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DSA Course: Interview Patterns and Problem Solving
Module 7: Graphs
Best Time to Buy and Sell Stock: Greedy Pattern
Maximum Subarray: Kadane Pattern
Move Zeroes: Two pointers Pattern
Contains Duplicate: Set Pattern
Valid Anagram: Frequency map Pattern
Longest Substring Without Repeating Characters: Sliding window Pattern
Valid Palindrome: Two pointers Pattern
Longest Palindromic Substring: Expand around center Pattern
Group Anagrams: Hash key Pattern
Binary Search: Classic search Pattern
Search Insert Position: Lower bound Pattern
First Bad Version: Predicate search Pattern
Search in Rotated Sorted Array: Rotated search Pattern
Find Minimum in Rotated Sorted Array: Rotated minimum Pattern
Valid Parentheses: Stack matching Pattern
Min Stack: Auxiliary stack Pattern
Daily Temperatures: Monotonic stack Pattern
Next Greater Element I: Monotonic stack Pattern
Evaluate Reverse Polish Notation: Stack evaluation Pattern
Reverse Linked List: Pointer reversal Pattern
Merge Two Sorted Lists: Dummy node Pattern
Linked List Cycle: Fast and slow pointers Pattern
Middle of the Linked List: Fast and slow pointers Pattern
Remove Nth Node From End: Two pointers Pattern
Binary Tree Traversals: DFS recursion Pattern
Maximum Depth of Binary Tree: Height recursion Pattern
Binary Tree Level Order Traversal: BFS queue Pattern
Validate Binary Search Tree: Range bounds Pattern
Lowest Common Ancestor: Recursive split Pattern
Connected Components: Adjacency DFS Pattern
Number of Islands: Grid DFS Pattern
Flood Fill: Boundary DFS Pattern
Clone Graph: Hash Map DFS Pattern
Course Schedule: Topological Sort Pattern
Union Find Components: Disjoint Set Pattern
Shortest Path in Unweighted Graph: BFS Distance Pattern
Climbing Stairs: Fibonacci DP Pattern
House Robber: Pick or Skip DP Pattern
Coin Change: Minimum Coins DP Pattern
Longest Increasing Subsequence: Binary Search DP Pattern
Longest Common Subsequence: 2D DP Pattern
0/1 Knapsack: Capacity DP Pattern
Longest Consecutive Sequence: Hash Set Pattern
Subarray Sum Equals K: Prefix Sum Hashmap Pattern
First Unique Character: Frequency Map Pattern
Find Duplicates: Frequency Map Pattern
Ransom Note: Character Availability Pattern
Sort Colors: Dutch National Flag Pattern
Next Permutation: Pivot and Suffix Reversal Pattern
Merge Intervals: Sort and Sweep Pattern
Find First and Last Position: Boundary Binary Search Pattern
Search a 2D Matrix: Flattened Binary Search Pattern
Subsets: Pick or Skip Recursion Pattern
Generate Parentheses: Valid State Backtracking Pattern
Combination Sum: Reuse Choice Backtracking Pattern
N-Queens: Constraint Backtracking Pattern
Word Search: Grid Backtracking Pattern
Kth Largest Element: Size-K Min-Heap Pattern
Top K Frequent Elements: Frequency Heap Pattern
Merge K Sorted Lists: Min-Heap Multiway Merge Pattern
Median Finder: Two Heaps Pattern
Task Scheduler: Greedy Max-Heap Pattern
Jump Game: Farthest Reach Greedy Pattern
Gas Station: Greedy Reset Pattern
Non-overlapping Intervals: Earliest End Greedy Pattern
Minimum Arrows to Burst Balloons: Interval End Greedy Pattern
Partition Labels: Last Occurrence Greedy Pattern
Single Number: XOR Cancellation Pattern
Power of Two: n and n-1 Pattern
Number of 1 Bits: Brian Kernighan Pattern
Single Number III: Rightmost Set Bit Pattern
XOR From 1 to N: Modulo Cycle Pattern
Prime Check: Square Root Trial Division Pattern
Sieve of Eratosthenes: Prime Marking Pattern
GCD: Euclidean Remainder Pattern
Binary Exponentiation: Fast Power Pattern
Modular Inverse: Extended Euclid Pattern
Implement Trie: Prefix Tree Pattern
Longest Common Prefix: Single Branch Trie Pattern
LRU Cache: Hash Map Plus Recency List Pattern
Segment Tree: Range Sum Query Pattern
Fenwick Tree: Binary Indexed Prefix Sum Pattern
CONTENTS

Clone Graph: Hash Map DFS Pattern

Deep-copy a graph while preserving neighbor links and cycles.

DSA Course: Interview Patterns and Problem Solving
Module 7: Graphs
dsa
graphs
+1
May 29, 2026
23
A

Learning Outcome

After this lesson, you should be able to use a map from original nodes to clone nodes so cycles do not create duplicate copies.

Problem Statement

Given a reference to a node in a connected undirected graph, return a deep copy of the graph.

InputOutputWhy
adjList = [[2,4],[1,3],[2,4],[1,3]]A separate graph with the same values and neighbor linksEach original node has exactly one clone, and cloned neighbors point to cloned nodes.

Brute Force Approach

Create a new node every time a neighbor is seen. In cyclic graphs this duplicates nodes and can recurse forever.

Optimized Approach

DFS from the start node. Store each original node in a map as soon as its clone is created, then recursively clone neighbors.

Exact Pseudocode

clone(node):
  if node is null:
    return null
  if node exists in map:
    return map[node]
  copy = new Node(node.val)
  map[node] = copy
  for neighbor in node.neighbors:
    copy.neighbors.add(clone(neighbor))
  return copy

Reference Code

class Solution:
    def cloneGraph(self, node):
        clones = {}

        def dfs(cur):
            if not cur:
                return None
            if cur in clones:
                return clones[cur]

            copy = Node(cur.val)
            clones[cur] = copy
            for nei in cur.neighbors:
                copy.neighbors.append(dfs(nei))
            return copy

        return dfs(node)

Sample Dry Run

StepStateResult
Visit node 1Create clone 1 and store map[1]Cycle protection starts
Clone neighbor 2Create clone 2Add it to clone 1 neighbors
Neighbor points backDFS sees node 1 already in mapReuse clone 1
FinishEvery original maps to one cloneReturn clone of start

Complexity

MeasureValueReason
TimeO(v + e)Every node and neighbor edge is visited once.
SpaceO(v)The clone map and recursion stack store graph nodes.

Edge Cases

  • Null input should return null.
  • Cycles must reuse existing clones.
  • Do not share original neighbor references in the clone.

Interview Checklist

  • Put the clone in the map before cloning neighbors.
  • Use node references as map keys.
  • Return the cloned start node, not the original node.

FAQs

Why store clone before neighbors?

A cycle can point back to the current node. The map must already know the clone to stop infinite recursion.

Is this a deep copy?

Yes, every node is new, and every neighbor link points to cloned nodes.

What is the core pattern?

DFS with an original-to-copy hash map.

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