Time Complexity of Kruskal Algorithm: Data Structure, Example

Introduction

Hey there, fellow curious minds! Welcome to our blog, where we embark on an exciting journey into the depths of Kruskal’s Algorithm, the rockstar of graph theory! In this blog, we’ll demystify the magic behind Kruskal’s Algorithm, exploring its time complexity and real-life applications and understanding the importance of data structures in this context. 

With crystal-clear explanations and captivating examples, we’ll equip you with the knowledge to tackle complex problems and design efficient networks like a pro! So, hop on board because this blog is your ticket to mastering Kruskal’s Algorithm and unlocking the secrets of data structures!

Overview of Kruskal’s Algorithm

In the vast realm of data structures, Kruskal’s algorithm is an essential tool in graph theory. It plays a crucial role in solving problems like finding the Minimum Spanning Tree (MST) of a connected, undirected graph. This remarkable algorithm, named after Joseph Kruskal, was designed to find the minimum-weight spanning tree for a given graph, where the sum of all edge weights is minimized while ensuring the tree remains connected.

Kruskal’s algorithm utilizes a greedy approach to construct the MST. It starts by sorting the edges in ascending order of their weights and then gradually adds edges to the MST, ensuring no cycles are formed until all vertices are connected. The process is intuitive, and efficient, and offers a promising solution for many real-world applications. Gain detailed understanding of this via Data Analytics 360 Cornell Certificate Program. 

Prim’s and Kruskal’s Algorithms

Prim’s and Kruskal’s algorithms are popular approaches to finding the minimum spanning tree (MST) in a connected weighted graph. The MST is a subset of edges that connects all vertices with the least total weight possible. Prim’s algorithm starts with a single vertex and repeatedly adds the minimum-weight edge that connects the current MST to a new vertex until all vertices are included.

• It operates in O(V^2) time with an adjacency matrix or O(E+V log V) time with a priority queue using an adjacency list.
• On the other hand, Kruskal’s algorithm sorts all edges by weight and iteratively adds the smallest edge that doesn’t form a cycle.
• It has a time complexity of O(E+log E) using a disjoint-set data structure.
• Both algorithms guarantee the construction of the MST, but their performance may vary depending on the graph’s characteristics.

Learn data science courses online from the World’s top Universities. Earn Executive PG Programs, Advanced Certificate Programs, or Masters Programs to fast-track your career.

Time Complexity Analysis of Kruskal’s Algorithm

As students delve into Kruskal’s algorithm, understanding its time complexity becomes crucial to comprehend its computational efficiency. The time complexity of this algorithm mainly depends on three fundamental operations:

1. Sorting the Edges: Since Kruskal’s algorithm begins by sorting the edges, the choice of sorting algorithm directly affects the overall time complexity. Typically, a comparison-based sorting algorithm such as QuickSort or MergeSort is used, resulting in a time complexity of O(E log E), where E represents the number of edges.
2. Union-Find Data Structure Operations: Kruskal’s algorithm relies on the Union-Find (Disjoint Set) data structure to check for cycles while adding edges. The time complexity of these operations is essential for determining the overall efficiency of the algorithm. We will delve into the details of these operations shortly.
3. MST Construction: The final step involves constructing the MST by adding edges to the growing forest. This process takes O(E) time, considering the graph has E edges.

Check out our free courses to get an edge over the competition.

Time Complexity of Union Function

The Union-Find (Disjoint Set) data structure plays a vital role in Kruskal’s algorithm, making it imperative to explore the time complexity of its key operation, the Union function.

• Union Operation:

The Union operation merges two disjoint sets into one set.

When performing the Union operation, the algorithm must ensure that merging two sets does not create any cycles in the forest. The algorithm employs “union by rank” or “union by size” and path compression to achieve this.

• Time Complexity Analysis:

With the “union by rank” and path compression techniques, the time complexity of the Union operation becomes very efficient, approximately O(1). Due to these optimizations, the overall time complexity of Kruskal’s algorithm becomes dominated by the sorting of edges, making it O(E log E), as previously discussed

Worst Case Time Complexity of Kruskal’s Algorithm

In the context of Kruskal’s algorithm, the worst-case scenario arises when sorting the edges takes the longest time. The time complexity O(E+log E) for sorting is a solid guarantee of the algorithm’s efficiency in practice.

Let’s visualize the worst-case time complexity with a detailed example:

Suppose we have a connected graph G(V, E) with V vertices and E edges. Each edge has a unique weight such that all edge weights are distinct. In this scenario, the sorting of edges becomes the most time-consuming operation.

Kruskal algorithm example:

Consider the following graph G with five vertices and seven edges:

• The edges, sorted in ascending order of their weights, would be: (1, 2), (1, 3), (1, 4), (2, 4), (2, 5), (3, 4), (4, 5).
• Let’s analyze the time complexity of the algorithm’s steps:
• Sorting the edges: As we have E = 7 edges, the sorting operation would take O(7 log 7) ≈ O(7) time.
• Union-Find Data Structure: The Union operation and its optimizations take approximately O(1) time per edge, resulting in O(E) time for all edges.
• MST Construction: Since E = 7, the construction of the MST takes O(7) time.

Hence, the overall worst-case time complexity of Kruskal’s algorithm for this example would be:

Total Time Complexity ≈ O(7) O(7) O(7) ≈ O(21) ≈ O(E)

Best Case Time Complexity of Kruskal’s Algorithm

In the best-case scenario, Kruskal’s Algorithm’s time complexity is primarily determined by two operations: sorting the edges and performing Union-Find operations. Let’s break down the complexities of these operations:

• Sorting the Edges:

Kruskal’s Algorithm begins by sorting all the edges in the non-decreasing order of their weights.

The most commonly used sorting algorithms like Merge Sort, Quick Sort, or Heap Sort have a time complexity of O(E log E), where E is the number of edges in the graph.

• Performing Union-Find Operations:

Kruskal’s Algorithm performs a Union-Find operation for each edge to detect cycles efficiently.

In the best case, the Union-Find operations have a time complexity of approximately O(log V), where V is the number of vertices in the graph. Considering both the sorting and Union-Find operations, the best-case time complexity of Kruskal’s Algorithm is approximately O(E+log E E log V).
Here are a few real-world applications where the Kruskals algorithm proves useful in the scenarios:

1. Discovering the route between two locations in a road network, where roads represent edges and distances represent weights.
2. Determining the cost way to connecting power plants to cities with power lines as edges and costs as weights.
3. Finding how to connect computers to a server considering network connections as edges and bandwidths as weights.

In some situations, it is uncommon for a graph to be structured as a forest. However, in worst-case scenarios, the Kruskals algorithm maintains an efficient time complexity of O(E log E).

Now let’s explore some instances where the Kruskals algorithm does not achieve its best-case time complexity:

1. A graph that forms a cycle requires the algorithm to scrutinize each edge for cycle creation.
2. When faced with edges of weight within a graph, the algorithm needs to sort all these edges – a process that can take O(E log E) time.

Average Case Time Complexity of Kruskal’s Algorithm

In the average-case scenario, the time complexity analysis involves the probabilities of edge selections during the algorithm’s execution. To understand this better, let’s consider an example:

Example: 

Suppose we have a connected graph with V vertices and E edges, where each edge has a unique weight. The edges are sorted in non-decreasing order. When we start adding edges to the MST, we can classify them into three categories based on the result of the Union-Find operation:

• Edges that do not create a cycle are part of the MST (Safe Edges).
• Edges that create a cycle and are not part of the MST (Unsafe Edges).
• Edges that create a cycle but are still part of the MST (Critical Edges).

Now, let’s analyze the probabilities associated with these edge categories:

• Probability of Safe Edges (Ps):

When adding edges to the MST, the probability of selecting a safe edge is approximately 1/V.

This is because there is only one way to form the MST for each edge added to the MST, and it contains one more vertex.

Thus, the probability of selecting a safe edge is 1/V.

• Probability of Unsafe Edges (Pu):

The probability of choosing an unsafe edge is approximately 1/V.

Since there can be, at most, V-1 edges in the MST, the number of unsafe edges is V – (V-1) = 1.

• Probability of Critical Edges (Pc):

Critical edges are those where adding them to the MST will increase its edge count by 1 without creating a cycle.

The probability of selecting a critical edge is approximately (V-2)/V.

Now, let’s calculate the average time complexity of Kruskal’s Algorithm using these probabilities:

Average Time Complexity = Ps * Ts Pu * Tu Pc * Tc

Ts, Tu, and Tc are the time complexities of safe, unsafe, and critical edge selection.

As safe edges and unsafe edges require O(log V) time for Union-Find operations, and critical edges require O(log V) time as well, the average case time complexity can be approximated as O(E log V).

Space Complexity of Kruskal’s Algorithm

In addition to time complexity, understanding the space complexity of Kruskal’s Algorithm is essential to evaluate its efficiency in memory usage. The primary space-consuming factor in the algorithm is the Kruskal algorithm in the data structure used for Union-Find operations.

The Union-Find Data Structure typically requires O(V) space to store each vertex’s parent and rank information. Sorting the edges can be done in place without requiring additional space. Hence, the space complexity of Kruskal’s Algorithm is O(V) for the Union-Find Data Structure.

Kruskal algorithm Python uses a Union-Find data structure to detect cycles and create the MST efficiently. The steps involve sorting the edges in ascending order of their weights and then iteratively adding edges to the MST while ensuring that no cycles are formed. The algorithm continues until all vertices are included in the MST.

Kruskal’s algorithm in C follows the same logic as in Python, but it uses arrays and loops to handle data structures. The algorithm efficiently selects edges while avoiding cycles, ultimately forming the MST.

Kruskal’s Algorithm in (DAA) Design and Analysis of Algorithms: Design and Analysis often involves a theoretical explanation of Kruskal’s algorithm. It emphasizes its greedy nature and proves its correctness and optimality regarding the MST.

Conclusion

Kruskal’s algorithm offers a powerful and efficient solution to graph theory’s Minimum Spanning Tree problem. Its time complexity of O(E+log E) makes it an appealing choice for numerous real-world applications that involve network design, clustering, and transportation planning, among others. 

By understanding the time complexity and inner workings of Kruskal’s algorithm, students can grasp its beauty and practicality in solving complex problems. So, embrace the power of Kruskal’s algorithm, and let it guide you through the intriguing world of data structures! Learn these concepts via Graduate Certificate Programme in Data Science from upGrad.