Data Structures & Algorithms
Prim's Algorithm
Ace your coding interviews by mastering Prim's algorithm for Minimum Spanning Trees (MST). Our guide covers its greedy strategy, implementation with priority queues, and offers AI-powered practice.
Prim's Algorithm Implementation
Here is a Python implementation of Prim's algorithm using a priority queue (min-heap) to find the MST of a graph.
import heapqdefprims(graph, start_node): mst = [] visited = set([start_node]) edges = [ (weight, start_node, to_node)for to_node, weight in graph[start_node].items() ] heapq.heapify(edges) total_weight = 0while edges: weight, from_node, to_node = heapq.heappop(edges)if to_node not in visited: visited.add(to_node) mst.append((from_node, to_node, weight)) total_weight += weightfor next_node, next_weight in graph[to_node].items():if next_node not in visited: heapq.heappush(edges, (next_weight, to_node, next_node))return mst, total_weight
AI Coach Tip: Prim's algorithm is conceptually similar to Dijkstra's. Both use a priority queue to greedily select the next vertex. The key difference is the value stored in the priority queue: Prim's prioritizes the minimum edge weight to an unvisited neighbor, while Dijkstra's prioritizes the minimum total distance from the source.
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