How do you solve Analyze Organization Hierarchy on LeetCode?

Analyze Organization Hierarchy (LeetCode #3482) can be solved using Brute Force or Optimal Solution. The optimal approach is Optimal Solution with O(n) time complexity and O(n) space complexity. Key insight: Understanding tree structures is crucial for hierarchy problems.

What is the time complexity of Analyze Organization Hierarchy?

The optimal solution for Analyze Organization Hierarchy runs in O(n) time and uses O(n) space. The complexity is linear as we traverse each employee once to build the hierarchy and calculate metrics.

What is the brute force solution for Analyze Organization Hierarchy?

The brute force approach for Analyze Organization Hierarchy is Brute Force, with O(n²) time complexity and O(1) space complexity. This approach involves checking each employee against all others to determine levels, team sizes, and salary budgets. It's straightforward but inefficient. The optimal Optimal Solution approach improves this to O(n).

What algorithm or data structure is used to solve Analyze Organization Hierarchy?

Analyze Organization Hierarchy uses the following concepts: Database. The recommended patterns to study are: Hash Map, Array.

What are the interview tips for Analyze Organization Hierarchy?

Clarify the requirements and constraints upfront. Discuss your thought process and approach before coding. Test your solution with edge cases.

What are common mistakes when solving Analyze Organization Hierarchy?

Neglecting to account for indirect reports. Incorrectly calculating levels by not considering the manager hierarchy.

#3482

Analyze Organization Hierarchy

Hard

DatabaseHash MapArray

LeetCode ↗

Approaches

Brute ForceOptimal

Complexity Comparison

	Brute Force	Optimal Solution★
Time	O(n²)	O(n)
Space	O(1)	O(n)

💡

Intuition

Time O(n)Space O(n)

Using a single pass to build a hierarchy tree allows us to calculate levels, team sizes, and salary budgets efficiently.

⚙️

Algorithm

3 steps

1Step 1: Create a mapping of employees to their direct reports.
2Step 2: Perform a depth-first search (DFS) to calculate levels, team sizes, and salary budgets in one traversal.
3Step 3: Store results in a structured format for output.

solution.py11 lines

1def analyze_hierarchy(employees):
2    from collections import defaultdict
3    tree = defaultdict(list)
4    for emp in employees:
5        tree[emp.manager_id].append(emp)
6    def dfs(emp):
7        level = 1 if emp.manager_id is None else dfs(emp.manager_id) + 1
8        size = sum(dfs(child) for child in tree[emp.employee_id]) + 1
9        budget = emp.salary + sum(dfs(child) for child in tree[emp.employee_id])
10        return level, size, budget
11    return [dfs(emp) for emp in employees]

ℹ

Complexity note: The complexity is linear as we traverse each employee once to build the hierarchy and calculate metrics.

1Understanding tree structures is crucial for hierarchy problems.
2Recursive depth-first search can simplify complex aggregations.

Solutions and explanations are original Tejav content. Problem titles © LeetCode — use the LeetCode button above for the full problem statement.