following Depth_First_Search function on it.

 

Depth_First_Search (G,v)

 

1. color[v] = GRAY

2. time = time + 1

3. d[v] = time

4. For each adjacent WHITE vertex ‘u’ of ‘v’, set π[u] = v and call Depth_First_Search (G,u)

5. color[v] = BLACK

6. time = time + 1

7. f[v] = time

 

DFS Time Complexity-

 

The total running time for Depth First Search is θ (V+E).

 

Types of Edges in DFS-

 

After a DFS traversal of any graph G, all its edges can be put in one of the following 4 classes-

 

 

  1. Tree Edge
  2. Back Edge
  3. Forward Edge
  4. Cross Edge

 

1. Tree Edge-

 

  • A tree edge is an edge that is included in the DFS tree.

 

2. Back Edge-

 

An edge from a vertex ‘u’ to one of its ancestors ‘v’ is called as a back edge.

A self-loop is considered as a back edge.

 

A back edge is discovered when-

  • DFS tries to extend the visit from a vertex ‘u’ to vertex ‘v’
  • And vertex ‘v’ is found to be an ancestor of vertex ‘u’ and grey at that time.

 

3. Forward Edge-

 

An edge from a vertex ‘u’ to one of its descendants ‘v’ is called as a forward edge.

 

A forward edge is discovered when-

  • DFS tries to extend the visit from a vertex ‘u’ to a vertex ‘v’
  • And finds that color(v) = BLACK and d(v) > d(u).

 

4. Cross Edge-

 

An edge from a vertex ‘u’ to a vertex ‘v’ that is neither its ancestor nor its descendant is called as a cross edge.

 

A cross edge is discovered when-

  • DFS tries to extend the visit from a vertex ‘u’ to a vertex ‘v’
  • And finds that color(v) = BLACK and d(v) < d(u).

 

PRACTICE PROBLEM BASED ON DEPTH FIRST SEARCH-

 

Problem-

 

Compute the DFS tree for the graph given below-

 

 

Also, show the discovery and finishing time for each vertex and classify the edges.

 

Solution-

 

Initially for all the vertices of the graph, we set the variables as-

  • color[v] = WHITE
  • π[v] = NIL
  • time = 0 (Global)

 

Let us start processing the graph from vertex U.

 

Step-01:

 

  • color[U] = GREY
  • time = 0 + 1 = 1
  • d[U] = 1

 

 

Step-02:

 

  • π[V] = U
  • color[V] = GREY
  • time = 1 + 1 = 2
  • d[V] = 2

 

 

Step-03:

 

  • π[Y] = V
  • color[Y] = GREY
  • time = 2 + 1 = 3
  • d[Y] = 3

 

 

Step-04:

 

  • π[X] = Y
  • color[X] = GREY
  • time = 3 + 1 = 4
  • d[X] = 4

 

 

Step-05:

 

When DFS tries to extend the visit from vertex X to vertex V, it finds-

  • Vertex V is an ancestor of vertex X since it has already been discovered
  • Vertex V is GREY in color.

 

Thus, edge XV is a back edge.

 

 

Step-06:

 

  • color[X] = BLACK
  • time = 4 + 1 = 5
  • f[X] = 5

 

 

Step-07:

 

  • color[Y] = BLACK
  • time = 5 + 1 = 6
  • f[Y] = 6

 

 

Step-08:

 

  • color[V] = BLACK
  • time = 6 + 1 = 7
  • f[V] = 7

 

 

Step-09:

 

When DFS tries to extend the visit from vertex U to vertex X, it finds-

  • Vertex X has already been completely processed i.e. vertex X has finished and is black.
  • But vertex U has still not finished.

 

Alternatively,

When DFS tries to extend the visit from vertex U to vertex X, it finds-

  • Color(X) = BLACK
  • d(X) > d(U)

 

Thus, edge UX is a forward edge.

 

 

Step-10:

 

  • color[U] = BLACK
  • time = 7 + 1 = 8
  • f[U] = 8

 

 

Step-11:

 

  • color[W] = GREY
  • time = 8 + 1 = 9
  • d[W] = 9

 

 

Step-12:

 

When DFS tries to extend the visit from vertex W to vertex Y, it finds-

  • Vertex Y has already been completely processed i.e. vertex Y has finished.
  • Vertex Y is neither a descendant nor an ancestor of vertex W.

 

Alternatively,

When DFS tries to extend the visit from vertex W to vertex Y, it finds-

  • Color(Y) = BLACK
  • d(Y) < d(W)

 

Thus, edge WY is a cross edge.

 

 

Step-13:

 

  • π[Z] = W
  • color[W] = GREY
  • time = 9 + 1 = 10
  • d[W] = 10

 

 

Step-14:

 

Since, self-loops are considered as back edges.

Therefore, self-loop present on vertex Z is considered as a back edge.

 

 

Step-15:

 

  • color[Z] = BLACK
  • time = 10 + 1 = 11
  • f[Z] = 11

 

 

Step-16:

 

  • color[W] = BLACK
  • time = 11 + 1 = 12
  • f[W] = 12

 

 

Since all the vertices have turned black, so we stop.

This is how a given graph is traversed using Depth First Search (DFS) technique.

 

To gain better understanding about Depth First Search Algorithm,

Watch this Video Lecture

 

Next Article- Breadth First Search

 

Get more notes and other study material of Design and Analysis of Algorithms.

Watch video lectures by visiting our YouTube channel LearnVidFun.

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Depth First Search Algorithm | DFS Example
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Depth First Search Algorithm | DFS Example
Description
Depth First Search Algorithm is a Graph Traversing Algorithm. DFS Algorithm is discussed Step by Step. DFS Example. DFS Algorithm searches deeper in the graph whenever possible.
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