# Boruvka’s algorithm | Greedy Algo-9

We have discussed following topics on Minimum Spanning Tree.

Applications of Minimum Spanning Tree Problem

Kruskal’s Minimum Spanning Tree Algorithm

Prim’s Minimum Spanning Tree Algorithm

In this post, Boruvka’s algorithm is discussed. Like Prim’s and Kruskal’s, Boruvka’s algorithm is also a Greedy algorithm. Below is complete algorithm.

1) Input is a connected, weighted and un-directed graph. 2) Initialize all vertices as individual components (or sets). 3) Initialize MST as empty. 4) While there are more than one components, do following for each component. a) Find the closest weight edge that connects this component to any other component. b) Add this closest edge to MST if not already added. 5) Return MST.

Below is the idea behind above algorithm (The idea is same as Prim’s MST algorithm). *A spanning tree means all vertices must be connected. So the two disjoint subsets (discussed above) of vertices must be connected to make a Spanning Tree. And they must be connected with the minimum weight edge to make it a Minimum Spanning Tree.*

Let us understand the algorithm with below example.

Initially MST is empty. Every vertex is singe component as highlighted in blue color in below diagram.

For every component, find the cheapest edge that connects it to some other component.

Component Cheapest Edge that connectsit to some other component{0} 0-1 {1} 0-1 {2} 2-8 {3} 2-3 {4} 3-4 {5} 5-6 {6} 6-7 {7} 6-7 {8} 2-8

The cheapest edges are highlighted with green color. Now MST becomes {0-1, 2-8, 2-3, 3-4, 5-6, 6-7}.

After above step, components are {{0,1}, {2,3,4,8}, {5,6,7}}. The components are encircled with blue color.

We again repeat the step, i.e., for every component, find the cheapest edge that connects it to some other component.

Component Cheapest Edge that connectsit to some other component{0,1} 1-2 (or 0-7) {2,3,4,8} 2-5 {5,6,7} 2-5

The cheapest edges are highlighted with green color. Now MST becomes {0-1, 2-8, 2-3, 3-4, 5-6, 6-7, 1-2, 2-5}

At this stage, there is only one component {0, 1, 2, 3, 4, 5, 6, 7, 8} which has all edges. Since there is only one component left, we stop and return MST.

**Implementation:** Below is implementation of above algorithm. The input graph is represented as a collection of edges and union-find data structure is used to keep track of components.

## Python

`# Boruvka's algorithm to find Minimum Spanning` `# Tree of a given connected, undirected and weighted graph` `from` `collections ` `import` `defaultdict` `#Class to represent a graph` `class` `Graph:` ` ` `def` `__init__(` `self` `,vertices):` ` ` `self` `.V` `=` `vertices ` `#No. of vertices` ` ` `self` `.graph ` `=` `[] ` `# default dictionary to store graph` ` ` ` ` ` ` `# function to add an edge to graph` ` ` `def` `addEdge(` `self` `,u,v,w):` ` ` `self` `.graph.append([u,v,w])` ` ` `# A utility function to find set of an element i` ` ` `# (uses path compression technique)` ` ` `def` `find(` `self` `, parent, i):` ` ` `if` `parent[i] ` `=` `=` `i:` ` ` `return` `i` ` ` `return` `self` `.find(parent, parent[i])` ` ` `# A function that does union of two sets of x and y` ` ` `# (uses union by rank)` ` ` `def` `union(` `self` `, parent, rank, x, y):` ` ` `xroot ` `=` `self` `.find(parent, x)` ` ` `yroot ` `=` `self` `.find(parent, y)` ` ` `# Attach smaller rank tree under root of high rank tree` ` ` `# (Union by Rank)` ` ` `if` `rank[xroot] < rank[yroot]:` ` ` `parent[xroot] ` `=` `yroot` ` ` `elif` `rank[xroot] > rank[yroot]:` ` ` `parent[yroot] ` `=` `xroot` ` ` `#If ranks are same, then make one as root and increment` ` ` `# its rank by one` ` ` `else` `:` ` ` `parent[yroot] ` `=` `xroot` ` ` `rank[xroot] ` `+` `=` `1` ` ` `# The main function to construct MST using Kruskal's algorithm` ` ` `def` `boruvkaMST(` `self` `):` ` ` `parent ` `=` `[]; rank ` `=` `[]; ` ` ` `# An array to store index of the cheapest edge of` ` ` `# subset. It store [u,v,w] for each component` ` ` `cheapest ` `=` `[]` ` ` `# Initially there are V different trees.` ` ` `# Finally there will be one tree that will be MST` ` ` `numTrees ` `=` `self` `.V` ` ` `MSTweight ` `=` `0` ` ` `# Create V subsets with single elements` ` ` `for` `node ` `in` `range` `(` `self` `.V):` ` ` `parent.append(node)` ` ` `rank.append(` `0` `)` ` ` `cheapest ` `=` `[` `-` `1` `] ` `*` `self` `.V` ` ` ` ` `# Keep combining components (or sets) until all` ` ` `# components are not combined into single MST` ` ` `while` `numTrees > ` `1` `:` ` ` `# Traverse through all edges and update` ` ` `# cheapest of every component` ` ` `for` `i ` `in` `range` `(` `len` `(` `self` `.graph)):` ` ` `# Find components (or sets) of two corners` ` ` `# of current edge` ` ` `u,v,w ` `=` `self` `.graph[i]` ` ` `set1 ` `=` `self` `.find(parent, u)` ` ` `set2 ` `=` `self` `.find(parent ,v)` ` ` `# If two corners of current edge belong to` ` ` `# same set, ignore current edge. Else check if ` ` ` `# current edge is closer to previous` ` ` `# cheapest edges of set1 and set2` ` ` `if` `set1 !` `=` `set2: ` ` ` ` ` `if` `cheapest[set1] ` `=` `=` `-` `1` `or` `cheapest[set1][` `2` `] > w :` ` ` `cheapest[set1] ` `=` `[u,v,w] ` ` ` `if` `cheapest[set2] ` `=` `=` `-` `1` `or` `cheapest[set2][` `2` `] > w :` ` ` `cheapest[set2] ` `=` `[u,v,w]` ` ` `# Consider the above picked cheapest edges and add them` ` ` `# to MST` ` ` `for` `node ` `in` `range` `(` `self` `.V):` ` ` `#Check if cheapest for current set exists` ` ` `if` `cheapest[node] !` `=` `-` `1` `:` ` ` `u,v,w ` `=` `cheapest[node]` ` ` `set1 ` `=` `self` `.find(parent, u)` ` ` `set2 ` `=` `self` `.find(parent ,v)` ` ` `if` `set1 !` `=` `set2 :` ` ` `MSTweight ` `+` `=` `w` ` ` `self` `.union(parent, rank, set1, set2)` ` ` `print` `(` `"Edge %d-%d with weight %d included in MST"` `%` `(u,v,w))` ` ` `numTrees ` `=` `numTrees ` `-` `1` ` ` ` ` `#reset cheapest array` ` ` `cheapest ` `=` `[` `-` `1` `] ` `*` `self` `.V` ` ` ` ` `print` `(` `"Weight of MST is %d"` `%` `MSTweight)` ` ` ` ` `g ` `=` `Graph(` `4` `)` `g.addEdge(` `0` `, ` `1` `, ` `10` `)` `g.addEdge(` `0` `, ` `2` `, ` `6` `)` `g.addEdge(` `0` `, ` `3` `, ` `5` `)` `g.addEdge(` `1` `, ` `3` `, ` `15` `)` `g.addEdge(` `2` `, ` `3` `, ` `4` `)` `g.boruvkaMST()` `#This code is contributed by Neelam Yadav` |

**Output**

Edge 0-3 with weight 5 included in MST Edge 0-1 with weight 10 included in MST Edge 2-3 with weight 4 included in MST Weight of MST is 19

**Interesting Facts about Boruvka’s algorithm:**

- Time Complexity of Boruvka’s algorithm is O(E log V) which is same as Kruskal’s and Prim’s algorithms.
- Boruvka’s algorithm is used as a step in a faster randomized algorithm that works in linear time O(E).
- Boruvka’s algorithm is the oldest minimum spanning tree algorithm was discovered by Boruuvka in 1926, long before computers even existed. The algorithm was published as a method of constructing an efficient electricity network.

**Exercise:**

The above code assumes that input graph is connected and it fails if a disconnected graph is given. Extend the above algorithm so that it works for a disconnected graph also and produces a forest.