Problem Description

A linked list of length n is given such that each node contains an additional random pointer, which could point to any node in the list, or null.

Construct a deep copy of the list. The deep copy should consist of exactly n brand new nodes, where each new node has its value set to the value of its corresponding original node. Both the next and random pointer of the new nodes should point to new nodes in the copied list such that the pointers in the original list and copied list represent the same list state. None of the pointers in the new list should point to nodes in the original list.

For example, if there are two nodes X and Y in the original list, where X.random --> Y, then for the corresponding two nodes x and y in the copied list, x.random --> y.

Return the head of the copied linked list.

The linked list is represented in the input/output as a list of n nodes. Each node is represented as a pair of [val, random_index] where:

val: an integer representing Node.val random_index: the index of the node (range from 0 to n-1) that the random pointer points to, or null if it does not point to any node. Your code will only be given the head of the original linked list.

Example 1:

138.1.png

  • Input: head = [[7,null],[13,0],[11,4],[10,2],[1,0]]
  • Output: [[7,null],[13,0],[11,4],[10,2],[1,0]]

Example 2:

138.2.png

  • Input: head = [[1,1],[2,1]]
  • Output: [[1,1],[2,1]]

My Idea

The main problem here is creating the random pointers for nodes that have not yet been created. Therefore, we first iterate over the original list once initializing only the value for the new nodes and storing those nodes in a hash map that maps the originals to their copies. Then we iterate over the items of the hash map and set the next and random attributes making use of the mapping. This yields a time complexity of O(n)

My solution

from typing import Optional
# Definition for singly-linked list.
class Node:
    def __init__(self, x: int, next = None, random = None):
        self.val = int(x)
        self.next = next
        self.random = random
# Time Complexity O(n)
def copyRandomList(head: Optional[Node]):
    if not head:
        return None
    mapping = dict()
    curr = head

    while curr:
        mapping[curr]=Node(curr.val)
        curr = curr.next

    for original in mapping:
        mapping[original].next = mapping.get(original.next,None)
        mapping[original].random = mapping.get(original.random,None)

    return mapping[head]