What is the time complexity of Employee Importance?

The optimal solution has a time complexity of O(n).

What data structures are used to solve Employee Importance?

This problem uses Array, Hash Table, Tree, Depth-First Search, Breadth-First Search. Understanding these concepts is essential for solving this Medium problem efficiently.

What are the key insights for solving Employee Importance?

Insight 1: The employee-subordinate relationship forms a tree structure, where each employee can be seen as a node and their subordinates as child nodes. Insight 2: Depth-First Search (DFS) is a suitable algorithm for traversing this tree and summing the importance values.

Which companies ask LeetCode 690. Employee Importance in interviews?

This problem is frequently asked at Rippling, Robinhood.

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Employee Importance - LeetCode 690 Solution

Employee Importance - Complete Solution Guide

Employee Importance is LeetCode problem 690, a Medium level challenge. This complete guide provides step-by-step explanations, multiple solution approaches, and optimized code in python3, java, cpp, c.

Problem Statement

You have a data structure of employee information, including the employee's unique ID, importance value, and direct subordinates' IDs. You are given an array of employees employees where: employees[i].id is the ID of the i th employee. employees[i].importance is the importance value of the i th employee. employees[i].subordinates is a list of the IDs of the direct subordinates of the i th employee. Given an integer id that represents an employee's ID, return the total importance value of this em

Detailed Explanation

The problem requires calculating the total importance of an employee and all their subordinates, both direct and indirect. Each employee has a unique ID, an importance value, and a list of IDs representing their direct subordinates. The input is a list of employee objects and a target employee ID. The output is the sum of the importance values of the target employee and all of their subordinates, forming a tree-like structure.

Solution Approach

The solution uses Depth-First Search (DFS) to traverse the employee hierarchy and calculate the total importance. A hash map is first created to store the employees, using their IDs as keys, to enable fast lookup of employee information. The DFS algorithm starts at the target employee and recursively explores their subordinates, summing up their importance values. The base case for the recursion is when an employee has no subordinates or all subordinates have been visited.

Step-by-Step Algorithm

Step 1: Create a hash map (employee_map) to store employees with their IDs as keys. Iterate through the input list of employees and populate the hash map.
Step 2: Define a recursive DFS function (dfs) that takes an employee ID as input.
Step 3: Inside the DFS function, retrieve the employee object from the hash map using the employee ID.
Step 4: Initialize total_importance with the importance value of the current employee.
Step 5: Iterate through the subordinate IDs of the current employee.
Step 6: For each subordinate ID, recursively call the DFS function and add the returned importance value to total_importance.
Step 7: Return total_importance from the DFS function.
Step 8: Call the DFS function with the initial target employee ID and return the result.

Key Insights

Insight 1: The employee-subordinate relationship forms a tree structure, where each employee can be seen as a node and their subordinates as child nodes.
Insight 2: Depth-First Search (DFS) is a suitable algorithm for traversing this tree and summing the importance values.
Insight 3: Using a hash map to store employees keyed by their IDs allows for efficient retrieval of employee data during DFS.

Complexity Analysis

Time Complexity: O(n)

Space Complexity: O(n)

Topics

This problem involves: Array, Hash Table, Tree, Depth-First Search, Breadth-First Search.

Companies

Asked at: Rippling, Robinhood.

Employee Importance | LeetCode 690

Medium

Array Hash Table Tree Depth-First Search Breadth-First Search

Time: O(n)Space: O(n)

Solution Code

Available in:

/*
// Definition for Employee.
class Employee {
    public int id;
    public int importance;
    public List<Integer> subordinates;
};
*/

class Solution {
    public int getImportance(List<Employee> employees, int id) {
        Map<Integer, Employee> employeeMap = new HashMap<>();
        for (Employee e : employees) {
            employeeMap.put(e.id, e);
        }

        return dfs(employeeMap, id);
    }

    private int dfs(Map<Integer, Employee> employeeMap, int employeeId) {
        Employee employee = employeeMap.get(employeeId);
        int totalImportance = employee.importance;
        if (employee.subordinates != null) {
            for (int subordinateId : employee.subordinates) {
                totalImportance += dfs(employeeMap, subordinateId);
            }
        }
        return totalImportance;
    }
}

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Problem Info

Description

Problem 690: Employee Importance - Detailed explanation and solution approaches.

Topics

Array Hash Table Tree Depth-First Search Breadth-First Search

Companies

RipplingRobinhood

Stats

Difficulty:Medium

Languages:4

Companies:2

Problem #:690

Problem Explanation

💡

Think Before You Code

🤔 Ask Yourself

→What is the input? What output do I need?
→Can I solve a smaller version of this problem first?
→What patterns have I seen in similar problems?

✏️ Try This First

Before looking at the solution, try to solve it on paper with a simple example!

🚀 Try on LeetCode

Solution Approach

📋

Step-by-Step Solution

8 steps

1
Step 1: Create a hash map (employee_map) to store employees with their IDs as keys. Iterate through the input list of employees and populate the hash map.
2
Step 2: Define a recursive DFS function (dfs) that takes an employee ID as input.
3
Step 3: Inside the DFS function, retrieve the employee object from the hash map using the employee ID.
4
Step 4: Initialize total_importance with the importance value of the current employee.
5
Step 5: Iterate through the subordinate IDs of the current employee.
6
Step 6: For each subordinate ID, recursively call the DFS function and add the returned importance value to total_importance.
7
Step 7: Return total_importance from the DFS function.
8
Step 8: Call the DFS function with the initial target employee ID and return the result.

💡

Pro Tip for Students

Try tracing through each step with a small example on paper before coding. Understanding "why" each step happens is more important than memorizing the code!

⚡

Complexity Analysis

⏱️

Time Complexity

O(n)

👍 Linear - Good! Visits each element once.

💾

Space Complexity

O(n)

📦 Linear space - Memory grows with input size.

📝

Why?

The time complexity is O(n) because building the hashmap takes O(n) time and the DFS traversal visits each employee at most once. The space complexity is O(n) due to the hashmap storing employee objects and the potential call stack depth of the DFS in the worst-case scenario, forming a linear chain of subordinates.

📖New to Big O? Click to learn more

Big O Notation - Quick Guide

O(1)Best - Instant

O(log n)Excellent - Binary Search

O(n)Good - One loop

O(n log n)OK - Sorting

O(n²)Slow - Nested loops

O(2ⁿ)Very Slow - Recursion

Alternative Approaches

Breadth-First Search (BFS)

Instead of DFS, BFS can also be used to traverse the employee hierarchy. It involves using a queue to store the employees to be visited and iteratively processing each employee and their subordinates.

Time: O(n)Space: O(n)

Trade-offs: BFS and DFS have similar time complexities for this problem. BFS may use slightly more memory in some cases due to the queue potentially holding a larger number of employees at once, especially in wider tree structures. DFS may be more space efficient in deep, skewed trees due to a smaller call stack compared to the number of nodes enqueued in BFS. The choice between BFS and DFS is a matter of preference in this particular scenario, and the problem lends itself naturally to a recursive DFS implementation.

✨

Key Insights

1Insight 1: The employee-subordinate relationship forms a tree structure, where each employee can be seen as a node and their subordinates as child nodes.
2Insight 2: Depth-First Search (DFS) is a suitable algorithm for traversing this tree and summing the importance values.
3Insight 3: Using a hash map to store employees keyed by their IDs allows for efficient retrieval of employee data during DFS.

📚

Key Terms Explained

For Students

Array

A collection of items stored at contiguous memory locations. Like a row of boxes.

Hash Table

A data structure that maps keys to values for quick lookup. Like a dictionary!

Tree

Hierarchical structure with nodes. Like a family tree!

Edge Cases

Edge case 1: Employee not found. The C implementation explicitly handles the case where an employee ID might not exist. Other implementations, based on the problem statement, assume valid IDs are provided. If the ID is invalid, hash map lookups in other languages may result in null exceptions. However the constraints specify valid IDs, so no handling is needed.
Edge case 2: Empty subordinate list. The code correctly handles employees with no subordinates as the loop iterating through the subordinate IDs will not execute, and only the employee's own importance will be returned.

⚠️

Common Mistakes to Avoid

✗Common mistake 1: Not using a hash map for employee lookup. Without a hash map, finding an employee by ID would require iterating through the entire list of employees, resulting in O(n^2) time complexity.

✗Common mistake 2: Not handling the base case for recursion in DFS. Failing to handle the case where an employee has no subordinates or all subordinates have been processed leads to infinite recursion.

✅

Learning Tip

Every mistake is a learning opportunity! When you make an error, understand why it happened before moving on.

🌍

Where is This Used in Real Life?

💼Real-world scenario 1: Organizational hierarchy. Calculating the total budget or resource allocation for a manager and their entire team, where employees represent nodes in a tree and importance values represent budget or resources.

🏦Real-world scenario 2: Social network influence analysis. Determining the total reach or influence of a user and their followers, where users represent nodes and importance values represent influence scores.

💡Understanding real-world applications helps you see the "why" behind algorithms. It's not just about passing interviews—these concepts power the apps and websites you use every day!

Author's Notes

💡 Key Insight

When dealing with arrays, always consider if sorting would simplify the problem. Many array problems have O(n) solutions using hash maps or two-pointer technique.

🎯 Interview Tip

Medium problems are the bread and butter of technical interviews. Practice explaining your approach before coding.

⚠️ Watch Out

Off-by-one errors are the most common mistake. Double-check your loop bounds and array indices.

👨‍💻 From Experience

This problem pattern appears frequently in real codebases. I've used similar logic when building production systems.

Written by Saswata Mondal

Software Engineer • Algorithms Enthusiast • Interview Coach

👨‍💻

About the Author

👨‍💻

Saswata MondalAuthor400+ LC Problems

B.Tech CSE, IEM Kolkata | LeetCode Rating: 1672 (Top 15%)

Certified: IBM Java Developer, NPTEL Programming in Java

Last updated: October 29, 2024LeetCode Profile GitHub

This solution has been crafted based on my experience solving 400+ LeetCode problems. Each explanation is designed to help you understand the underlying concepts, not just memorize the code.

Community Tips & Insights

Don't forget to handle the edge case when the input is empty. Many candidates miss this in interviews.

@DevExpert92Jul 13

The key insight here is recognizing the pattern. Once you see it, the solution becomes straightforward.

@AlgoMasterJul 6

❓

Frequently Asked Questions

What is the most efficient way to solve array problems?

Most array problems can be solved efficiently using techniques like two-pointer approach, sliding window, or hash maps. The key is identifying whether the array is sorted (enabling binary search) or if tracking seen elements (using hash maps) can help achieve O(n) time complexity.

What are the trade-offs of using a hash table?

Hash tables provide O(1) average-case lookup, insertion, and deletion, making them ideal for frequency counting and duplicate detection. However, they use O(n) extra space and may degrade to O(n) time in worst-case scenarios with hash collisions. Always consider if the space trade-off is acceptable.

What techniques are commonly tested in medium-difficulty problems?

Medium problems often require combining multiple techniques: two-pointer with hash maps, BFS/DFS with state tracking, dynamic programming with optimization, or divide-and-conquer approaches. They test your ability to recognize patterns and choose the right combination of techniques.

How should I practice algorithm problems effectively?

Focus on understanding patterns rather than memorizing solutions. After solving a problem, review optimal solutions and understand the intuition. Group similar problems to recognize patterns. Practice explaining your approach out loud. Review problems after a few days to test retention.

What is the optimal approach for "Employee Importance"?

For this medium-level Array problem, the key is to identify the core pattern. Analyze the input constraints to determine acceptable time complexity. Consider whether hash table techniques can optimize the solution. Always handle edge cases and validate your approach with examples before coding.

🤖 Need help with this problem?