Grokking Graph Algorithms for Coding Interviews
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Find Critical and Pseudo-Critical Edges in Minimum Spanning Tree (hard)
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Problem Statement

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Problem Statement

Given a weighted undirected graph with n vertices numbered from 0 to n-1, and an array edges where edges[i] = [a, b, weight] represents a bidirectional and weighted edge between nodes a and b.

A minimum spanning tree (MST) is a subset of the graph's edges that connects all vertices without cycles and with the smallest possible total edge weight.

An edge is critical if removing it would cause the MST's total weight to increase. An edge is pseudo-critical if it can be part of some MSTs but is not required in all MSTs.

Return a 2D array containing all the critical and pseudo-critical edges in the graph's MST.

Examples

Example 1

  • Input: n = 4, edges = [[0, 1, 1], [1, 2, 2], [2, 3, 3], [0, 3, 4], [1, 3, 5]]
  • Expected Output: [[0,1,2],[]]
Image
  • Justification: Edges [0, 1, 2] are critical as their removal increases the MST weight.

Example 2

  • Input: n = 4, edges = [[0, 1, 1], [1, 2, 1], [2, 3, 1], [0, 3, 1], [0, 2, 2]]
  • Expected Output: [[],[0,1,2,3]]
Image
  • Justification: Edges [0, 1, 2, 3] are pseudo-critical as they exists in some of MSTs.

Example 3

  • Input: n = 6, edges = [[0, 1, 2], [1, 2, 3], [2, 3, 1], [3, 4, 4], [4, 5, 5], [0, 5, 6], [1, 4, 2]]
  • Expected Output: [[0,1,2,4,6],[]]
  • Justification: Edges [0,1,2,4,6] are critical as their removal increases the MST weight.

Constraints:

  • 2 <= n <= 100
  • 1 <= edges.length <= min(200, n * (n - 1) / 2)
  • edges[i].length == 3
  • 0 <= a<sub>i</sub> < b<sub>i</sub> < n
  • 1 <= weight<sub>i</sub> <= 1000
  • All pairs (a<sub>i</sub>, b<sub>i</sub>) are distinct.

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Python3
Python3
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Problem Statement

Examples

Try it yourself