Skip to content
geeksforgeeks
  • Tutorials
    • Python
    • Java
    • Data Structures & Algorithms
    • ML & Data Science
    • Interview Corner
    • Programming Languages
    • Web Development
    • CS Subjects
    • DevOps And Linux
    • School Learning
    • Practice Coding Problems
  • Courses
    • DSA to Development
    • Get IBM Certification
    • Newly Launched!
      • Master Django Framework
      • Become AWS Certified
    • For Working Professionals
      • Interview 101: DSA & System Design
      • Data Science Training Program
      • JAVA Backend Development (Live)
      • DevOps Engineering (LIVE)
      • Data Structures & Algorithms in Python
    • For Students
      • Placement Preparation Course
      • Data Science (Live)
      • Data Structure & Algorithm-Self Paced (C++/JAVA)
      • Master Competitive Programming (Live)
      • Full Stack Development with React & Node JS (Live)
    • Full Stack Development
    • Data Science Program
    • All Courses
  • DSA
  • Interview Problems on Graph
  • Practice Graph
  • MCQs on Graph
  • Graph Tutorial
  • Graph Representation
  • Graph Properties
  • Types of Graphs
  • Graph Applications
  • BFS on Graph
  • DFS on Graph
  • Graph VS Tree
  • Transpose Graph
  • Dijkstra's Algorithm
  • Minimum Spanning Tree
  • Prim’s Algorithm
  • Topological Sorting
  • Floyd Warshall Algorithm
  • Strongly Connected Components
  • Advantages & Disadvantages
Open In App
Next Article:
Topological Sort of a graph using departure time of vertex
Next article icon

Topological Sort of a graph using departure time of vertex

Last Updated : 20 Feb, 2023
Comments
Improve
Suggest changes
Like Article
Like
Report

Given a Directed Acyclic Graph (DAG), find Topological Sort of the graph.

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".

Topological Sort

Please note that the first vertex in topological sorting is always a vertex with in-degree as 0 (a vertex with no incoming edges). For above graph, vertex 4 and 5 have no incoming edges.

We have already discussed a DFS-based algorithm using stack and Kahn’s Algorithm for Topological Sorting. We have also discussed how to print all topological sorts of the DAG here. In this post, another DFS based approach is discussed for finding Topological sort of a graph by introducing concept of arrival and departure time of a vertex in DFS.

What is Arrival Time & Departure Time of Vertices in DFS? 
In DFS, Arrival Time is the time at which the vertex was explored for the first time and Departure Time is the time at which we have explored all the neighbors of the vertex and we are ready to backtrack.

How to find Topological Sort of a graph using departure time? 
To find Topological Sort of a graph, we run DFS starting from all unvisited vertices one by one. For any vertex, before exploring any of its neighbors, we note the arrival time of that vertex and after exploring all the neighbors of the vertex, we note its departure time. Please note only departure time is needed to find Topological Sort of a graph, so we can skip arrival time of vertex. Finally, after we have visited all the vertices of the graph, we print the vertices in order of their decreasing departure time which is our desired Topological Order of Vertices.

Below is C++ implementation of above idea –

C++
// A C++ program to print topological sorting of a DAG #include <bits/stdc++.h> using namespace std;  // Graph class represents a directed graph using adjacency // list representation class Graph {     int V; // No. of vertices     // Pointer to an array containing adjacency lists     list<int>* adj;  public:     Graph(int); // Constructor     ~Graph(); // Destructor      // function to add an edge to graph     void addEdge(int, int);      // The function to do DFS traversal     void DFS(int, vector<bool>&, vector<int>&, int&);      // The function to do Topological Sort.     void topologicalSort(); };  Graph::Graph(int V) {     this->V = V;     this->adj = new list<int>[V]; }  Graph::~Graph() { delete[] this->adj; }  void Graph::addEdge(int v, int w) {     adj[v].push_back(w); // Add w to v's list. }  // The function to do DFS() and stores departure time // of all vertex void Graph::DFS(int v, vector<bool>& visited,                 vector<int>& departure, int& time) {     visited[v] = true;     // time++;    // arrival time of vertex v      for (int i : adj[v])         if (!visited[i])             DFS(i, visited, departure, time);      // set departure time of vertex v     departure[time++] = v; }  // The function to do Topological Sort. It uses DFS(). void Graph::topologicalSort() {     // vector to store departure time of vertex.     vector<int> departure(V, -1);      // Mark all the vertices as not visited     vector<bool> visited(V, false);     int time = 0;      // perform DFS on all unvisited vertices     for (int i = 0; i < V; i++) {         if (visited[i] == 0) {             DFS(i, visited, departure, time);         }     }     // print the topological sort     for (int i = V - 1; i >= 0; i--)         cout << departure[i] << " "; }  // Driver program to test above functions int main() {     // Create a graph given in the above diagram     Graph g(6);     g.addEdge(5, 2);     g.addEdge(5, 0);     g.addEdge(4, 0);     g.addEdge(4, 1);     g.addEdge(2, 3);     g.addEdge(3, 1);      cout << "Topological Sort of the given graph is \n";     g.topologicalSort();      return 0; } 
Python3
# A Python3 program to print topological sorting of a DAG def addEdge(u, v):     global adj     adj[u].append(v)  # The function to do DFS() and stores departure time # of all vertex def DFS(v):     global visited, departure, time     visited[v] = 1     for i in adj[v]:         if visited[i] == 0:             DFS(i)     departure[time] = v     time += 1  # The function to do Topological Sort. It uses DFS(). def topologicalSort():      # perform DFS on all unvisited vertices     for i in range(V):         if(visited[i] == 0):             DFS(i)      # Print vertices in topological order     for i in range(V - 1, -1, -1):         print(departure[i], end = " ")  # Driver code if __name__ == '__main__':        # Create a graph given in the above diagram     V,time, adj, visited, departure = 6, 0, [[] for i in range(7)], [0 for i in range(7)],[-1 for i in range(7)]     addEdge(5, 2)     addEdge(5, 0)     addEdge(4, 0)     addEdge(4, 1)     addEdge(2, 3)     addEdge(3, 1)      print("Topological Sort of the given graph is")     topologicalSort()  # This code is contributed by mohit kumar 29 
C#
// C# program to print topological sorting of a DAG using System; using System.Collections.Generic;  // Graph class represents a directed graph using adjacency // list representation public class Graph {   private int V;   private List<int>[] adj;    // constructor   public Graph(int v)   {     V = v;     adj = new List<int>[ v ];     for (int i = 0; i < v; i++)       adj[i] = new List<int>();   }    // Add an edge   public void AddEdge(int v, int w)   {     adj[v].Add(w); // Add w to v's list   }    // The function to do DFS() and stores departure time   // of all vertex   private void DFS(int v, bool[] visited, int[] departure,                    ref int time)   {     visited[v] = true;     // time++; // arrival time of vertex v      foreach(int i in adj[v])     {       if (!visited[i])         DFS(i, visited, departure, ref time);     }      // set departure time of vertex v     departure[time++] = v;   }    // The function to do Topological Sort. It uses DFS().   public void TopologicalSort()   {     // vector to store departure time of vertex.     int[] departure = new int[V];     for (int i = 0; i < V; i++)       departure[i] = -1;      // Mark all the vertices as not visited     bool[] visited = new bool[V];     int time = 0;      // perform DFS on all unvisited vertices     for (int i = 0; i < V; i++) {       if (visited[i] == false) {         DFS(i, visited, departure, ref time);       }     }     // print the topological sort     for (int i = V - 1; i >= 0; i--)       Console.Write(departure[i] + " ");     Console.WriteLine();   } }  class GFG {   // Driver program to test above functions   static void Main(string[] args)   {     // Create a graph given in the above diagram     Graph g = new Graph(6);     g.AddEdge(5, 2);     g.AddEdge(5, 0);     g.AddEdge(4, 0);     g.AddEdge(4, 1);     g.AddEdge(2, 3);     g.AddEdge(3, 1);      Console.WriteLine(       "Topological Sort of the given graph is");     g.TopologicalSort();   } }  // This code is contributed by cavi4762 
JavaScript
<script>  // A JavaScript program to print topological  // sorting of a DAG          let adj=new Array(7);     for(let i=0;i<adj.length;i++)     {         adj[i]=[];     }     let V=6;     let time=0;     let visited=new Array(7);     let departure =new Array(7);     for(let i=0;i<7;i++)     {         visited[i]=0;         departure[i]=-1;     }     function addEdge(u, v)     {         adj[u].push(v)     }          // The function to do DFS() and      // stores departure time     // of all vertex     function DFS(v)     {         visited[v] = 1;         for(let i=0;i<adj[v].length;i++)         {             if(visited[adj[v][i]]==0)                 DFS(adj[v][i]);         }         departure[time] = v         time += 1     }     // The function to do Topological      // Sort. It uses DFS().     function topologicalSort()     {     //perform DFS on all unvisited vertices         for(let i=0;i<V;i++)         {             if(visited[i] == 0)                 DFS(i)         }         //perform DFS on all unvisited vertices         for(let i=V-1;i>=0;i--)         {                         document.write(departure[i]+" ");         }     }          // Create a graph given in the above diagram          addEdge(5, 2);     addEdge(5, 0);     addEdge(4, 0);     addEdge(4, 1);     addEdge(2, 3);     addEdge(3, 1);     document.write(     "Topological Sort of the given graph is<br>"     );     topologicalSort()          // This code is contributed by unknown2108      </script> 
Java
import java.util.ArrayList;  // Graph class represents a directed graph using adjacency // list representation public class GFG {     int V;     ArrayList<ArrayList<Integer> > adj;     int time = 0;     // constructor     public GFG(int v)     {         V = v;         adj = new ArrayList<>();         for (int i = 0; i < v; i++)             adj.add(new ArrayList<>());     }      // Add an edge     public void AddEdge(int v, int w)     {         adj.get(v).add(w); // Add w to v's list     }      // The function to do DFS() and stores departure time     // of all vertex     private void DFS(int v, boolean[] visited,                      int[] departure)     {         visited[v] = true;         // time++; // arrival time of vertex v          for (int i : adj.get(v)) {             if (!visited[i])                 DFS(i, visited, departure);         }          // set departure time of vertex v         departure[time++] = v;     }      // The function to do Topological Sort. It uses DFS().     public void TopologicalSort()     {         // vector to store departure time of vertex.         int[] departure = new int[V];         for (int i = 0; i < V; i++)             departure[i] = -1;          // Mark all the vertices as not visited         boolean[] visited = new boolean[V];         // perform DFS on all unvisited vertices         for (int i = 0; i < V; i++) {             if (!visited[i]) {                 DFS(i, visited, departure);             }         }         // print the topological sort         for (int i = V - 1; i >= 0; i--)             System.out.print(departure[i] + " ");     }     // Driver program to test above functions     public static void main(String[] args)     {         // Create a graph given in the above diagram         GFG g = new GFG(6);         g.AddEdge(5, 2);         g.AddEdge(5, 0);         g.AddEdge(4, 0);         g.AddEdge(4, 1);         g.AddEdge(2, 3);         g.AddEdge(3, 1);          System.out.println(             "Topological Sort of the given graph is");         g.TopologicalSort();     } }  // This code is contributed by Prithi_Dey 

Output
Topological Sort of the given graph is  5 4 2 3 1 0 


Time Complexity of above solution is O(V + E).

Space Complexity of this algorithm is O(V). This is because we use a vector to store the departure time of each vertex, which is of size V.


Next Article
Topological Sort of a graph using departure time of vertex

K

kartik
Improve
Article Tags :
  • Graph
  • DSA
  • Topological Sorting
Practice Tags :
  • Graph

Similar Reads

    Graph Algorithms
    Graph is a non-linear data structure like tree data structure. The limitation of tree is, it can only represent hierarchical data. For situations where nodes or vertices are randomly connected with each other other, we use Graph. Example situations where we use graph data structure are, a social net
    3 min read
    Introduction to Graph Data Structure
    Graph Data Structure is a non-linear data structure consisting of vertices and edges. It is useful in fields such as social network analysis, recommendation systems, and computer networks. In the field of sports data science, graph data structure can be used to analyze and understand the dynamics of
    15+ min read
    Graph and its representations
    A Graph is a non-linear data structure consisting of vertices and edges. The vertices are sometimes also referred to as nodes and the edges are lines or arcs that connect any two nodes in the graph. More formally a Graph is composed of a set of vertices( V ) and a set of edges( E ). The graph is den
    12 min read
    Types of Graphs with Examples
    A graph is a mathematical structure that represents relationships between objects by connecting a set of points. It is used to establish a pairwise relationship between elements in a given set. graphs are widely used in discrete mathematics, computer science, and network theory to represent relation
    9 min read
    Basic Properties of a Graph
    A Graph is a non-linear data structure consisting of nodes and edges. The nodes are sometimes also referred to as vertices and the edges are lines or arcs that connect any two nodes in the graph. The basic properties of a graph include: Vertices (nodes): The points where edges meet in a graph are kn
    4 min read
    Applications, Advantages and Disadvantages of Graph
    Graph is a non-linear data structure that contains nodes (vertices) and edges. A graph is a collection of set of vertices and edges (formed by connecting two vertices). A graph is defined as G = {V, E} where V is the set of vertices and E is the set of edges. Graphs can be used to model a wide varie
    7 min read
    Transpose graph
    Transpose of a directed graph G is another directed graph on the same set of vertices with all of the edges reversed compared to the orientation of the corresponding edges in G. That is, if G contains an edge (u, v) then the converse/transpose/reverse of G contains an edge (v, u) and vice versa. Giv
    9 min read
    Difference Between Graph and Tree
    Graphs and trees are two fundamental data structures used in computer science to represent relationships between objects. While they share some similarities, they also have distinct differences that make them suitable for different applications. Difference Between Graph and Tree What is Graph?A grap
    2 min read

    BFS and DFS on Graph

    Breadth First Search or BFS for a Graph
    Given a undirected graph represented by an adjacency list adj, where each adj[i] represents the list of vertices connected to vertex i. Perform a Breadth First Search (BFS) traversal starting from vertex 0, visiting vertices from left to right according to the adjacency list, and return a list conta
    15+ min read
    Depth First Search or DFS for a Graph
    In Depth First Search (or DFS) for a graph, we traverse all adjacent vertices one by one. When we traverse an adjacent vertex, we completely finish the traversal of all vertices reachable through that adjacent vertex. This is similar to a tree, where we first completely traverse the left subtree and
    13 min read
    Applications, Advantages and Disadvantages of Depth First Search (DFS)
    Depth First Search is a widely used algorithm for traversing a graph. Here we have discussed some applications, advantages, and disadvantages of the algorithm. Applications of Depth First Search:1. Detecting cycle in a graph: A graph has a cycle if and only if we see a back edge during DFS. So we ca
    4 min read
    Applications, Advantages and Disadvantages of Breadth First Search (BFS)
    We have earlier discussed Breadth First Traversal Algorithm for Graphs. Here in this article, we will see the applications, advantages, and disadvantages of the Breadth First Search. Applications of Breadth First Search: 1. Shortest Path and Minimum Spanning Tree for unweighted graph: In an unweight
    4 min read
    Iterative Depth First Traversal of Graph
    Given a directed Graph, the task is to perform Depth First Search of the given graph.Note: Start DFS from node 0, and traverse the nodes in the same order as adjacency list.Note : There can be multiple DFS traversals of a graph according to the order in which we pick adjacent vertices. Here we pick
    10 min read
    BFS for Disconnected Graph
    In the previous post, BFS only with a particular vertex is performed i.e. it is assumed that all vertices are reachable from the starting vertex. But in the case of a disconnected graph or any vertex that is unreachable from all vertex, the previous implementation will not give the desired output, s
    14 min read
    Transitive Closure of a Graph using DFS
    Given a directed graph, find out if a vertex v is reachable from another vertex u for all vertex pairs (u, v) in the given graph. Here reachable means that there is a path from vertex u to v. The reach-ability matrix is called transitive closure of a graph. For example, consider below graph: GraphTr
    8 min read
    Difference between BFS and DFS
    Breadth-First Search (BFS) and Depth-First Search (DFS) are two fundamental algorithms used for traversing or searching graphs and trees. This article covers the basic difference between Breadth-First Search and Depth-First Search.Difference between BFS and DFSParametersBFSDFSStands forBFS stands fo
    2 min read

    Cycle in a Graph

    Detect Cycle in a Directed Graph
    Given the number of vertices V and a list of directed edges, determine whether the graph contains a cycle or not.Examples: Input: V = 4, edges[][] = [[0, 1], [0, 2], [1, 2], [2, 0], [2, 3]]Cycle: 0 → 2 → 0 Output: trueExplanation: The diagram clearly shows a cycle 0 → 2 → 0 Input: V = 4, edges[][] =
    15+ min read
    Detect cycle in an undirected graph
    Given an undirected graph, the task is to check if there is a cycle in the given graph.Examples:Input: V = 4, edges[][]= [[0, 1], [0, 2], [1, 2], [2, 3]]Undirected Graph with 4 vertices and 4 edgesOutput: trueExplanation: The diagram clearly shows a cycle 0 → 2 → 1 → 0Input: V = 4, edges[][] = [[0,
    8 min read
    Detect Cycle in a directed graph using colors
    Given a directed graph represented by the number of vertices V and a list of directed edges, determine whether the graph contains a cycle.Your task is to implement a function that accepts V (number of vertices) and edges (an array of directed edges where each edge is a pair [u, v]), and returns true
    9 min read
    Detect a negative cycle in a Graph | (Bellman Ford)
    Given a directed weighted graph, your task is to find whether the given graph contains any negative cycles that are reachable from the source vertex (e.g., node 0).Note: A negative-weight cycle is a cycle in a graph whose edges sum to a negative value.Example:Input: V = 4, edges[][] = [[0, 3, 6], [1
    15+ min read
    Cycles of length n in an undirected and connected graph
    Given an undirected and connected graph and a number n, count the total number of simple cycles of length n in the graph. A simple cycle of length n is defined as a cycle that contains exactly n vertices and n edges. Note that for an undirected graph, each cycle should only be counted once, regardle
    10 min read
    Detecting negative cycle using Floyd Warshall
    We are given a directed graph. We need compute whether the graph has negative cycle or not. A negative cycle is one in which the overall sum of the cycle comes negative. Negative weights are found in various applications of graphs. For example, instead of paying cost for a path, we may get some adva
    12 min read
    Clone a Directed Acyclic Graph
    A directed acyclic graph (DAG) is a graph which doesn't contain a cycle and has directed edges. We are given a DAG, we need to clone it, i.e., create another graph that has copy of its vertices and edges connecting them. Examples: Input : 0 - - - > 1 - - - -> 4 | / \ ^ | / \ | | / \ | | / \ |
    12 min read
geeksforgeeks-footer-logo
Corporate & Communications Address:
A-143, 7th Floor, Sovereign Corporate Tower, Sector- 136, Noida, Uttar Pradesh (201305)
Registered Address:
K 061, Tower K, Gulshan Vivante Apartment, Sector 137, Noida, Gautam Buddh Nagar, Uttar Pradesh, 201305
GFG App on Play Store GFG App on App Store
Advertise with us
  • Company
  • About Us
  • Legal
  • Privacy Policy
  • In Media
  • Contact Us
  • Advertise with us
  • GFG Corporate Solution
  • Placement Training Program
  • Languages
  • Python
  • Java
  • C++
  • PHP
  • GoLang
  • SQL
  • R Language
  • Android Tutorial
  • Tutorials Archive
  • DSA
  • Data Structures
  • Algorithms
  • DSA for Beginners
  • Basic DSA Problems
  • DSA Roadmap
  • Top 100 DSA Interview Problems
  • DSA Roadmap by Sandeep Jain
  • All Cheat Sheets
  • Data Science & ML
  • Data Science With Python
  • Data Science For Beginner
  • Machine Learning
  • ML Maths
  • Data Visualisation
  • Pandas
  • NumPy
  • NLP
  • Deep Learning
  • Web Technologies
  • HTML
  • CSS
  • JavaScript
  • TypeScript
  • ReactJS
  • NextJS
  • Bootstrap
  • Web Design
  • Python Tutorial
  • Python Programming Examples
  • Python Projects
  • Python Tkinter
  • Python Web Scraping
  • OpenCV Tutorial
  • Python Interview Question
  • Django
  • Computer Science
  • Operating Systems
  • Computer Network
  • Database Management System
  • Software Engineering
  • Digital Logic Design
  • Engineering Maths
  • Software Development
  • Software Testing
  • DevOps
  • Git
  • Linux
  • AWS
  • Docker
  • Kubernetes
  • Azure
  • GCP
  • DevOps Roadmap
  • System Design
  • High Level Design
  • Low Level Design
  • UML Diagrams
  • Interview Guide
  • Design Patterns
  • OOAD
  • System Design Bootcamp
  • Interview Questions
  • Inteview Preparation
  • Competitive Programming
  • Top DS or Algo for CP
  • Company-Wise Recruitment Process
  • Company-Wise Preparation
  • Aptitude Preparation
  • Puzzles
  • School Subjects
  • Mathematics
  • Physics
  • Chemistry
  • Biology
  • Social Science
  • English Grammar
  • Commerce
  • World GK
  • GeeksforGeeks Videos
  • DSA
  • Python
  • Java
  • C++
  • Web Development
  • Data Science
  • CS Subjects
@GeeksforGeeks, Sanchhaya Education Private Limited, All rights reserved
We use cookies to ensure you have the best browsing experience on our website. By using our site, you acknowledge that you have read and understood our Cookie Policy & Privacy Policy
Lightbox
Improvement
Suggest Changes
Help us improve. Share your suggestions to enhance the article. Contribute your expertise and make a difference in the GeeksforGeeks portal.
geeksforgeeks-suggest-icon
Create Improvement
Enhance the article with your expertise. Contribute to the GeeksforGeeks community and help create better learning resources for all.
geeksforgeeks-improvement-icon
Suggest Changes
min 4 words, max Words Limit:1000

Thank You!

Your suggestions are valuable to us.

What kind of Experience do you want to share?

Interview Experiences
Admission Experiences
Career Journeys
Work Experiences
Campus Experiences
Competitive Exam Experiences