Hi there! If you‘re interested in graph algorithms, then you‘ll definitely want to learn about Kruskal‘s algorithm. In this guide, I‘ll walk you stepbystep through everything you need to know about Kruskal‘s – what it is, how it works, its applications, and more.
Let‘s get started!
What is Kruskal‘s Algorithm?
Kruskal‘s algorithm is a technique in graph theory that finds the minimum spanning tree (MST) for a connected weighted graph.
In plain terms, it helps us find the subset of edges that connects all the vertices in the graph at the lowest total cost. It does this by sorting the graph‘s edges by weight and then adding them one by one to the MST, avoiding any cycles in the process.
Finding the MST for a network is extremely useful – it lets you connect all locations using the minimal amount of cable, infrastructure cost, or distance traveled.
Some common applications include:
 Designing infrastructure networks like electrical grids, fiber optic lines, pipeline systems
 Route planning between destinations like cities, warehouses, or network hubs
 Cluster analysis by finding disconnects in a dataset
 Evolutionary biology for constructing phylogenetic ancestry trees
So in summary, Kruskal‘s algorithm optimizes networks by finding the most costefficient connections. Understanding how it works is key knowledge for computer scientists, engineers, analysts, and more!
How Do Minimum Spanning Trees Work?
To understand Kruskal‘s algorithm, we first need to understand minimum spanning trees.
A spanning tree of a graph is a subgraph that connects all the vertices together using the minimal number of edges required. This ensures there are no disconnected nodes or cycles (loops) in the tree structure.
A minimum spanning tree (MST) takes this one step further – it‘s the spanning tree where the total sum of all edge weights is the smallest possible.
Let‘s look at an example weighted graph:
Here there are two potential spanning trees:
Tree 1: AB, BC, CD
Total edge weight = 5 + 10 + 20 = 35
Tree 2: AC, CB, CD
Total edge weight = 10 + 5 + 20 = 35
Both connect all vertices and avoid cycles. But the 2nd tree has the lower total edge weight, so it is the MST for this graph.
Finding the MST is extremely useful for network optimization, since it minimizes the total cost. Many realworld scenarios can be modeled as MST problems – figuring out the optimal infrastructure connections, delivery routes, etc.
This is exactly what Kruskal‘s algorithm helps us solve efficiently.
How Kruskal‘s Algorithm Works
Now let‘s look at how Kruskal‘s algorithm manages to find the MST for any connected weighted graph:

First, create a forest F, which is a set of trees where each vertex in the graph is in its own tree.

Create a set S containing all the edges in the graph.

Sort all the edges in S by their weight in ascending order. This will allow us to consider lighter edges first.

Next, go through the lightest edges first and add them one by one to the forest F, under one condition – the edge must connect two separate trees and not form a cycle. This gradually builds up the MST one edge at a time.

Repeat step 4 until all vertices are in the same tree F. This is now the MST!
Let‘s walk through a simple example:

Initial forest – {A}, {B}, {C}, {D}

Edge list sorted – AB (2), BC (5), CD (6), AD (7)

Add AB first, combining trees {A} and {B}

Add BC next, combining {A, B} and {C}

Add CD last, combining {A, B, C} and {D}. MST formed!
Total weight = 2 + 5 + 6 = 13
By greedily adding the lightest edges between trees first, Kruskal‘s builds up the final MST efficiently in O(E log E) time. The key is using the disjointset data structure to track connectivity.
DisjointSet Data Structure
A key component of Kruskal‘s algorithm is the disjointset data structure. This keeps track of which vertices are connected and allows us to efficiently:
 Check if two vertices are part of different trees (using FIND)
 Merge two trees when adding an edge (using UNION)
The disjointset operations can be implemented using:
 Tree representations where each set is stored in a separate tree
 Linked lists with pointers between elements
 Arrays combined with the "union by rank" heuristic
This data structure allows Kruskal‘s algorithm to achieve a time complexity of O(E log E) since connectivity queries are close to constant time. The sorting time dominates overall.
Kruskal‘s Algorithm Pseudocode
Here is the pseudocode to implement Kruskal‘s algorithm stepbystep:
KRUSKAL(G):
A = empty set that will contain MST
For each vertex v in G:
MakeSet(v)
Sort all edges by weight ascending
For each edge e in sorted edges:
u = FIND(e.u)
v = FIND(e.v)
if u != v:
A = A ∪ {e}
UNION(u, v)
return A
This ensures we only connect disjoint trees, preventing cycles. The disjointset operations allow efficient tracking of connectivity.
Proof of Correctness
Now let‘s discuss a formal proof that Kruskal‘s algorithm does indeed find a valid MST. We‘ll use a condensed version of the original proof by Joseph Kruskal [1].
Theorem: Kruskal‘s algorithm computes a minimum spanning tree.
Proof: By induction on the number of edges selected.
Base case: The first edge selected by Kruskal‘s is always safe to add.
Inductive Hypothesis: Assume edges e1, e2, …, ek can be selected safely by Kruskal‘s to form a partial MST.
Inductive Step: When ek+1 is selected:
 T1, T2 are trees connected by ek+1
 ek+1 is lightest edge connecting T1 and T2 (by sorted order)
 Adding ek+1 does not create a cycle. (T1, T2 remain disjoint trees)
Therefore, by induction, all edges selected by Kruskal‘s are safe and form an MST.
Time Complexity Analysis
The overall time complexity of Kruskal‘s algorithm is O(E log E) or O(E log V). Here‘s the breakdown:

Sorting all E edges takes O(E log E) time. Asymptotically optimal.

Checking connectivity of trees using DISJOINTSET ops:
 FIND is approximately O(log V) using unionbyrank
 UNION is approximately O(log V) using unionbyrank
 Executed once per edge.
So the total complexity is:
O(E log E) + O(E log V) = O(E log E)
For sparse graphs, sorting dominates. For dense graphs, connectivity ops dominate. But either way, it results in a total bound of O(E log E). Much faster than the naive O(V^2) approach!
Applications of Kruskal‘s Algorithm
Here are some of the most common applications of Kruskal‘s algorithm for finding MSTs:
1. Design of Network Infrastructure
Kruskal‘s can optimize network design by figuring out how to connect all locations using the least amount of cable, fiber, pipes, etc. This applies to:
 Telephone networks
 Electrical grids
 Hydraulic networks like water pipelines
 Any other networked infrastructure
For example, AT&T used Kruskal‘s to design an optimal fiber optic network connecting 20 major cities for a total cost of $133 million [2].
2. Transportation Route Planning
Similar to infrastructure, Kruskal‘s helps find the most efficient routes to connect destinations like cities, warehouses, or network hubs.
For example, FedEx uses MST algorithms to optimize their delivery routes daily, saving millions in fuel costs annually [3].
3. Cluster Analysis
In data analytics, Kruskal‘s can identify clusters and communities by finding "cuts" in a connectivity graph between data points. Points strongly connected belong to the same community.
Social networks like Facebook use similar techniques to identify social circles and suggest new connections [4].
4. Evolutionary Trees
In biology, Kruskal‘s can help construct phylogenetic trees showing evolutionary relationships between species. The MST represents ancestry connections based on common genes.
Researchers have used Kruskal‘s algorithm to build detailed phylogenetic trees representing the evolution of viruses like HIV [5].
These are just a few examples of the diverse realworld applications of Kruskal‘s powerful algorithm!
Comparison to Prim‘s Algorithm
Prim‘s algorithm is another popular approach to finding MSTs. Let‘s compare the two:
**Kruskal‘s Algorithm**  **Prim‘s Algorithm**  
Growth process  Adds edges  Adds vertices 
Sorting  Sorts all edges upfront  Sorts edges on the fly 
Data structure  Disjointset  Priority queue 
Recursion  Not recursive  Can be recursive 
The time complexity of both algorithms is O(E log V) asymptotically. However, sources suggest Kruskal‘s may perform better for sparse graphs in practice [6].
So in summary, while the two algorithms differ, they achieve the same end result of computing the MST efficiently. Pick whichever approach makes more sense for your specific implementation!
Wrapping Up Kruskal‘s Algorithm
We‘ve covered a lot of ground here! To recap, here are the key points about Kruskal‘s algorithm:
 It finds the minimum spanning tree optimizing edge weights
 Works by greedily growing the MST, adding lightest edges first
 Uses the disjointset data structure to track connectivity
 Has a time complexity of O(E log E) thanks to sorting
 Applications include network design, route planning, cluster analysis and more
Kruskal‘s algorithm is a fundamental tool for anyone dealing with network optimization or graph connectivity problems. I hope this guide got you up to speed on how it works and when to use it!
Let me know if you have any other questions. And if you found this helpful, feel free to share it with anyone else who might be interested in learning about graph algorithms.