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How to Solve Longest Common Subpath Problem
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Start Free for WindowsHard#1923
LeetCode ProblemLongest Common Subpath
There is a country of n cities numbered from 0 to n - 1. In this country, there is a road connecting every pair of cities. There are m friends numbered from 0 to m - 1 who are traveling through the country. Each one of them will take a path consisting of some cities. Each path is represented by an integer array that contains the visited cities in order. The path may contain a city more than once, but the same city will not be listed consecutively. Given an integer n and a 2D integer array paths where paths[i] is an integer array representing the path of the ith friend, return the length of the longest common subpath that is shared by every friend's path, or 0 if there is no common subpath at all. A subpath of a path is a contiguous sequence of cities within that path.
ArrayBinary SearchRolling HashSuffix ArrayHash Function
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Problem Breakdown
Understanding the Longest Common Subpath Problem
Let's break down this LeetCode problem and understand what makes it challenging in interview settings.
Problem Statement
There is a country of n cities numbered from 0 to n - 1. In this country, there is a road connecting every pair of cities. There are m friends numbered from 0 to m - 1 who are traveling through the country. Each one of them will take a path consisting of some cities. Each path is represented by an integer array that contains the visited cities in order. The path may contain a city more than once, but the same city will not be listed consecutively. Given an integer n and a 2D integer array paths where paths[i] is an integer array representing the path of the ith friend, return the length of the longest common subpath that is shared by every friend's path, or 0 if there is no common subpath at all. A subpath of a path is a contiguous sequence of cities within that path.
HardProblem #1923
LeetCodeLongest Common Subpath
Related Topics
ArrayBinary SearchRolling HashSuffix ArrayHash Function
How Phantom Code Helps
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Examples
# Example 1
Input
n = 5, paths = [[0,1,2,3,4], [2,3,4], [4,0,1,2,3]]
Output
2
# Example 2
Input
n = 3, paths = [[0],[1],[2]]
Output
0
# Example 3
Input
n = 5, paths = [[0,1,2,3,4], [4,3,2,1,0]]
Output
1
Constraints
1 <= n <= 105
m == paths.length
2 <= m <= 105
sum(paths[i].length) <= 105
0 <= paths[i][j] < n
The same city is not listed multiple times consecutively in paths[i].
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Solve Longest Common Subpath — There is a country of n cities numbered from 0 to n - 1. In this country, there ...
Here's the optimal approach using Array:
def solve(input):
# Optimal O(n) solution
return result
Time: O(n) | Space: O(n)
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Frequently Asked Questions
Common questions about solving Longest Common Subpath and using Phantom Code during coding interviews.
How does Phantom Code help me solve coding problems like Longest Common Subpath during interviews?
Phantom Code generates complete solutions instantly with proper complexity analysis, letting you focus on explaining your approach and demonstrating problem-solving skills rather than getting stuck on implementation details during high-pressure situations.
What if the interviewer asks follow-up questions or modifications to problems like Longest Common Subpath?
Phantom Code adapts in real-time. Screenshot the modified problem or use audio mode to capture the interviewer's follow-up, and get an updated solution within seconds — including edge cases and optimizations.
Can Phantom Code help me communicate my solution for problems like Longest Common Subpath?
Yes. Phantom Code provides step-by-step approach explanations with 3 progressive thoughts, so you can walk through your solution naturally. It also provides time and space complexity analysis to discuss trade-offs.
How does Phantom Code assist with different programming languages for Longest Common Subpath type problems?
Phantom Code supports 11 programming languages including Python, Java, C++, JavaScript, TypeScript, Go, Rust, Ruby, Swift, Kotlin, and C#. Set your preferred language and get idiomatic solutions.
What coding mistakes does Phantom Code help me avoid during Longest Common Subpath interviews?
Phantom Code catches common mistakes like off-by-one errors, missing edge cases, incorrect base conditions, and suboptimal approaches. It provides clean, well-structured code that handles all edge cases.
Is Phantom Code detectable when solving problems like Longest Common Subpath during live interviews?
No. Phantom Code runs as an invisible desktop overlay that doesn't appear in screen shares, recordings, or proctoring software. It works with Zoom, HackerRank, CodeSignal, CoderPad, and all major platforms.
How does Phantom Code help when I'm struggling with Longest Common Subpath style questions under pressure?
The AI provides structured guidance: first the approach (what algorithm/data structure to use and why), then the implementation with clean code, and finally complexity analysis. This helps you think clearly even under pressure.
Does Phantom Code work for the coding platforms used in Longest Common Subpath interviews?
Yes. Phantom Code works with all major coding platforms including LeetCode, HackerRank, CodeSignal, CoderPad, HackerEarth, and any browser-based coding environment.
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