Difference between Prim’s and Kruskal’s algorithm for MST
Last Updated :
27 May, 2024
Minimum Spanning Tree (MST) is a fundamental concept in graph theory and has various applications in network design, clustering, and optimization problems. Two of the most commonly used algorithms to find the MST of a graph are Prim’s and Kruskal’s algorithms. Although both algorithms achieve the same goal, they do so in different ways. In this article we are going to explore the differences between them which can help in choosing the right algorithm for specific types of graphs and applications.
Prim’s algorithm is a greedy algorithm that builds the MST incrementally. It starts with a single vertex and grows the MST one edge at a time, always choosing the smallest edge that connects a vertex in the MST to a vertex outside the MST.
Steps of Prim’s Algorithm:
- Initialization: Start with an arbitrary vertex and mark it as part of the MST.
- Edge Selection: From the set of edges that connect vertices in the MST to vertices outside the MST, select the edge with the minimum weight.
- Update: Add the selected edge and the connected vertex to the MST.
- Repeat: Repeat the edge selection and update steps until all vertices are included in the MST.
Prim’s algorithm is typically implemented using a priority queue to efficiently select the minimum weight edge at each step.
Kruskal’s algorithm is also a greedy algorithm but takes a different approach. It begins with all the vertices and no edges, and it adds edges one by one in increasing order of weight, ensuring no cycles are formed until the MST is complete.
Steps of Kruskal’s Algorithm:
- Initialization: Sort all the edges in the graph by their weight in non-decreasing order.
- Edge Selection: Starting from the smallest edge, add the edge to the MST if it doesn’t form a cycle with the already included edges.
- Cycle Detection: Use a union-find data structure to detect and prevent cycles.
- Repeat: Continue adding edges until the MST contains exactly (V-1) edges, where V is the number of vertices.
Key Differences Between Prim’s and Kruskal’s Algorithm for MST
Here is a table summarizing the key differences between Prim’s and Kruskal’s algorithms for finding the Minimum Spanning Tree (MST):
| Feature |
Prim’s Algorithm |
Kruskal’s Algorithm |
| Approach |
Vertex-based, grows the MST one vertex at a time |
Edge-based, adds edges in increasing order of weight |
| Data Structure |
Priority queue (min-heap) |
Union-Find data structure |
| Graph Representation |
Adjacency matrix or adjacency list |
Edge list |
| Initialization |
Starts from an arbitrary vertex |
Starts with all vertices as separate trees (forest) |
| Edge Selection |
Chooses the minimum weight edge from the connected vertices |
Chooses the minimum weight edge from all edges |
| Cycle Management |
Not explicitly managed; grows connected component |
Uses Union-Find to avoid cycles |
| Complexity |
O(V^2) for adjacency matrix, O(E + V log V) with a priority queue |
O(E log E) or O(E log V), due to edge sorting |
| Suitable for |
Dense graphs |
Sparse graphs |
| Implementation Complexity |
Relatively simpler in dense graphs |
More complex due to cycle management |
| Parallelism |
Difficult to parallelize |
Easier to parallelize edge sorting and union operations |
| Memory Usage |
More memory for priority queue |
Less memory if edges can be sorted externally |
| Example Use Cases |
Network design, clustering with dense connections |
Road networks, telecommunications with sparse connections |
| Starting Point |
Requires a starting vertex |
No specific starting point, operates on global edges |
| Optimal for |
Dense graphs where adjacency list is used |
Sparse graphs where edge list is efficient |
Conclusion
Prim’s and Kruskal’s algorithms are both powerful tools for finding the MST of a graph, each with its unique advantages. Prim’s algorithm is typically preferred for dense graphs, leveraging its efficient priority queue-based approach, while Kruskal’s algorithm excels in handling sparse graphs with its edge-sorting and union-find techniques. Understanding the structural differences and appropriate use cases for each algorithm ensures optimal performance in various graph-related problems.
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