LeetCode #3123 — HARD

Find Edges in Shortest Paths

Break down a hard problem into reliable checkpoints, edge-case handling, and complexity trade-offs.

Solve on LeetCode
The Problem

Problem Statement

You are given an undirected weighted graph of n nodes numbered from 0 to n - 1. The graph consists of m edges represented by a 2D array edges, where edges[i] = [ai, bi, wi] indicates that there is an edge between nodes ai and bi with weight wi.

Consider all the shortest paths from node 0 to node n - 1 in the graph. You need to find a boolean array answer where answer[i] is true if the edge edges[i] is part of at least one shortest path. Otherwise, answer[i] is false.

Return the array answer.

Note that the graph may not be connected.

Example 1:

Input: n = 6, edges = [[0,1,4],[0,2,1],[1,3,2],[1,4,3],[1,5,1],[2,3,1],[3,5,3],[4,5,2]]

Output: [true,true,true,false,true,true,true,false]

Explanation:

The following are all the shortest paths between nodes 0 and 5:

  • The path 0 -> 1 -> 5: The sum of weights is 4 + 1 = 5.
  • The path 0 -> 2 -> 3 -> 5: The sum of weights is 1 + 1 + 3 = 5.
  • The path 0 -> 2 -> 3 -> 1 -> 5: The sum of weights is 1 + 1 + 2 + 1 = 5.

Example 2:

Input: n = 4, edges = [[2,0,1],[0,1,1],[0,3,4],[3,2,2]]

Output: [true,false,false,true]

Explanation:

There is one shortest path between nodes 0 and 3, which is the path 0 -> 2 -> 3 with the sum of weights 1 + 2 = 3.

Constraints:

  • 2 <= n <= 5 * 104
  • m == edges.length
  • 1 <= m <= min(5 * 104, n * (n - 1) / 2)
  • 0 <= ai, bi < n
  • ai != bi
  • 1 <= wi <= 105
  • There are no repeated edges.

Roadmap

  1. Brute Force Baseline
  2. Core Insight
  3. Algorithm Walkthrough
  4. Edge Cases
  5. Full Annotated Code
  6. Interactive Study Demo
  7. Complexity Analysis
Step 01

Brute Force Baseline

Problem summary: You are given an undirected weighted graph of n nodes numbered from 0 to n - 1. The graph consists of m edges represented by a 2D array edges, where edges[i] = [ai, bi, wi] indicates that there is an edge between nodes ai and bi with weight wi. Consider all the shortest paths from node 0 to node n - 1 in the graph. You need to find a boolean array answer where answer[i] is true if the edge edges[i] is part of at least one shortest path. Otherwise, answer[i] is false. Return the array answer. Note that the graph may not be connected.

Baseline thinking

Start with the most direct exhaustive search. That gives a correctness anchor before optimizing.

Pattern signal: General problem-solving

Example 1

6
[[0,1,4],[0,2,1],[1,3,2],[1,4,3],[1,5,1],[2,3,1],[3,5,3],[4,5,2]]

Example 2

4
[[2,0,1],[0,1,1],[0,3,4],[3,2,2]]
Step 02

Core Insight

What unlocks the optimal approach

  • Find all the shortest paths starting from nodes 0 and <code>n - 1</code> to all other nodes.
  • How to use the above calculated shortest paths to check if an edge is part of at least one shortest path from 0 to <code>n - 1</code>?
Interview move: turn each hint into an invariant you can check after every iteration/recursion step.
Step 03

Algorithm Walkthrough

Iteration Checklist

  1. Define state (indices, window, stack, map, DP cell, or recursion frame).
  2. Apply one transition step and update the invariant.
  3. Record answer candidate when condition is met.
  4. Continue until all input is consumed.
Use the first example testcase as your mental trace to verify each transition.
Step 04

Edge Cases

Minimum Input
Single element / shortest valid input
Validate boundary behavior before entering the main loop or recursion.
Duplicates & Repeats
Repeated values / repeated states
Decide whether duplicates should be merged, skipped, or counted explicitly.
Extreme Constraints
Largest constraint values
Re-check complexity target against constraints to avoid time-limit issues.
Invalid / Corner Shape
Empty collections, zeros, or disconnected structures
Handle special-case structure before the core algorithm path.
Step 05

Full Annotated Code

Source-backed implementations are provided below for direct study and interview prep.

// Accepted solution for LeetCode #3123: Find Edges in Shortest Paths
class Solution {
    public boolean[] findAnswer(int n, int[][] edges) {
        List<int[]>[] g = new List[n];
        Arrays.setAll(g, k -> new ArrayList<>());
        int m = edges.length;
        for (int i = 0; i < m; ++i) {
            int a = edges[i][0], b = edges[i][1], w = edges[i][2];
            g[a].add(new int[] {b, w, i});
            g[b].add(new int[] {a, w, i});
        }
        int[] dist = new int[n];
        final int inf = 1 << 30;
        Arrays.fill(dist, inf);
        dist[0] = 0;
        PriorityQueue<int[]> pq = new PriorityQueue<>((a, b) -> a[0] - b[0]);
        pq.offer(new int[] {0, 0});
        while (!pq.isEmpty()) {
            var p = pq.poll();
            int da = p[0], a = p[1];
            if (da > dist[a]) {
                continue;
            }
            for (var e : g[a]) {
                int b = e[0], w = e[1];
                if (dist[b] > dist[a] + w) {
                    dist[b] = dist[a] + w;
                    pq.offer(new int[] {dist[b], b});
                }
            }
        }
        boolean[] ans = new boolean[m];
        if (dist[n - 1] == inf) {
            return ans;
        }
        Deque<Integer> q = new ArrayDeque<>();
        q.offer(n - 1);
        while (!q.isEmpty()) {
            int a = q.poll();
            for (var e : g[a]) {
                int b = e[0], w = e[1], i = e[2];
                if (dist[a] == dist[b] + w) {
                    ans[i] = true;
                    q.offer(b);
                }
            }
        }
        return ans;
    }
}
Step 06

Interactive Study Demo

Use this to step through a reusable interview workflow for this problem.

Press Step or Run All to begin.
Step 07

Complexity Analysis

Time
O(m × log m)
Space
O(n + m)

Approach Breakdown

BRUTE FORCE
O(n²) time
O(1) space

Two nested loops check every pair or subarray. The outer loop fixes a starting point, the inner loop extends or searches. For n elements this gives up to n²/2 operations. No extra space, but the quadratic time is prohibitive for large inputs.

OPTIMIZED
O(n) time
O(1) space

Most array problems have an O(n²) brute force (nested loops) and an O(n) optimal (single pass with clever state tracking). The key is identifying what information to maintain as you scan: a running max, a prefix sum, a hash map of seen values, or two pointers.

Shortcut: If you are using nested loops on an array, there is almost always an O(n) solution. Look for the right auxiliary state.
Coach Notes

Common Mistakes

Review these before coding to avoid predictable interview regressions.

Off-by-one on range boundaries

Wrong move: Loop endpoints miss first/last candidate.

Usually fails on: Fails on minimal arrays and exact-boundary answers.

Fix: Re-derive loops from inclusive/exclusive ranges before coding.