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Kruskal's algorithm in Javascript
Kruskal's algorithm is a greedy algorithm that works as follows −
1. It Creates a set of all edges in the graph.
2. While the above set is not empty and not all vertices are covered,
- It removes the minimum weight edge from this set
- It checks if this edge is forming a cycle or just connecting 2 trees. If it forms a cycle, we discard this edge, else we add it to our tree.
3. When the above processing is complete, we have a minimum spanning tree.
In order to implement this algorithm, we need 2 more data structures.
First, we need a priority queue that we can use to keep the edges in a sorted order and get our required edge on each iteration.
Next, we need a disjoint set data structure. A disjoint-set data structure (also called a union-find data structure or merge–find set) is a data structure that tracks a set of elements partitioned into a number of disjoint (non-overlapping) subsets. Whenever we add a new node to a tree, we will check if they are already connected. If yes, then we have a cycle. If no, we will make a union of both vertices of the edge. This will add them to the same subset.
Let us look at the implementation of UnionFind or DisjointSet data structure &minsu;
Example
class UnionFind {
constructor(elements) {
// Number of disconnected components
this.count = elements.length;
// Keep Track of connected components
this.parent = {};
// Initialize the data structure such that all
// elements have themselves as parents
elements.forEach(e => (this.parent[e] = e));
}
union(a, b) {
let rootA = this.find(a);
let rootB = this.find(b);
// Roots are same so these are already connected.
if (rootA === rootB) return;
// Always make the element with smaller root the parent.
if (rootA < rootB) {
if (this.parent[b] != b) this.union(this.parent[b], a);
this.parent[b] = this.parent[a];
} else {
if (this.parent[a] != a) this.union(this.parent[a], b);
this.parent[a] = this.parent[b];
}
}
// Returns final parent of a node
find(a) {
while (this.parent[a] !== a) {
a = this.parent[a];
}
return a;
}
// Checks connectivity of the 2 nodes
connected(a, b) {
return this.find(a) === this.find(b);
}
}You can test this using −
Example
let uf = new UnionFind(["A", "B", "C", "D", "E"]);
uf.union("A", "B"); uf.union("A", "C");
uf.union("C", "D");
console.log(uf.connected("B", "E"));
console.log(uf.connected("B", "D"));Output
This will give the output −
false true
Now let us look at the implementation of Kruskal's algorithm using this data structure −
Example
kruskalsMST() {
// Initialize graph that'll contain the MST
const MST = new Graph();
this.nodes.forEach(node => MST.addNode(node));
if (this.nodes.length === 0) {
return MST;
}
// Create a Priority Queue
edgeQueue = new PriorityQueue(this.nodes.length * this.nodes.length);
// Add all edges to the Queue:
for (let node in this.edges) {
this.edges[node].forEach(edge => {
edgeQueue.enqueue([node, edge.node], edge.weight);
});
}
let uf = new UnionFind(this.nodes);
// Loop until either we explore all nodes or queue is empty
while (!edgeQueue.isEmpty()) {
// Get the edge data using destructuring
let nextEdge = edgeQueue.dequeue();
let nodes = nextEdge.data;
let weight = nextEdge.priority;
if (!uf.connected(nodes[0], nodes[1])) {
MST.addEdge(nodes[0], nodes[1], weight);
uf.union(nodes[0], nodes[1]);
}
}
return MST;
}You can test this using −
Example
let g = new Graph();
g.addNode("A");
g.addNode("B");
g.addNode("C");
g.addNode("D");
g.addNode("E");
g.addNode("F");
g.addNode("G");
g.addEdge("A", "C", 100);
g.addEdge("A", "B", 3);
g.addEdge("A", "D", 4);
g.addEdge("C", "D", 3);
g.addEdge("D", "E", 8);
g.addEdge("E", "F", 10);
g.addEdge("B", "G", 9);
g.addEdge("E", "G", 50);
g.kruskalsMST().display();Output
This will give the output −
A->B, D B->A, G C->D D->C, A, E E->D, F F->E G->B
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