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Implementation of Kruskal's Algorithm

Explore how to implement Kruskal's algorithm for finding minimum spanning trees in graphs efficiently. Learn to use the Disjoint Set Union data structure to manage connected components, check if vertices belong to the same set, and merge sets while processing edges. Understand the algorithm's runtime and how sorting edges and union-find operations contribute to its performance.

Implementation Notes

During Kruskal’s algorithm, we’ll iteratively build up a minimum spanning tree TT by processing the edges one by one. For each edge e=(u,v)e = (u, v) we need to check whether uu and vv are already connected in TT. The main challenge of implementing Kruskal is performing it efficiently.

The naive approaches, such as running a BFS from uu to vv for each edge, would be quite costly. The key component in speeding up the algorithm is using the correct data structure, which is called Disjoint-Set-Union (DSU) or Union-Find.

As the name suggests, a DSU data structure stores disjoint sets. In our case, it will store the connected components of our current graph TT. Each set has a representative. We can efficiently check whether two elements are in the same set by comparing their representatives.

Here is an illustration of a DSU data structure containing the three disjoint sets {a,b ...