Detect Cycle in Graph using DSU
Last Updated :
16 Aug, 2023
Given an undirected graph, the task is to check if the graph contains a cycle or not, using DSU.
Examples:
Input: The following is the graph
Output: Yes
Explanation: There is a cycle of vertices {0, 1, 2}.
We already have discussed an algorithm to detect cycle in directed graph. Here Union-Find Algorithm can be used to check whether an undirected graph contains cycle or not. The idea is that,
Initially create subsets containing only a single node which are the parent of itself. Now while traversing through the edges, if the two end nodes of the edge belongs to the same set then they form a cycle. Otherwise, perform union to merge the subsets together.
Note: This method assumes that the graph doesn’t contain any self-loops.
Illustration:
Follow the below illustration for a better understanding
Let us consider the following graph:
Use an array to keep track of the subsets and which nodes belong to that subset. Let the array be parent[].
Initially, all slots of parent array are initialized to hold the same values as the node.
parent[] = {0, 1, 2}. Also when the value of the node and its parent are same, that is the root of that subset of nodes.
Now process all edges one by one.
Edge 0-1:
=> Find the subsets in which vertices 0 and 1 are.
=> 0 and 1 belongs to subset 0 and 1.
=> Since they are in different subsets, take the union of them.
=> For taking the union, either make node 0 as parent of node 1 or vice-versa.
=> 1 is made parent of 0 (1 is now representative of subset {0, 1})
=> parent[] = {1, 1, 2}
Edge 1-2:
=> 1 is in subset 1 and 2 is in subset 2.
=> Since they are in different subsets, take union.
=> Make 2 as parent of 1. (2 is now representative of subset {0, 1, 2})
=> parent[] = {1, 2, 2}
Edge 0-2:
=> 0 is in subset 2 and 2 is also in subset 2.
=> Because 1 is parent of 0 and 2 is parent of 1. So 0 also belongs to subset 2
=> Hence, including this edge forms a cycle.
Therefore, the above graph contains a cycle.
Follow the below steps to implement the idea:
- Initially create a parent[] array to keep track of the subsets.
- Traverse through all the edges:
- Check to which subset each of the nodes belong to by finding the parent[] array till the node and the parent are the same.
- If the two nodes belong to the same subset then they belong to a cycle.
- Otherwise, perform union operation on those two subsets.
- If no cycle is found, return false.
Below is the implementation of the above approach.
C++
#include <bits/stdc++.h>
using namespace std;
class Edge {
public :
int src, dest;
};
class Graph {
public :
int V, E;
Edge* edge;
};
Graph* createGraph( int V, int E)
{
Graph* graph = new Graph();
graph->V = V;
graph->E = E;
graph->edge = new Edge[graph->E * sizeof (Edge)];
return graph;
}
int find( int parent[], int i)
{
if (parent[i] == i)
return i;
return find(parent, parent[i]);
}
void Union( int parent[], int x, int y) { parent[x] = y; }
int isCycle(Graph* graph)
{
int * parent = new int [graph->V];
for ( int i = 0; i < graph->V; i++) {
parent[i] = i;
}
for ( int i = 0; i < graph->E; ++i) {
int x = find(parent, graph->edge[i].src);
int y = find(parent, graph->edge[i].dest);
if (x == y)
return 1;
Union(parent, x, y);
}
return 0;
}
int main()
{
int V = 3, E = 3;
Graph* graph = createGraph(V, E);
graph->edge[0].src = 0;
graph->edge[0].dest = 1;
graph->edge[1].src = 1;
graph->edge[1].dest = 2;
graph->edge[2].src = 0;
graph->edge[2].dest = 2;
if (isCycle(graph))
cout << "Graph contains cycle" ;
else
cout << "Graph doesn't contain cycle" ;
return 0;
}
|
C
#include <stdio.h>
#include <stdlib.h>
#include <string.h>
struct Edge {
int src, dest;
};
struct Graph {
int V, E;
struct Edge* edge;
};
struct Graph* createGraph( int V, int E)
{
struct Graph* graph
= ( struct Graph*) malloc ( sizeof ( struct Graph));
graph->V = V;
graph->E = E;
graph->edge = ( struct Edge*) malloc (
graph->E * sizeof ( struct Edge));
return graph;
}
int find( int parent[], int i)
{
if (parent[i] == -1)
return i;
return find(parent, parent[i]);
}
void Union( int parent[], int x, int y)
{
parent[y] = x;
}
int isCycle( struct Graph* graph)
{
int * parent = ( int *) malloc (graph->V);
memset (parent, -1, sizeof (graph->V));
for ( int i = 0; i < graph->E; ++i) {
int x = find(parent, graph->edge[i].src);
int y = find(parent, graph->edge[i].dest);
if (x == y && (x!=-1 && y!=-1))
return 1;
Union(parent, x,y);
}
return 0;
}
int main()
{
int V = 3, E = 3;
struct Graph* graph = createGraph(V, E);
graph->edge[0].src = 0;
graph->edge[0].dest = 1;
graph->edge[1].src = 1;
graph->edge[1].dest = 2;
graph->edge[2].src = 0;
graph->edge[2].dest = 2;
if (isCycle(graph))
printf ( "Graph contains cycle" );
else
printf ( "Graph doesn't contain cycle" );
return 0;
}
|
Java
import java.io.*;
import java.lang.*;
import java.util.*;
public class Graph {
int V, E;
Edge edge[];
class Edge {
int src, dest;
};
Graph( int v, int e)
{
V = v;
E = e;
edge = new Edge[E];
for ( int i = 0 ; i < e; ++i)
edge[i] = new Edge();
}
int find( int parent[], int i)
{
if (parent[i] == i)
return i;
return find(parent, parent[i]);
}
void Union( int parent[], int x, int y)
{
parent[x] = y;
}
int isCycle(Graph graph)
{
int parent[] = new int [graph.V];
for ( int i = 0 ; i < graph.V; ++i)
parent[i] = i;
for ( int i = 0 ; i < graph.E; ++i) {
int x = graph.find(parent, graph.edge[i].src);
int y = graph.find(parent, graph.edge[i].dest);
if (x == y)
return 1 ;
graph.Union(parent, x, y);
}
return 0 ;
}
public static void main(String[] args)
{
int V = 3 , E = 3 ;
Graph graph = new Graph(V, E);
graph.edge[ 0 ].src = 0 ;
graph.edge[ 0 ].dest = 1 ;
graph.edge[ 1 ].src = 1 ;
graph.edge[ 1 ].dest = 2 ;
graph.edge[ 2 ].src = 0 ;
graph.edge[ 2 ].dest = 2 ;
if (graph.isCycle(graph) == 1 )
System.out.println( "Graph contains cycle" );
else
System.out.println(
"Graph doesn't contain cycle" );
}
}
|
Python3
from collections import defaultdict
class Graph:
def __init__( self , vertices):
self .V = vertices
self .graph = defaultdict( list )
def addEdge( self , u, v):
self .graph[u].append(v)
def find_parent( self , parent, i):
if parent[i] = = i:
return i
if parent[i] ! = i:
return self .find_parent(parent, parent[i])
def union( self , parent, x, y):
parent[x] = y
def isCyclic( self ):
parent = [ 0 ] * ( self .V)
for i in range ( self .V):
parent[i] = i
for i in self .graph:
for j in self .graph[i]:
x = self .find_parent(parent, i)
y = self .find_parent(parent, j)
if x = = y:
return True
self .union(parent, x, y)
g = Graph( 3 )
g.addEdge( 0 , 1 )
g.addEdge( 1 , 2 )
g.addEdge( 2 , 0 )
if g.isCyclic():
print ( "Graph contains cycle" )
else :
print ( "Graph does not contain cycle " )
|
C#
using System;
class Graph {
public int V, E;
public Edge[] edge;
public class Edge {
public int src, dest;
};
public Graph( int v, int e)
{
V = v;
E = e;
edge = new Edge[E];
for ( int i = 0; i < e; ++i)
edge[i] = new Edge();
}
int find( int [] parent, int i)
{
if (parent[i] == i)
return i;
return find(parent, parent[i]);
}
void Union( int [] parent, int x, int y)
{
parent[x] = y;
}
int isCycle(Graph graph)
{
int [] parent = new int [graph.V];
for ( int i = 0; i < graph.V; ++i)
parent[i] = i;
for ( int i = 0; i < graph.E; ++i) {
int x = graph.find(parent, graph.edge[i].src);
int y = graph.find(parent, graph.edge[i].dest);
if (x == y)
return 1;
graph.Union(parent, x, y);
}
return 0;
}
public static void Main(String[] args)
{
int V = 3, E = 3;
Graph graph = new Graph(V, E);
graph.edge[0].src = 0;
graph.edge[0].dest = 1;
graph.edge[1].src = 1;
graph.edge[1].dest = 2;
graph.edge[2].src = 0;
graph.edge[2].dest = 2;
if (graph.isCycle(graph) == 1)
Console.WriteLine( "Graph contains cycle" );
else
Console.WriteLine(
"Graph doesn't contain cycle" );
}
}
|
Javascript
<script>
var V, E;
var edge;
class Edge
{
constructor()
{
this .src = 0;
this .dest = 0;
}
};
function initialize(v,e)
{
V = v;
E = e;
edge = Array.from(Array(E), () => Array());
}
function find(parent, i)
{
if (parent[i] == i)
return i;
return find(parent, parent[i]);
}
function Union(parent, x, y)
{
parent[x] = y;
}
function isCycle()
{
var parent = Array(V).fill(0);
for ( var i = 0; i < V; ++i)
parent[i] = i;
for ( var i = 0; i < E; ++i)
{
var x = find(parent,
edge[i].src);
var y = find(parent,
edge[i].dest);
if (x == y)
return 1;
Union(parent, x, y);
}
return 0;
}
var V = 3, E = 3;
initialize(V, E);
edge[0].src = 0;
edge[0].dest = 1;
edge[1].src = 1;
edge[1].dest = 2;
edge[2].src = 0;
edge[2].dest = 2;
if (isCycle() == 1)
document.write( "Graph contains cycle" );
else
document.write( "Graph doesn't contain cycle" );
</script>
|
Output
Graph contains cycle
The time and space complexity of the given code is as follows:
Time Complexity:
- Creating the graph takes O(V + E) time, where V is the number of vertices and E is the number of edges.
- Finding the subset of an element takes O(log V) time in the worst case, where V is the number of vertices. The worst case occurs when the tree is skewed, and the depth of the tree is V.
- Union of two subsets takes O(1) time.
- The loop iterating through all edges takes O(E) time.
- Therefore, the overall time complexity of the algorithm is O(E log V).
However, in practice, it can be much faster than O(E log V) because the worst-case scenario of finding the subset of an element does not happen often.
Space Complexity:
- The space complexity of creating the graph is O(E).
- The space complexity of creating the parent array is O(V).
- The space complexity of the algorithm is O(max(V,E)) because at any point in time, there can be at most max(V,E) subsets.
- Therefore, the overall space complexity of the algorithm is O(max(V,E)).
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