# Topological Sorting

Topological sorting for Directed Acyclic Graph (DAG) is a linear ordering of vertices such that for every directed edge uv, vertex u comes before v in the ordering. Topological Sorting for a graph is not possible if the graph is not a DAG.

For example, a topological sorting of the following graph is “5 4 2 3 1 0”. There can be more than one topological sorting for a graph. For example, another topological sorting of the following graph is “4 5 2 3 1 0”. The first vertex in topological sorting is always a vertex with in-degree as 0 (a vertex with no in-coming edges).

Topological Sorting vs Depth First Traversal (DFS):
In DFS, we print a vertex and then recursively call DFS for its adjacent vertices. In topological sorting, we need to print a vertex before its adjacent vertices. For example, in the given graph, the vertex ‘5’ should be printed before vertex ‘0’, but unlike DFS, the vertex ‘4’ should also be printed before vertex ‘0’. So Topological sorting is different from DFS. For example, a DFS of the shown graph is “5 2 3 1 0 4”, but it is not a topological sorting

## Recommended: Please solve it on “PRACTICE” first, before moving on to the solution.

Algorithm to find Topological Sorting:
We recommend to first see implementation of DFS here. We can modify DFS to find Topological Sorting of a graph. In DFS, we start from a vertex, we first print it and then recursively call DFS for its adjacent vertices. In topological sorting, we use a temporary stack. We don’t print the vertex immediately, we first recursively call topological sorting for all its adjacent vertices, then push it to a stack. Finally, print contents of stack. Note that a vertex is pushed to stack only when all of its adjacent vertices (and their adjacent vertices and so on) are already in stack.

Following are C++ and Java implementations of topological sorting. Please see the code for Depth First Traversal for a disconnected Graph and note the differences between the second code given there and the below code.

## C++

```// A C++ program to print topological sorting of a DAG
#include<iostream>
#include <list>
#include <stack>
using namespace std;

// Class to represent a graph
class Graph
{
int V;    // No. of vertices'

// Pointer to an array containing adjacency listsList

// A function used by topologicalSort
void topologicalSortUtil(int v, bool visited[], stack<int> &Stack);
public:
Graph(int V);   // Constructor

// function to add an edge to graph

// prints a Topological Sort of the complete graph
void topologicalSort();
};

Graph::Graph(int V)
{
this->V = V;
}

{
}

// A recursive function used by topologicalSort
void Graph::topologicalSortUtil(int v, bool visited[],
stack<int> &Stack)
{
// Mark the current node as visited.
visited[v] = true;

// Recur for all the vertices adjacent to this vertex
list<int>::iterator i;
if (!visited[*i])
topologicalSortUtil(*i, visited, Stack);

// Push current vertex to stack which stores result
Stack.push(v);
}

// The function to do Topological Sort. It uses recursive
// topologicalSortUtil()
void Graph::topologicalSort()
{
stack<int> Stack;

// Mark all the vertices as not visited
bool *visited = new bool[V];
for (int i = 0; i < V; i++)
visited[i] = false;

// Call the recursive helper function to store Topological
// Sort starting from all vertices one by one
for (int i = 0; i < V; i++)
if (visited[i] == false)
topologicalSortUtil(i, visited, Stack);

// Print contents of stack
while (Stack.empty() == false)
{
cout << Stack.top() << " ";
Stack.pop();
}
}

// Driver program to test above functions
int main()
{
// Create a graph given in the above diagram
Graph g(6);

cout << "Following is a Topological Sort of the given graph n";
g.topologicalSort();

return 0;
}
```

## Java

```// A Java program to print topological sorting of a DAG
import java.io.*;
import java.util.*;

// This class represents a directed graph using adjacency
// list representation
class Graph
{
private int V;   // No. of vertices

//Constructor
Graph(int v)
{
V = v;
for (int i=0; i<v; ++i)
}

// Function to add an edge into the graph

// A recursive function used by topologicalSort
void topologicalSortUtil(int v, boolean visited[],
Stack stack)
{
// Mark the current node as visited.
visited[v] = true;
Integer i;

// Recur for all the vertices adjacent to this
// vertex
while (it.hasNext())
{
i = it.next();
if (!visited[i])
topologicalSortUtil(i, visited, stack);
}

// Push current vertex to stack which stores result
stack.push(new Integer(v));
}

// The function to do Topological Sort. It uses
// recursive topologicalSortUtil()
void topologicalSort()
{
Stack stack = new Stack();

// Mark all the vertices as not visited
boolean visited[] = new boolean[V];
for (int i = 0; i < V; i++)
visited[i] = false;

// Call the recursive helper function to store
// Topological Sort starting from all vertices
// one by one
for (int i = 0; i < V; i++)
if (visited[i] == false)
topologicalSortUtil(i, visited, stack);

// Print contents of stack
while (stack.empty()==false)
System.out.print(stack.pop() + " ");
}

// Driver method
public static void main(String args[])
{
// Create a graph given in the above diagram
Graph g = new Graph(6);

System.out.println("Following is a Topological " +
"sort of the given graph");
g.topologicalSort();
}
}
// This code is contributed by Aakash Hasija
```

## Python

```#Python program to print topological sorting of a DAG
from collections import defaultdict

#Class to represent a graph
class Graph:
def __init__(self,vertices):
self.graph = defaultdict(list) #dictionary containing adjacency List
self.V = vertices #No. of vertices

# function to add an edge to graph
self.graph[u].append(v)

# A recursive function used by topologicalSort
def topologicalSortUtil(self,v,visited,stack):

# Mark the current node as visited.
visited[v] = True

# Recur for all the vertices adjacent to this vertex
for i in self.graph[v]:
if visited[i] == False:
self.topologicalSortUtil(i,visited,stack)

# Push current vertex to stack which stores result
stack.insert(0,v)

# The function to do Topological Sort. It uses recursive
# topologicalSortUtil()
def topologicalSort(self):
# Mark all the vertices as not visited
visited = [False]*self.V
stack =[]

# Call the recursive helper function to store Topological
# Sort starting from all vertices one by one
for i in range(self.V):
if visited[i] == False:
self.topologicalSortUtil(i,visited,stack)

# Print contents of stack
print stack

g= Graph(6)

print "Following is a Topological Sort of the given graph"
g.topologicalSort()
#This code is contributed by Neelam Yadav
```

Output:
```Following is a Topological Sort of the given graph
5 4 2 3 1 0```

Time Complexity: The above algorithm is simply DFS with an extra stack. So time complexity is same as DFS which is O(V+E).

Applications:
Topological Sorting is mainly used for scheduling jobs from the given dependencies among jobs. In computer science, applications of this type arise in instruction scheduling, ordering of formula cell evaluation when recomputing formula values in spreadsheets, logic synthesis, determining the order of compilation tasks to perform in makefiles, data serialization, and resolving symbol dependencies in linkers [2].

Related Articles:
Kahn’s algorithm for Topological Sorting : Another O(V + E) algorithm.
All Topological Sorts of a Directed Acyclic Graph

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