What is Minimum Spanning Tree?
Given a connected and undirected graph, a spanning tree of that graph is a subgraph that is a tree and connects all the vertices together. A single graph can have many different spanning trees. A minimum spanning tree (MST) or minimum weight spanning tree for a weighted, connected and undirected graph is a spanning tree with weight less than or equal to the weight of every other spanning tree. The weight of a spanning tree is the sum of weights given to each edge of the spanning tree.

How many edges does a minimum spanning tree has?
A minimum spanning tree has (V – 1) edges where V is the number of vertices in the given graph.

What are the applications of Minimum Spanning Tree?
See this for applications of MST.

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Below are the steps for finding MST using Kruskal’s algorithm:

1. Sort all the edges in non-decreasing order of their weight.

2. Pick the smallest edge. Check if it forms a cycle with the spanning tree 
formed so far. If cycle is not formed, include this edge. Else, discard it.  

3. Repeat step#2 until there are (V-1) edges in the spanning tree.

The step#2 uses Union-Find algorithm to detect cycle. So we recommend to read following post as a prerequisite.
Union-Find Algorithm | Set 1 (Detect Cycle in a Graph)
Union-Find Algorithm | Set 2 (Union By Rank and Path Compression)

The algorithm is a Greedy Algorithm. The Greedy Choice is to pick the smallest weight edge that does not cause a cycle in the MST constructed so far. Let us understand it with an example: Consider the below input graph.

Kruskal’s Minimum Spanning Tree Algorithm

The graph contains 9 vertices and 14 edges. So, the minimum spanning tree formed will be having (9 – 1) = 8 edges.

After sorting:
Weight   Src    Dest
1         7      6
2         8      2
2         6      5
4         0      1
4         2      5
6         8      6
7         2      3
7         7      8
8         0      7
8         1      2
9         3      4
10        5      4
11        1      7
14        3      5

1. Pick edge 7-6: No cycle is formed, include it.

Kruskal’s Minimum Spanning Tree Algorithm)

2. Pick edge 8-2: No cycle is formed, include it.

Kruskal’s Minimum Spanning Tree Algorithm)

3. Pick edge 6-5: No cycle is formed, include it.

Kruskal’s Minimum Spanning Tree Algorithm

4. Pick edge 0-1: No cycle is formed, include it

Kruskal’s Minimum Spanning Tree Algorithm

5. Pick edge 2-5: No cycle is formed, include it.

6. Pick edge 8-6: Since including this edge results in cycle, discard it.

7. Pick edge 2-3: No cycle is formed, include it.

Kruskal’s Minimum Spanning Tree Algorithm)

8. Pick edge 7-8: Since including this edge results in cycle, discard it.

9. Pick edge 0-7: No cycle is formed, include it.

Kruskal’s Minimum Spanning Tree Algorithm)

10. Pick edge 1-2: Since including this edge results in cycle, discard it.

11. Pick edge 3-4: No cycle is formed, include it.

Kruskal’s Minimum Spanning Tree Algorithm)

Java programming:

// Java program for Kruskal's algorithm to find Minimum Spanning Tree
// of a given connected, undirected and weighted graph
import java.util.*;
import java.lang.*;
import java.io.*;

class Graph
{
// A class to represent a graph edge
class Edge implements Comparable<Edge>
{
int src, dest, weight;

// Comparator function used for sorting edges based on
// their weight
public int compareTo(Edge compareEdge)
{
return this.weight-compareEdge.weight;
}
};

// A class to represent a subset for union-find
class subset
{
int parent, rank;
};

int V, E; // V-> no. of vertices & E->no.of edges
Edge edge[]; // collection of all edges

// Creates a graph with V vertices and E edges
Graph(int v, int e)
{
V = v;
E = e;
edge = new Edge[E];
for (int i=0; i<e; ++i)
edge[i] = new Edge();
}

// A utility function to find set of an element i
// (uses path compression technique)
int find(subset subsets[], int i)
{
// find root and make root as parent of i (path compression)
if (subsets[i].parent != i)
subsets[i].parent = find(subsets, subsets[i].parent);

return subsets[i].parent;
}

// A function that does union of two sets of x and y
// (uses union by rank)
void Union(subset subsets[], int x, int y)
{
int xroot = find(subsets, x);
int yroot = find(subsets, y);

// Attach smaller rank tree under root of high rank tree
// (Union by Rank)
if (subsets[xroot].rank < subsets[yroot].rank)
subsets[xroot].parent = yroot;
else if (subsets[xroot].rank > subsets[yroot].rank)
subsets[yroot].parent = xroot;

// If ranks are same, then make one as root and increment
// its rank by one
else
{
subsets[yroot].parent = xroot;
subsets[xroot].rank++;
}
}

// The main function to construct MST using Kruskal's algorithm
void KruskalMST()
{
Edge result[] = new Edge[V]; // Tnis will store the resultant MST
int e = 0; // An index variable, used for result[]
int i = 0; // An index variable, used for sorted edges
for (i=0; i<V; ++i)
result[i] = new Edge();

// Step 1: Sort all the edges in non-decreasing order of their
// weight. If we are not allowed to change the given graph, we
// can create a copy of array of edges
Arrays.sort(edge);

// Allocate memory for creating V ssubsets
subset subsets[] = new subset[V];
for(i=0; i<V; ++i)
subsets[i]=new subset();

// Create V subsets with single elements
for (int v = 0; v < V; ++v)
{
subsets[v].parent = v;
subsets[v].rank = 0;
}

i = 0; // Index used to pick next edge

// Number of edges to be taken is equal to V-1
while (e < V - 1)
{
// Step 2: Pick the smallest edge. And increment the index
// for next iteration
Edge next_edge = new Edge();
next_edge = edge[i++];

int x = find(subsets, next_edge.src);
int y = find(subsets, next_edge.dest);

// If including this edge does't cause cycle, include it
// in result and increment the index of result for next edge
if (x != y)
{
result[e++] = next_edge;
Union(subsets, x, y);
}
// Else discard the next_edge
}

// print the contents of result[] to display the built MST
System.out.println("Following are the edges in the constructed MST");
for (i = 0; i < e; ++i)
System.out.println(result[i].src+" -- "+result[i].dest+" == "+
result[i].weight);
}

// Driver Program
public static void main (String[] args)
{

/* Let us create following weighted graph
10
0--------1
| \ |
6| 5\ |15
| \ |
2--------3
4 */
int V = 4; // Number of vertices in graph
int E = 5; // Number of edges in graph
Graph graph = new Graph(V, E);

// add edge 0-1
graph.edge[0].src = 0;
graph.edge[0].dest = 1;
graph.edge[0].weight = 10;

// add edge 0-2
graph.edge[1].src = 0;
graph.edge[1].dest = 2;
graph.edge[1].weight = 6;

// add edge 0-3
graph.edge[2].src = 0;
graph.edge[2].dest = 3;
graph.edge[2].weight = 5;

// add edge 1-3
graph.edge[3].src = 1;
graph.edge[3].dest = 3;
graph.edge[3].weight = 15;

// add edge 2-3
graph.edge[4].src = 2;
graph.edge[4].dest = 3;
graph.edge[4].weight = 4;

graph.KruskalMST();
}
}

 

Following are the edges in the constructed MST
2 -- 3 == 4
0 -- 3 == 5
0 -- 1 == 10

Time Complexity: O(ElogE) or O(ElogV). Sorting of edges takes O(ELogE) time. After sorting, we iterate through all edges and apply find-union algorithm. The find and union operations can take atmost O(LogV) time. So overall complexity is O(ELogE + ELogV) time. The value of E can be atmost O(V2), so O(LogV) are O(LogE) same. Therefore, overall time complexity is O(ElogE) or O(ElogV)

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