Binary TreesBinary Trees36
  1. 1Preorder Traversal of a Binary Tree using Recursion
  2. 2Preorder Traversal of a Binary Tree using Iteration
  3. 3Inorder Traversal of a Binary Tree using Recursion
  4. 4Inorder Traversal of a Binary Tree using Iteration
  5. 5Postorder Traversal of a Binary Tree Using Recursion
  6. 6Postorder Traversal of a Binary Tree using Iteration
  7. 7Level Order Traversal of a Binary Tree using Recursion
  8. 8Level Order Traversal of a Binary Tree using Iteration
  9. 9Reverse Level Order Traversal of a Binary Tree using Iteration
  10. 10Reverse Level Order Traversal of a Binary Tree using Recursion
  11. 11Find Height of a Binary Tree
  12. 12Find Diameter of a Binary Tree
  13. 13Find Mirror of a Binary Tree
  14. 14Left View of a Binary Tree
  15. 15Right View of a Binary Tree
  16. 16Top View of a Binary Tree
  17. 17Bottom View of a Binary Tree
  18. 18Zigzag Traversal of a Binary Tree
  19. 19Check if a Binary Tree is Balanced
  20. 20Diagonal Traversal of a Binary Tree
  21. 21Boundary Traversal of a Binary Tree
  22. 22Construct a Binary Tree from a String with Bracket Representation
  23. 23Convert a Binary Tree into a Doubly Linked List
  24. 24Convert a Binary Tree into a Sum Tree
  25. 25Find Minimum Swaps Required to Convert a Binary Tree into a BST
  26. 26Check if a Binary Tree is a Sum Tree
  27. 27Check if All Leaf Nodes are at the Same Level in a Binary Tree
  28. 28Lowest Common Ancestor (LCA) in a Binary Tree
  29. 29Solve the Tree Isomorphism Problem
  30. 30Check if a Binary Tree Contains Duplicate Subtrees of Size 2 or More
  31. 31Check if Two Binary Trees are Mirror Images
  32. 32Calculate the Sum of Nodes on the Longest Path from Root to Leaf in a Binary Tree
  33. 33Print All Paths in a Binary Tree with a Given Sum
  34. 34Find the Distance Between Two Nodes in a Binary Tree
  35. 35Find the kth Ancestor of a Node in a Binary Tree
  36. 36Find All Duplicate Subtrees in a Binary Tree
GraphsGraphs46
  1. 1Breadth-First Search in Graphs
  2. 2Depth-First Search in Graphs
  3. 3Number of Provinces in an Undirected Graph
  4. 4Connected Components in a Matrix
  5. 5Rotten Oranges Problem - BFS in Matrix
  6. 6Flood Fill Algorithm - Graph Based
  7. 7Detect Cycle in an Undirected Graph using DFS
  8. 8Detect Cycle in an Undirected Graph using BFS
  9. 9Distance of Nearest Cell Having 1 - Grid BFS
  10. 10Surrounded Regions in Matrix using Graph Traversal
  11. 11Number of Enclaves in Grid
  12. 12Word Ladder - Shortest Transformation using Graph
  13. 13Word Ladder II - All Shortest Transformation Sequences
  14. 14Number of Distinct Islands using DFS
  15. 15Check if a Graph is Bipartite using DFS
  16. 16Topological Sort Using DFS
  17. 17Topological Sort using Kahn's Algorithm
  18. 18Cycle Detection in Directed Graph using BFS
  19. 19Course Schedule - Task Ordering with Prerequisites
  20. 20Course Schedule 2 - Task Ordering Using Topological Sort
  21. 21Find Eventual Safe States in a Directed Graph
  22. 22Alien Dictionary Character Order
  23. 23Shortest Path in Undirected Graph with Unit Distance
  24. 24Shortest Path in DAG using Topological Sort
  25. 25Dijkstra's Algorithm Using Set - Shortest Path in Graph
  26. 26Dijkstra’s Algorithm Using Priority Queue
  27. 27Shortest Distance in a Binary Maze using BFS
  28. 28Path With Minimum Effort in Grid using Graphs
  29. 29Cheapest Flights Within K Stops - Graph Problem
  30. 30Number of Ways to Reach Destination in Shortest Time - Graph Problem
  31. 31Minimum Multiplications to Reach End - Graph BFS
  32. 32Bellman-Ford Algorithm for Shortest Paths
  33. 33Floyd Warshall Algorithm for All-Pairs Shortest Path
  34. 34Find the City With the Fewest Reachable Neighbours
  35. 35Minimum Spanning Tree in Graphs
  36. 36Prim's Algorithm for Minimum Spanning Tree
  37. 37Disjoint Set (Union-Find) with Union by Rank and Path Compression
  38. 38Kruskal's Algorithm - Minimum Spanning Tree
  39. 39Minimum Operations to Make Network Connected
  40. 40Most Stones Removed with Same Row or Column
  41. 41Accounts Merge Problem using Disjoint Set Union
  42. 42Number of Islands II - Online Queries using DSU
  43. 43Making a Large Island Using DSU
  44. 44Bridges in Graph using Tarjan's Algorithm
  45. 45Articulation Points in Graphs
  46. 46Strongly Connected Components using Kosaraju's Algorithm

Check for Duplicate Subtrees in a Binary Tree

Problem Statement

Given a binary tree, determine whether it contains any duplicate subtrees of size two or more. Two subtrees are considered duplicate if they have the same structure and the same node values. Note that subtrees of size 1 (single leaf nodes) should not be considered for duplication check.

Examples

Input Tree Has Duplicate Subtrees? Description
[1, 2, 3, 4, null, 2, 4, null, null, 4]
true The subtree [4] appears more than once and so does [2, 4].
[1, 2, 3, 4, 5, 6, 7]
false All subtrees are unique; no duplication occurs.
[1, 1, 1, null, null, null, 1]
true The subtree consisting of just node [1] is duplicated multiple times.
[] false An empty tree has no subtrees and thus no duplicates.
[1]
false A single-node tree cannot contain any duplicate subtrees.

Solution

Case 1: Tree with Obvious Duplicate Subtrees

In this scenario, the binary tree contains subtrees that appear more than once and are structurally and numerically identical. For example, if a node '2' has a left child '4', and this structure is repeated elsewhere, we consider it a duplicate. These are the kinds of duplications the algorithm targets — not just value repetition, but full structural duplication.

Case 2: Tree with All Unique Subtrees

Sometimes, a tree may share node values (e.g., multiple nodes having value '4'), but their structural layout differs. In such cases, although node values repeat, the structure does not, so it doesn't count as a duplicate subtree. The algorithm needs to distinguish structural sameness from just value duplication.

Case 3: Tree with a Single Node

If the binary tree has only one node, there are no subtrees of size ≥ 2, so it’s impossible to have any duplicates. This is a trivial case and should return false by default.

Case 4: Empty Tree

If the input tree is empty, it doesn’t contain any nodes or subtrees, so it logically follows that there can be no duplicate subtrees. This is another trivial base case handled directly at the start of the algorithm.

How the Algorithm Works

The algorithm works by performing a postorder traversal (left, right, root) and serializing each subtree into a unique string (e.g., "2,4,#,#,#" for a node 2 with a left child 4). These strings are stored in a hash map. If any serialized string occurs more than once and represents a subtree of size ≥ 2, the function returns true.

This serialization strategy ensures that both the structure and values are captured, helping to reliably detect duplicates, even if node values repeat but structures differ.

Algorithm Steps

  1. Traverse the binary tree in a postorder manner and serialize each subtree into a string representation.
  2. For each node, if the node is not a leaf (i.e. it has at least one child), store its serialized string in a hash map with a frequency count.
  3. If the frequency of any serialized subtree becomes 2 (or more), mark that duplicate has been found.
  4. After processing all nodes, if a duplicate subtree (of size 2 or more) is detected, return true; otherwise, return false.

Code

Python
Java
JavaScript
C
C++
C#
Kotlin
Swift
Go
Php
class TreeNode:
    def __init__(self, val=0, left=None, right=None):
        self.val = val
        self.left = left
        self.right = right


def hasDuplicateSubtrees(root):
    seen = {}
    duplicate = False

    def serialize(node):
        nonlocal duplicate
        if not node:
            return '#'
        left = serialize(node.left)
        right = serialize(node.right)
        subtree = str(node.val) + ',' + left + ',' + right
        # Only consider subtrees of size >= 2 (node with at least one child)
        if node.left or node.right:
            seen[subtree] = seen.get(subtree, 0) + 1
            if seen[subtree] == 2:
                duplicate = True
        return subtree

    serialize(root)
    return duplicate

# Example usage:
if __name__ == '__main__':
    # Construct a sample binary tree:
    #         1
    #        / \
    #       2   3
    #      /   / \
    #     4   2   4
    #        /
    #       4
    root = TreeNode(1, TreeNode(2, TreeNode(4)), TreeNode(3, TreeNode(2, TreeNode(4)), TreeNode(4)))
    print(hasDuplicateSubtrees(root))

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