LeetCode #2092 — HARD

Find All People With Secret

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 integer n indicating there are n people numbered from 0 to n - 1. You are also given a 0-indexed 2D integer array meetings where meetings[i] = [xi, yi, timei] indicates that person xi and person yi have a meeting at timei. A person may attend multiple meetings at the same time. Finally, you are given an integer firstPerson.

Person 0 has a secret and initially shares the secret with a person firstPerson at time 0. This secret is then shared every time a meeting takes place with a person that has the secret. More formally, for every meeting, if a person xi has the secret at timei, then they will share the secret with person yi, and vice versa.

The secrets are shared instantaneously. That is, a person may receive the secret and share it with people in other meetings within the same time frame.

Return a list of all the people that have the secret after all the meetings have taken place. You may return the answer in any order.

Example 1:

Input: n = 6, meetings = [[1,2,5],[2,3,8],[1,5,10]], firstPerson = 1
Output: [0,1,2,3,5]
Explanation:
At time 0, person 0 shares the secret with person 1.
At time 5, person 1 shares the secret with person 2.
At time 8, person 2 shares the secret with person 3.
At time 10, person 1 shares the secret with person 5.​​​​
Thus, people 0, 1, 2, 3, and 5 know the secret after all the meetings.

Example 2:

Input: n = 4, meetings = [[3,1,3],[1,2,2],[0,3,3]], firstPerson = 3
Output: [0,1,3]
Explanation:
At time 0, person 0 shares the secret with person 3.
At time 2, neither person 1 nor person 2 know the secret.
At time 3, person 3 shares the secret with person 0 and person 1.
Thus, people 0, 1, and 3 know the secret after all the meetings.

Example 3:

Input: n = 5, meetings = [[3,4,2],[1,2,1],[2,3,1]], firstPerson = 1
Output: [0,1,2,3,4]
Explanation:
At time 0, person 0 shares the secret with person 1.
At time 1, person 1 shares the secret with person 2, and person 2 shares the secret with person 3.
Note that person 2 can share the secret at the same time as receiving it.
At time 2, person 3 shares the secret with person 4.
Thus, people 0, 1, 2, 3, and 4 know the secret after all the meetings.

Constraints:

  • 2 <= n <= 105
  • 1 <= meetings.length <= 105
  • meetings[i].length == 3
  • 0 <= xi, yi <= n - 1
  • xi != yi
  • 1 <= timei <= 105
  • 1 <= firstPerson <= n - 1
Patterns Used

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 integer n indicating there are n people numbered from 0 to n - 1. You are also given a 0-indexed 2D integer array meetings where meetings[i] = [xi, yi, timei] indicates that person xi and person yi have a meeting at timei. A person may attend multiple meetings at the same time. Finally, you are given an integer firstPerson. Person 0 has a secret and initially shares the secret with a person firstPerson at time 0. This secret is then shared every time a meeting takes place with a person that has the secret. More formally, for every meeting, if a person xi has the secret at timei, then they will share the secret with person yi, and vice versa. The secrets are shared instantaneously. That is, a person may receive the secret and share it with people in other meetings within the same time frame. Return a list of all the people that have the secret after all the meetings have

Baseline thinking

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

Pattern signal: Union-Find

Example 1

6
[[1,2,5],[2,3,8],[1,5,10]]
1

Example 2

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

Example 3

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

Related Problems

  • Reachable Nodes In Subdivided Graph (reachable-nodes-in-subdivided-graph)
Step 02

Core Insight

What unlocks the optimal approach

  • Could you model all the meetings happening at the same time as a graph?
  • What data structure can you use to efficiently share the secret?
  • You can use the union-find data structure to quickly determine who knows the secret and share the secret.
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 #2092: Find All People With Secret
class Solution {
    public List<Integer> findAllPeople(int n, int[][] meetings, int firstPerson) {
        boolean[] vis = new boolean[n];
        vis[0] = true;
        vis[firstPerson] = true;
        int m = meetings.length;
        Arrays.sort(meetings, Comparator.comparingInt(a -> a[2]));
        for (int i = 0; i < m;) {
            int j = i;
            for (; j + 1 < m && meetings[j + 1][2] == meetings[i][2];) {
                ++j;
            }
            Map<Integer, List<Integer>> g = new HashMap<>();
            Set<Integer> s = new HashSet<>();
            for (int k = i; k <= j; ++k) {
                int x = meetings[k][0], y = meetings[k][1];
                g.computeIfAbsent(x, key -> new ArrayList<>()).add(y);
                g.computeIfAbsent(y, key -> new ArrayList<>()).add(x);
                s.add(x);
                s.add(y);
            }
            Deque<Integer> q = new ArrayDeque<>();
            for (int u : s) {
                if (vis[u]) {
                    q.offer(u);
                }
            }
            while (!q.isEmpty()) {
                int u = q.poll();
                for (int v : g.getOrDefault(u, List.of())) {
                    if (!vis[v]) {
                        vis[v] = true;
                        q.offer(v);
                    }
                }
            }
            i = j + 1;
        }
        List<Integer> ans = new ArrayList<>();
        for (int i = 0; i < n; ++i) {
            if (vis[i]) {
                ans.add(i);
            }
        }
        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 + n)
Space
O(n)

Approach Breakdown

BRUTE FORCE
O(n²) time
O(n) space

Track components with a list or adjacency matrix. Each union operation may need to update all n elements’ component labels, giving O(n) per union. For n union operations total: O(n²). Find is O(1) with direct lookup, but union dominates.

UNION-FIND
O(α(n)) time
O(n) space

With path compression and union by rank, each find/union operation takes O(α(n)) amortized time, where α is the inverse Ackermann function — effectively constant. Space is O(n) for the parent and rank arrays. For m operations on n elements: O(m × α(n)) total.

Shortcut: Union-Find with path compression + rank → O(α(n)) per operation ≈ O(1). Just say “nearly constant.”
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.