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Minimum number of edges between two vertices of a Graph
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Detect Cycle in a Directed Graph using BFS

Last Updated : 28 Jul, 2022
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Given a directed graph, check whether the graph contains a cycle or not. Your function should return true if the given graph contains at least one cycle, else return false. For example, the following graph contains two cycles 0->1->2->3->0 and 2->4->2, so your function must return true.

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

We have discussed a DFS based solution to detect cycle in a directed graph. In this post, BFS based solution is discussed.
The idea is to simply use Kahn’s algorithm for Topological Sorting

Steps involved in detecting cycle in a directed graph using BFS.
Step-1: Compute in-degree (number of incoming edges) for each of the vertex present in the graph and initialize the count of visited nodes as 0.
Step-2: Pick all the vertices with in-degree as 0 and add them into a queue (Enqueue operation)
Step-3: Remove a vertex from the queue (Dequeue operation) and then. 

  1. Increment count of visited nodes by 1.
  2. Decrease in-degree by 1 for all its neighboring nodes.
  3. If in-degree of a neighboring nodes is reduced to zero, then add it to the queue.

Step 4: Repeat Step 3 until the queue is empty.
Step 5: If count of visited nodes is not equal to the number of nodes in the graph has cycle, otherwise not.

How to find in-degree of each node? 
There are 2 ways to calculate in-degree of every vertex: 
Take an in-degree array which will keep track of 
1) Traverse the array of edges and simply increase the counter of the destination node by 1. 

for each node in Nodes     indegree[node] = 0; for each edge(src,dest) in Edges     indegree[dest]++

Time Complexity: O(V+E)

2) Traverse the list for every node and then increment the in-degree of all the nodes connected to it by 1. 

    for each node in Nodes         If (list[node].size()!=0) then         for each dest in list             indegree[dest]++;

Time Complexity: The outer for loop will be executed V number of times and the inner for loop will be executed E number of times, Thus overall time complexity is O(V+E).

The overall time complexity of the algorithm is O(V+E) 

C++




// A C++ program to check if there is a cycle in
// directed graph using BFS.
#include <bits/stdc++.h>
using namespace std;
 
// Class to represent a graph
class Graph {
    int V; // No. of vertices'
 
    // Pointer to an array containing adjacency list
    list<int>* adj;
 
public:
    Graph(int V); // Constructor
 
    // function to add an edge to graph
    void addEdge(int u, int v);
 
    // Returns true if there is a cycle in the graph
    // else false.
    bool isCycle();
};
 
Graph::Graph(int V)
{
    this->V = V;
    adj = new list<int>[V];
}
 
void Graph::addEdge(int u, int v)
{
    adj[u].push_back(v);
}
 
// This function returns true if there is a cycle
// in directed graph, else returns false.
bool Graph::isCycle()
{
    // Create a vector to store indegrees of all
    // vertices. Initialize all indegrees as 0.
    vector<int> in_degree(V, 0);
 
    // Traverse adjacency lists to fill indegrees of
    // vertices. This step takes O(V+E) time
    for (int u = 0; u < V; u++) {
        for (auto v : adj[u])
            in_degree[v]++;
    }
 
    // Create an queue and enqueue all vertices with
    // indegree 0
    queue<int> q;
    for (int i = 0; i < V; i++)
        if (in_degree[i] == 0)
            q.push(i);
 
    // Initialize count of visited vertices
    // 1 For src Node
    int cnt = 1;
 
    // Create a vector to store result (A topological
    // ordering of the vertices)
    vector<int> top_order;
 
    // One by one dequeue vertices from queue and enqueue
    // adjacents if indegree of adjacent becomes 0
    while (!q.empty()) {
 
        // Extract front of queue (or perform dequeue)
        // and add it to topological order
        int u = q.front();
        q.pop();
        top_order.push_back(u);
 
        // Iterate through all its neighbouring nodes
        // of dequeued node u and decrease their in-degree
        // by 1
        list<int>::iterator itr;
        for (itr = adj[u].begin(); itr != adj[u].end(); itr++)
 
            // If in-degree becomes zero, add it to queue
            if (--in_degree[*itr] == 0)
            {
              q.push(*itr);
              //while we are pushing elements to the queue we will incrementing the cnt
              cnt++;
            }
 
        
    }
 
    // Check if there was a cycle
    if (cnt != V)
        return true;
    else
        return false;
}
 
// Driver program to test above functions
int main()
{
    // Create a graph given in the above diagram
    Graph g(6);
    g.addEdge(0, 1);
    g.addEdge(1, 2);
    g.addEdge(2, 0);
    g.addEdge(3, 4);
    g.addEdge(4, 5);
 
    if (g.isCycle())
        cout << "Yes";
    else
        cout << "No";
 
    return 0;
}
 
 

Java




// Java program to check if there is a cycle in
// directed graph using BFS.
import java.io.*;
import java.util.*;
 
class GFG
{
 
    // Class to represent a graph
    static class Graph
    {
        int V; // No. of vertices'
 
        // Pointer to an array containing adjacency list
        Vector<Integer>[] adj;
 
        @SuppressWarnings("unchecked")
        Graph(int V)
        {
            // Constructor
            this.V = V;
            this.adj = new Vector[V];
            for (int i = 0; i < V; i++)
                adj[i] = new Vector<>();
        }
 
        // function to add an edge to graph
        void addEdge(int u, int v)
        {
            adj[u].add(v);
        }
 
        // Returns true if there is a cycle in the graph
        // else false.
 
        // This function returns true if there is a cycle
        // in directed graph, else returns false.
        boolean isCycle()
        {
 
            // Create a vector to store indegrees of all
            // vertices. Initialize all indegrees as 0.
            int[] in_degree = new int[this.V];
            Arrays.fill(in_degree, 0);
 
            // Traverse adjacency lists to fill indegrees of
            // vertices. This step takes O(V+E) time
            for (int u = 0; u < V; u++)
            {
                for (int v : adj[u])
                    in_degree[v]++;
            }
 
            // Create an queue and enqueue all vertices with
            // indegree 0
            Queue<Integer> q = new LinkedList<Integer>();
            for (int i = 0; i < V; i++)
                if (in_degree[i] == 0)
                    q.add(i);
 
            // Initialize count of visited vertices
            int cnt = 0;
 
            // Create a vector to store result (A topological
            // ordering of the vertices)
            Vector<Integer> top_order = new Vector<>();
 
            // One by one dequeue vertices from queue and enqueue
            // adjacents if indegree of adjacent becomes 0
            while (!q.isEmpty())
            {
 
                // Extract front of queue (or perform dequeue)
                // and add it to topological order
                int u = q.poll();
                top_order.add(u);
 
                // Iterate through all its neighbouring nodes
                // of dequeued node u and decrease their in-degree
                // by 1
                for (int itr : adj[u])
                    if (--in_degree[itr] == 0)
                        q.add(itr);
                cnt++;
            }
 
            // Check if there was a cycle
            if (cnt != this.V)
                return true;
            else
                return false;
        }
    }
 
    // Driver Code
    public static void main(String[] args)
    {
 
        // Create a graph given in the above diagram
        Graph g = new Graph(6);
        g.addEdge(0, 1);
        g.addEdge(1, 2);
        g.addEdge(2, 0);
        g.addEdge(3, 4);
        g.addEdge(4, 5);
 
        if (g.isCycle())
            System.out.println("Yes");
        else
            System.out.println("No");
    }
}
 
// This code is contributed by
// sanjeev2552
 
 

Python3




# A Python3 program to check if there is a cycle in 
# directed graph using BFS.
import math
import sys
from collections import defaultdict
 
# Class to represent a graph
class Graph:
    def __init__(self,vertices):
        self.graph=defaultdict(list)
        self.V=vertices # No. of vertices'
     
    # function to add an edge to graph
    def addEdge(self,u,v):
        self.graph[u].append(v)
 
# This function returns true if there is a cycle
# in directed graph, else returns false.
def isCycleExist(n,graph):
 
    # Create a vector to store indegrees of all
    # vertices. Initialize all indegrees as 0.
    in_degree=[0]*n
 
    # Traverse adjacency lists to fill indegrees of
    # vertices. This step takes O(V+E) time
    for i in range(n):
        for j in graph[i]:
            in_degree[j]+=1
     
    # Create an queue and enqueue all vertices with
    # indegree 0
    queue=[]
    for i in range(len(in_degree)):
        if in_degree[i]==0:
            queue.append(i)
     
    # Initialize count of visited vertices
    cnt=0
 
    # One by one dequeue vertices from queue and enqueue
    # adjacents if indegree of adjacent becomes 0
    while(queue):
 
        # Extract front of queue (or perform dequeue)
        # and add it to topological order
        nu=queue.pop(0)
 
        # Iterate through all its neighbouring nodes
        # of dequeued node u and decrease their in-degree
        # by 1
        for v in graph[nu]:
            in_degree[v]-=1
 
            # If in-degree becomes zero, add it to queue
            if in_degree[v]==0:
                queue.append(v)
        cnt+=1
 
    # Check if there was a cycle
    if cnt==n:
        return False
    else:
        return True
         
 
# Driver program to test above functions
if __name__=='__main__':
 
    # Create a graph given in the above diagram
    g=Graph(6)
    g.addEdge(0,1)
    g.addEdge(1,2)
    g.addEdge(2,0)
    g.addEdge(3,4)
    g.addEdge(4,5)
     
    if isCycleExist(g.V,g.graph):
        print("Yes")
    else:
        print("No")
 
# This Code is Contributed by Vikash Kumar 37
 
 

C#




// C# program to check if there is a cycle in
// directed graph using BFS.
using System;
using System.Collections.Generic;
 
class GFG{
     
// Class to represent a graph
public class Graph
{
     
    // No. of vertices'
    public int V;
     
    // Pointer to an array containing
    // adjacency list
    public List<int>[] adj;
     
    public Graph(int V)
    {
         
        // Constructor
        this.V = V;
        this.adj = new List<int>[V];
        for (int i = 0; i < V; i++)
        adj[i] = new List<int>();
    }
     
    // Function to add an edge to graph
    public void addEdge(int u, int v)
    {
        adj[u].Add(v);
    }
     
    // Returns true if there is a cycle in the
    // graph else false.
     
    // This function returns true if there is
    // a cycle in directed graph, else returns
    // false.
    public bool isCycle()
    {
         
        // Create a vector to store indegrees of all
        // vertices. Initialize all indegrees as 0.
        int[] in_degree = new int[this.V];
         
        // Traverse adjacency lists to fill indegrees
        // of vertices. This step takes O(V+E) time
        for(int u = 0; u < V; u++)
        {
            foreach(int v in adj[u])
                in_degree[v]++;
        }
         
        // Create an queue and enqueue all
        // vertices with indegree 0
        Queue<int> q = new Queue<int>();
        for(int i = 0; i < V; i++)
            if (in_degree[i] == 0)
                q.Enqueue(i);
         
        // Initialize count of visited vertices
        int cnt = 0;
         
        // Create a vector to store result
        // (A topological ordering of the
        // vertices)
        List<int> top_order = new List<int>();
         
        // One by one dequeue vertices from
        // queue and enqueue adjacents if
        // indegree of adjacent becomes 0
        while (q.Count != 0)
        {
         
            // Extract front of queue (or perform
            // dequeue) and add it to topological
            // order
            int u = q.Peek();
            q.Dequeue();
            top_order.Add(u);
             
            // Iterate through all its neighbouring
            // nodes of dequeued node u and decrease
            // their in-degree by 1
            foreach(int itr in adj[u])
                if (--in_degree[itr] == 0)
                    q.Enqueue(itr);
                     
            cnt++;
        }
         
        // Check if there was a cycle
        if (cnt != this.V)
            return true;
        else
            return false;
    }
}
 
// Driver Code
public static void Main(String[] args)
{
     
    // Create a graph given in the above diagram
    Graph g = new Graph(6);
    g.addEdge(0, 1);
    g.addEdge(1, 2);
    g.addEdge(2, 0);
    g.addEdge(3, 4);
    g.addEdge(4, 5);
 
    if (g.isCycle())
        Console.WriteLine("Yes");
    else
        Console.WriteLine("No");
}
}
 
// This code is contributed by Princi Singh
 
 

Javascript




<script>
 
// JavaScript program to check if there is a cycle in
// directed graph using BFS.
 
// Class to represent a graph
// No. of vertices'
var V = 0;
 
// Pointer to an array containing
// adjacency list
var adj ;
 
function initialize(v)
{
     
    // Constructor
    V = v;
    adj = Array.from(Array(V), ()=>Array(V));
}
 
// Function to add an edge to graph
function addEdge(u, v)
{
    adj[u].push(v);
}
 
// Returns true if there is a cycle in the
// graph else false.
 
// This function returns true if there is
// a cycle in directed graph, else returns
// false.
function isCycle()
{
     
    // Create a vector to store indegrees of all
    // vertices. Initialize all indegrees as 0.
    var in_degree = Array(V).fill(0);
     
    // Traverse adjacency lists to fill indegrees
    // of vertices. This step takes O(V+E) time
    for(var u = 0; u < V; u++)
    {
        for(var v of adj[u])
            in_degree[v]++;
    }
     
    // Create an queue and enqueue all
    // vertices with indegree 0
    var q = [];
    for(var i = 0; i < V; i++)
        if (in_degree[i] == 0)
            q.push(i);
     
    // Initialize count of visited vertices
    var cnt = 0;
     
    // Create a vector to store result
    // (A topological ordering of the
    // vertices)
    var top_order = [];
     
    // One by one dequeue vertices from
    // queue and enqueue adjacents if
    // indegree of adjacent becomes 0
    while (q.length != 0)
    {
     
        // Extract front of queue (or perform
        // dequeue) and add it to topological
        // order
        var u = q[0];
        q.shift();
        top_order.push(u);
         
        // Iterate through all its neighbouring
        // nodes of dequeued node u and decrease
        // their in-degree by 1
        for(var itr of adj[u])
            if (--in_degree[itr] == 0)
                q.push(itr);
                 
        cnt++;
    }
     
    // Check if there was a cycle
    if (cnt != V)
        return true;
    else
        return false;
}
 
// Create a graph given in the above diagram
initialize(6)
addEdge(0, 1);
addEdge(1, 2);
addEdge(2, 0);
addEdge(3, 4);
addEdge(4, 5);
if (isCycle())
    document.write("Yes");
else
    document.write("No");
 
 
</script>
 
 
Output: 
Yes

 

Time Complexity: O(V+E)
Auxiliary Space: O(V) 



Next Article
Minimum number of edges between two vertices of a Graph
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  • Advanced Data Structure
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      Given a directed graph, a source vertex ‘src’ and a destination vertex ‘dst’, print all paths from given ‘src’ to ‘dst’. Please note that in the cases, we have cycles in the graph, we need not to consider paths have cycles as in case of cycles, there can by infinitely many by doing multiple iteratio
      9 min read

    • Minimum steps to reach target by a Knight | Set 1
      Given a square chessboard of n x n size, the position of the Knight and the position of a target are given. We need to find out the minimum steps a Knight will take to reach the target position. Examples: Input: knightPosition: (1, 3) , targetPosition: (5, 0) Output: 3Explanation: In above diagram K
      9 min read

    Intermediate problems on BFS

    • Traversal of a Graph in lexicographical order using BFS
      C/C++ Code // C++ program to implement // the above approach #include <bits/stdc++.h> using namespace std; // Function to traverse the graph in // lexicographical order using BFS void LexiBFS(map<char, set<char> >& G, char S, map<char, bool>& vis) { // Stores nodes of
      9 min read

    • Detect cycle in an undirected graph using BFS
      Given an undirected graph, the task is to determine if cycle is present in it or not. Examples: Input: V = 5, edges[][] = [[0, 1], [0, 2], [0, 3], [1, 2], [3, 4]] Output: trueExplanation: The diagram clearly shows a cycle 0 → 2 → 1 → 0. Input: V = 4, edges[][] = [[0, 1], [1, 2], [2, 3]] Output: fals
      6 min read

    • Detect Cycle in a Directed Graph using BFS
      Given a directed graph, check whether the graph contains a cycle or not. Your function should return true if the given graph contains at least one cycle, else return false. For example, the following graph contains two cycles 0->1->2->3->0 and 2->4->2, so your function must return
      11 min read

    • Minimum number of edges between two vertices of a Graph
      You are given an undirected graph G(V, E) with N vertices and M edges. We need to find the minimum number of edges between a given pair of vertices (u, v). Examples: Input: For given graph G. Find minimum number of edges between (1, 5). Output: 2Explanation: (1, 2) and (2, 5) are the only edges resu
      8 min read

    • Word Ladder - Shortest Chain To Reach Target Word
      Given an array of strings arr[], and two different strings start and target, representing two words. The task is to find the length of the smallest chain from string start to target, such that only one character of the adjacent words differs and each word exists in arr[]. Note: Print 0 if it is not
      15 min read

    • Print the lexicographically smallest BFS of the graph starting from 1
      Given a connected graph with N vertices and M edges. The task is to print the lexicographically smallest BFS traversal of the graph starting from 1. Note: The vertices are numbered from 1 to N.Examples: Input: N = 5, M = 5 Edges: 1 4 3 4 5 4 3 2 1 5 Output: 1 4 3 2 5 Start from 1, go to 4, then to 3
      7 min read

    • Shortest path in an unweighted graph
      Given an unweighted, undirected graph of V nodes and E edges, a source node S, and a destination node D, we need to find the shortest path from node S to node D in the graph. Input: V = 8, E = 10, S = 0, D = 7, edges[][] = {{0, 1}, {1, 2}, {0, 3}, {3, 4}, {4, 7}, {3, 7}, {6, 7}, {4, 5}, {4, 6}, {5,
      11 min read

    • Number of shortest paths in an unweighted and directed graph
      Given an unweighted directed graph, can be cyclic or acyclic. Print the number of shortest paths from a given vertex to each of the vertices. For example consider the below graph. There is one shortest path vertex 0 to vertex 0 (from each vertex there is a single shortest path to itself), one shorte
      11 min read

    • Distance of nearest cell having 1 in a binary matrix
      Given a binary grid of n*m. Find the distance of the nearest 1 in the grid for each cell.The distance is calculated as |i1  - i2| + |j1 - j2|, where i1, j1 are the row number and column number of the current cell, and i2, j2 are the row number and column number of the nearest cell having value 1. Th
      15+ min read

    Hard Problems on BFS

    • Islands in a graph using BFS
      Given an n x m grid of 'W' (Water) and 'L' (Land), the task is to count the number of islands. An island is a group of adjacent 'L' cells connected horizontally, vertically, or diagonally, and it is surrounded by water or the grid boundary. The goal is to determine how many distinct islands exist in
      15+ min read

    • Print all shortest paths between given source and destination in an undirected graph
      Given an undirected and unweighted graph and two nodes as source and destination, the task is to print all the paths of the shortest length between the given source and destination.Examples: Input: source = 0, destination = 5 Output: 0 -> 1 -> 3 -> 50 -> 2 -> 3 -> 50 -> 1 ->
      13 min read

    • Count Number of Ways to Reach Destination in a Maze using BFS
      Given a maze of dimensions n x m represented by the matrix mat, where mat[i][j] = -1 represents a blocked cell and mat[i][j] = 0 represents an unblocked cell, the task is to count the number of ways to reach the bottom-right cell starting from the top-left cell by moving right (i, j+1) or down (i+1,
      8 min read

    • Coin Change | BFS Approach
      Given an integer X and an array arr[] of length N consisting of positive integers, the task is to pick minimum number of integers from the array such that they sum up to N. Any number can be chosen infinite number of times. If no answer exists then print -1.Examples: Input: X = 7, arr[] = {3, 5, 4}
      6 min read

    • Water Jug problem using BFS
      Given two empty jugs of m and n litres respectively. The jugs don't have markings to allow measuring smaller quantities. You have to use the jugs to measure d litres of water. The task is to find the minimum number of operations to be performed to obtain d litres of water in one of the jugs. In case
      12 min read

    • Word Ladder - Set 2 ( Bi-directional BFS )
      Given a dictionary, and two words start and target (both of the same length). Find length of the smallest chain from start to target if it exists, such that adjacent words in the chain only differ by one character and each word in the chain is a valid word i.e., it exists in the dictionary. It may b
      15+ min read

    • Implementing Water Supply Problem using Breadth First Search
      Given N cities that are connected using N-1 roads. Between Cities [i, i+1], there exists an edge for all i from 1 to N-1.The task is to set up a connection for water supply. Set the water supply in one city and water gets transported from it to other cities using road transport. Certain cities are b
      10 min read

    • Minimum Cost Path in a directed graph via given set of intermediate nodes
      Given a weighted, directed graph G, an array V[] consisting of vertices, the task is to find the Minimum Cost Path passing through all the vertices of the set V, from a given source S to a destination D. Examples: Input: V = {7}, S = 0, D = 6 Output: 11 Explanation: Minimum path 0->7->5->6.
      10 min read

    • Shortest path in a Binary Maze
      Given an M x N matrix where each element can either be 0 or 1. We need to find the shortest path between a given source cell to a destination cell. The path can only be created out of a cell if its value is 1. Note: You can move into an adjacent cell in one of the four directions, Up, Down, Left, an
      15+ min read

    • Minimum cost to traverse from one index to another in the String
      Given a string S of length N consisting of lower case character, the task is to find the minimum cost to reach from index i to index j. At any index k, the cost to jump to the index k+1 and k-1(without going out of bounds) is 1. Additionally, the cost to jump to any index m such that S[m] = S[k] is
      10 min read

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