Determine whether a universal sink exists in a directed graph
Last Updated :
20 Feb, 2023
Determine whether a universal sink exists in a directed graph. A universal sink is a vertex which has no edge emanating from it, and all other vertices have an edge towards the sink.
Input :
v1 -> v2 (implies vertex 1 is connected to vertex 2)
v3 -> v2
v4 -> v2
v5 -> v2
v6 -> v2
Output :
Sink found at vertex 2
Input :
v1 -> v6
v2 -> v3
v2 -> v4
v4 -> v3
v5 -> v3
Output :
No Sink
We try to eliminate n – 1 non-sink vertices in O(n) time and check the remaining vertex for the sink property.
To eliminate vertices, we check whether a particular index (A[i][j]) in the adjacency matrix is a 1 or a 0. If it is a 0, it means that the vertex corresponding to index j cannot be a sink. If the index is a 1, it means the vertex corresponding to i cannot be a sink. We keep increasing i and j in this fashion until either i or j exceeds the number of vertices.
Using this method allows us to carry out the universal sink test for only one vertex instead of all n vertices. Suppose we are left with only vertex i.
We now check for whether row i has only 0s and whether row j as only 1s except for A[i][i], which will be 0.
Illustration :
v1 -> v2
v3 -> v2
v4 -> v2
v5 -> v2
v6 -> v2
We can visualize the adjacency matrix for
the above as follows:
0 1 0 0 0 0
0 0 0 0 0 0
0 1 0 0 0 0
0 1 0 0 0 0
0 1 0 0 0 0
We observe that vertex 2 does not have any emanating edge, and that every other vertex has an edge in vertex 2. At A[0][0] (A[i][j]), we encounter a 0, so we increment j and next look at A[0][1]. Here we encounter a 1. So we have to increment i by 1. A[1][1] is 0, so we keep increasing j.
We notice that A[1][2], A[1][3].. etc are all 0, so j will exceed the number of vertices (6 in this example). We now check row i and column i for the sink property. Row i must be completely 0, and column i must be completely 1 except for the index A[i][i]
Adjacency Matrix
Second Example:
v1 -> v6
v2 -> v3
v2 -> v4
v4 -> v3
v5 -> v3
We can visualize the adjacency matrix
for the above as follows:
0 0 0 0 0 1
0 0 1 1 0 0
0 0 0 0 0 0
0 0 1 0 0 0
0 0 1 0 0 0
0 0 0 0 0 0
In this example, we observer that in row 1, every element is 0 except for the last column. So we will increment j until we reach the 1. When we reach 1, we increment i as long as the value of A[i][j] is 0. If i exceeds the number of vertices, it is not possible to have a sink, and in this case, i will exceed the number of vertices.
Adjacency Matrix
Implementation:
C++
#include <bits/stdc++.h>
using namespace std;
const int MAX = 100;
class Graph {
int vertices;
int adjacency_matrix[MAX][MAX];
public :
Graph( int vertices)
{
this ->vertices = vertices;
memset (adjacency_matrix, 0,
sizeof (adjacency_matrix));
}
void insert( int source, int destination)
{
adjacency_matrix[destination - 1] = 1;
}
bool is_sink( int i)
{
for ( int j = 0; j < vertices; j++) {
if (adjacency_matrix[i][j] == 1)
return false ;
if (adjacency_matrix[j][i] == 0 && j != i)
return false ;
}
return true ;
}
int eliminate()
{
int i = 0, j = 0;
while (i < vertices && j < vertices) {
if (adjacency_matrix[i][j] == 1)
i = i + 1;
else
j = j + 1;
}
if (i > vertices)
return -1;
else if (!is_sink(i))
return -1;
else
return i;
}
};
int main()
{
int number_of_vertices = 6, number_of_edges = 5;
Graph g(number_of_vertices);
g.insert(1, 6);
g.insert(2, 3);
g.insert(2, 4);
g.insert(4, 3);
g.insert(5, 3);
int vertex = g.eliminate();
if (vertex >= 0)
cout << "Sink found at vertex " << (vertex + 1)
<< endl;
else
cout << "No Sink" << endl;
return 0;
}
|
Java
import java.io.*;
import java.util.*;
class graph
{
int vertices;
int [][] adjacency_matrix;
public graph( int vertices)
{
this .vertices = vertices;
adjacency_matrix = new int [vertices][vertices];
}
public void insert( int source, int destination)
{
adjacency_matrix[destination- 1 ] = 1 ;
}
public boolean issink( int i)
{
for ( int j = 0 ; j < vertices ; j++)
{
if (adjacency_matrix[i][j] == 1 )
return false ;
if (adjacency_matrix[j][i] == 0 && j != i)
return false ;
}
return true ;
}
public int eliminate()
{
int i = 0 , j = 0 ;
while (i < vertices && j < vertices)
{
if (adjacency_matrix[i][j] == 1 )
i = i + 1 ;
else
j = j + 1 ;
}
if (i > vertices)
return - 1 ;
else if (!issink(i))
return - 1 ;
else return i;
}
}
public class Sink
{
public static void main(String[] args) throws IOException
{
int number_of_vertices = 6 ;
int number_of_edges = 5 ;
graph g = new graph(number_of_vertices);
g.insert( 1 , 6 );
g.insert( 2 , 3 );
g.insert( 2 , 4 );
g.insert( 4 , 3 );
g.insert( 5 , 3 );
int vertex = g.eliminate();
if (vertex >= 0 )
System.out.println( "Sink found at vertex "
+ (vertex + 1 ));
else
System.out.println( "No Sink" );
}
}
|
Python3
class Graph:
def __init__( self , vertices):
self .vertices = vertices
self .adjacency_matrix = [[ 0 for i in range (vertices)]
for j in range (vertices)]
def insert( self , s, destination):
self .adjacency_matrix[s - 1 ][destination - 1 ] = 1
def issink( self , i):
for j in range ( self .vertices):
if self .adjacency_matrix[i][j] = = 1 :
return False
if self .adjacency_matrix[j][i] = = 0 and j ! = i:
return False
return True
def eliminate( self ):
i = 0
j = 0
while i < self .vertices and j < self .vertices:
if self .adjacency_matrix[i][j] = = 1 :
i + = 1
else :
j + = 1
if i > self .vertices:
return - 1
elif self .issink(i) is False :
return - 1
else :
return i
if __name__ = = "__main__" :
number_of_vertices = 6
number_of_edges = 5
g = Graph(number_of_vertices)
g.insert( 1 , 6 )
g.insert( 2 , 3 )
g.insert( 2 , 4 )
g.insert( 4 , 3 )
g.insert( 5 , 3 )
vertex = g.eliminate()
if vertex > = 0 :
print ( "Sink found at vertex %d" % (vertex + 1 ))
else :
print ( "No Sink" )
|
C#
using System;
using System.Collections.Generic;
class graph
{
int vertices, itr;
int [,] adjacency_matrix;
public graph( int vertices)
{
this .vertices = vertices;
adjacency_matrix = new int [vertices, vertices];
}
public void insert( int source, int destination)
{
adjacency_matrix = 1;
}
public bool issink( int i)
{
for ( int j = 0 ; j < vertices ; j++)
{
if (adjacency_matrix[i, j] == 1)
return false ;
if (adjacency_matrix[j, i] == 0 && j != i)
return false ;
}
return true ;
}
public int eliminate()
{
int i = 0, j = 0;
while (i < vertices && j < vertices)
{
if (adjacency_matrix[i, j] == 1)
i = i + 1;
else
j = j + 1;
}
if (i > vertices)
return -1;
else if (!issink(i))
return -1;
else return i;
}
}
public class Sink
{
public static void Main(String[] args)
{
int number_of_vertices = 6;
graph g = new graph(number_of_vertices);
g.insert(1, 6);
g.insert(2, 3);
g.insert(2, 4);
g.insert(4, 3);
g.insert(5, 3);
int vertex = g.eliminate();
if (vertex >= 0)
Console.WriteLine( "Sink found at vertex "
+ (vertex + 1));
else
Console.WriteLine( "No Sink" );
}
}
|
Javascript
<script>
class Graph{
constructor(vertices){
this .vertices = vertices
this .adjacency_matrix = new Array( this .vertices).fill(0).map(()=> new Array( this .vertices).fill(0))
}
insert(s, destination){
this .adjacency_matrix[s - 1][destination - 1] = 1
}
issink(i){
for (let j=0;j< this .vertices;j++){
if ( this .adjacency_matrix[i][j] == 1)
return false
if ( this .adjacency_matrix[j][i] == 0 && j != i)
return false
}
return true
}
eliminate(){
let i = 0
let j = 0
while (i < this .vertices && j < this .vertices){
if ( this .adjacency_matrix[i][j] == 1)
i += 1
else
j += 1
}
if (i > this .vertices)
return -1
else if ( this .issink(i) == false )
return -1
else
return i
}
}
let number_of_vertices = 6
let number_of_edges = 5
let g = new Graph(number_of_vertices)
g.insert(1, 6)
g.insert(2, 3)
g.insert(2, 4)
g.insert(4, 3)
g.insert(5, 3)
let vertex = g.eliminate()
if (vertex >= 0)
document.write(`Sink found at vertex ${(vertex + 1)}`, "</br>" )
else
document.write( "No Sink" , "</br>" )
</script>
|
This program eliminates non-sink vertices in O(n) complexity and checks for the sink property in O(n) complexity.
Time complexity: O(V^2)
We have used a 2-D array of size V x V to store the adjacency matrix of the given graph. The time complexity of the algorithm is O(V^2) as we need to traverse the complete adjacency matrix to find the sink vertex.
Time complexity: O(V^2)
The space complexity of the algorithm is also O(V^2) since we need to store the adjacency matrix.
You may also try The Celebrity Problem, which is an application of this concept
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