You are given a data structure of employee information, which includes the employee's unique id, his importance value and his direct subordinates' id.
For example, employee 1 is the leader of employee 2, and employee 2 is the leader of employee 3. They have importance value 15, 10 and 5, respectively. Then employee 1 has a data structure like [1, 15, [2]], and employee 2 has [2, 10, [3]], and employee 3 has [3, 5, []]. Note that although employee 3 is also a subordinate of employee 1, the relationship is not direct.
Now given the employee information of a company, and an employee id, you need to return the total importance value of this employee and all his subordinates.
Example 1:
Input: [[1, 5, [2, 3]], [2, 3, []], [3, 3, []]], 1
Output: 11
Explanation:
Employee 1 has importance value 5, and he has two direct subordinates: employee 2 and employee 3. They both have importance value 3. So the total importance value of employee 1 is 5 + 3 + 3 = 11.
Note: One employee has at most one direct leader and may have several subordinates. The maximum number of employees won't exceed 2000.
The Idea: DFS and accumulate the graph weights.
Complexity: O(N) time and space
# Employee infoclassEmployee:def__init__(self,id,importance,subordinates):# It's the unique id of each node.# unique id of this employee self.id =id# the importance value of this employee self.importance = importance# the id of direct subordinates self.subordinates = subordinatesdefgetImportance(self,employees,id):""" :type employees: Employee :type id: int :rtype: int """ graph ={employee.id:{'weight':employee.importance,'subordinates':employee.subordinates}for employee in employees} visited =set()defdfs(root): visited.add(root) subordinates = graph[root]['subordinates']ifnot subordinates:return graph[root]['weight']else:sum= graph[root]['weight']for employee in subordinates:if employee notin visited:sum+=dfs(employee)returnsumreturndfs(id)