Borůvka’s Algorithm: An In-Depth Guide to a Powerful Graph Algorithm

Borůvka’s Algorithm is a powerful graph algorithm that has applications in various fields, including network design, clustering, and computer science. In this comprehensive guide, we will dive deep into the workings of Borůvka’s Algorithm, its history, and practical implementations. We will also provide code examples to help you understand how to use this algorithm effectively.

Understanding Borůvka’s Algorithm

Borůvka’s Algorithm, named after Otakar Borůvka, is a graph algorithm used for finding minimum spanning trees in a graph. A minimum spanning tree is a subset of the edges of a connected graph that connects all the vertices with the minimum possible total edge weight. It is widely employed in network design, where the goal is to connect a set of locations with the least cost.

The algorithm works by iteratively selecting the edges that have the minimum weight for each connected component of the graph. These selected edges are then added to the minimum spanning tree. Borůvka’s Algorithm continues this process until all vertices are connected, resulting in a minimum spanning tree for the entire graph.

History and Development

Borůvka’s Algorithm was originally developed by Otakar Borůvka in 1926 and was primarily used in the field of electrical circuit design. However, its applications have expanded over the years, and it is now widely employed in computer science, telecommunications, and transportation network design.

Practical Implementation

Let’s explore a practical implementation of Borůvka’s Algo in Python. In this example, we will use a graph represented as an adjacency list, and we will find the minimum spanning tree of the graph.

class Graph:
    def __init__(self, vertices):
        self.V = vertices
        self.graph = []

    def add_edge(self, u, v, w):
        self.graph.append([u, v, w])

    def boruvka_mst(self):
        parent = [-1] * self.V
        cheapest = [-1] * self.V
        num_trees = self.V
        mst_weight = 0

        while num_trees > 1:
            cheapest_edge = [float('inf')] * self.V

            for i in range(len(self.graph)):
                u, v, w = self.graph[i]
                set1 = self.find(parent, u)
                set2 = self.find(parent, v)

                if set1 != set2:
                    if cheapest_edge[set1] == -1 or cheapest_edge[set1][2] > w:
                        cheapest_edge[set1] = [u, v, w]
                    if cheapest_edge[set2] == -1 or cheapest_edge[set2][2] > w:
                        cheapest_edge[set2] = [u, v, w]

            for node in range(self.V):
                if cheapest_edge[node] != -1:
                    u, v, w = cheapest_edge[node]
                    set1 = self.find(parent, u)
                    set2 = self.find(parent, v)

                    if set1 != set2:
                        mst_weight += w
                        parent[set1] = set2
                        cheapest[node] = -1
                        num_trees -= 1

            # Print the MST edges
            for node in range(self.V):
                if cheapest_edge[node] != -1:
                    u, v, w = cheapest_edge[node]
                    print(f"Edge ({u}-{v}) weight: {w}")

        print(f"Minimum Spanning Tree Weight: {mst_weight}")

    def find(self, parent, i):
        if parent[i] == -1:
            return i
        return self.find(parent, parent[i])

g = Graph(4)
g.add_edge(0, 1, 10)
g.add_edge(0, 2, 6)
g.add_edge(0, 3, 5)
g.add_edge(1, 3, 15)
g.add_edge(2, 3, 4)

Borůvka's Algorithm
Borůvka’s Algo

Links to External Resources

Internal Links

This concludes our in-depth exploration of Borůvka’s Algorithm. It is a versatile algorithm with a wide range of applications in various fields. By understanding its principles and implementations, you can leverage its power to solve complex problems efficiently.

  1. Wikipedia: Wikipedia provides an extensive overview of Borůvka’s Algo, including its history, applications, and variations.
  2. GeeksforGeeks: GeeksforGeeks offers detailed explanations and code implementations of Borůvka’s Algo, making it a valuable resource for programmers and computer science enthusiasts.
  3. provides a comprehensive guide to Borůvka’s Algorithm, including step-by-step explanations and visualizations to help you grasp the concepts.
  4. YouTube: You can also watch video tutorials on Borůvka’s Algo on YouTube to gain a visual understanding of how the algorithm works.

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