The time complexity of Kruskal's algorithm is O(ElogE) or O(ElogV), where E is the number of edges in the graph and V is the number of vertices. Here's a step-by-step breakdown:

1. Sorting of edges: This operation can be done in O(ElogE) time. If we have E edges, then the sort operation will take ElogE time.

2. Union-Find Algorithm: After sorting, we iterate through all edges and apply the find-union algorithm. The time complexity of this step is O(ElogV), as in the worst case, we may have to traverse all vertices for each edge.

So, the overall time complexity is O(ElogE) or O(ElogV), whichever is larger. In most cases, we can consider E as V^2, so ElogE is a good approximation. However, in a sparse graph, E can be close to V, and therefore, ElogV might be a more accurate estimate.

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The time complexity of Kruskal's algorithm is O(ElogE) or O(ElogV), where E is the number of edges in the graph and V is the number of vertices. Here's a step-by-step breakdown:

1. Sorting of edges: This operation can be done in O(ElogE) time. If we have E edges, then the sort operation will take ElogE time.

2. Union-Find Algorithm: After sorting, we iterate through all edges and apply the find-union algorithm. The time complexity of this step is O(ElogV), as in the worst case, we may have to traverse all vertices for each edge.

So, the overall time complexity is O(ElogE) or O(ElogV), whichever is larger. In most cases, we can consider E as V^2, so ElogE is a good approximation. However, in a sparse graph, E can be close to V, and therefore, ElogV might be a more accurate estimate.

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The time complexity of Kruskal's algorithm is O(ElogE) or O(ElogV), where E is the number of edges in the graph and V is the number of vertices. Here's a step-by-step breakdown:

1. Sorting of edges: This operation can be done in O(ElogE) time. If we have E edges, then the sort operation will take ElogE time.

2. Union-Find Algorithm: After sorting, we iterate through all edges and apply the find-union algorithm. The time complexity of this step is O(ElogV), as in the worst case, we may have to traverse all vertices for each edge.

So, the overall time complexity is O(ElogE) or O(ElogV), whichever is larger. In most cases, we can consider E as V^2, so ElogE is a good approximation. However, in a sparse graph, E can be close to V, and therefore, ElogV might be a more accurate estimate.

What is the time complexity of Kruskal’s algorithm?

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