Detect Loop in a Linked List: A Comprehensive Guide

Detecting a loop in a linked list is one of the most common and important problems in data structures. A loop in a linked list occurs when a node’s next pointer points back to a previous node, forming a cycle. This problem is crucial for understanding linked lists and appears frequently in coding interviews.

In this blog, we will discuss:

• What a loop in a linked list is.
• Applications of detecting loops.
• Different approaches to solve the problem.
• Code implementations in Python.
• Time and space complexity analysis.

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What is a Loop in a Linked List?

A loop, or cycle, in a linked list exists if the next pointer of any node points back to one of the previous nodes in the list instead of null. This causes an infinite traversal loop.

Example of a Linked List with a Loop:

Consider a linked list:
1 -> 2 -> 3 -> 4 -> 5 -> 3 (back to 3)

In this example, the node with value 5 points back to the node with value 3, forming a loop.

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Applications of Detecting Loops

1. Avoid Infinite Loops: Prevent endless traversal of the linked list in case of a loop.
2. Memory Management: Identify and fix memory leaks caused by circular references in linked data structures.
3. Algorithm Design: Detecting cycles is critical in graph-related problems, which are often represented using linked lists.

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Approaches to Detect a Loop

1. Naive Approach: Using a Hash Table

Concept:

• Traverse the linked list and store the addresses of visited nodes in a hash table.
• If a node’s address is already in the table, a loop exists.

Algorithm Steps:

1. Initialize an empty hash table.
2. Traverse the linked list.
3. For each node:

• Check if the node’s address is already in the hash table.
• If yes, a loop is detected.
• Otherwise, add the node’s address to the hash table.

1. If the traversal ends, there is no loop.

Python Code:

class Node:
    def __init__(self, data):
        self.data = data
        self.next = None

def detect_loop_hashing(head):
    visited = set()
    current = head
    while current:
        if current in visited:
            return True  # Loop detected
        visited.add(current)
        current = current.next
    return False  # No loop

# Example usage
head = Node(1)
head.next = Node(2)
head.next.next = Node(3)
head.next.next.next = head.next  # Creates a loop

print("Loop detected (Hashing):", detect_loop_hashing(head))

Output:
Loop detected (Hashing): True

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2. Efficient Approach: Floyd’s Cycle Detection Algorithm

Concept:

• Use two pointers:
• A slow pointer moves one step at a time.
• A fast pointer moves two steps at a time.
• If the list contains a loop, the slow and fast pointers will meet.

Algorithm Steps:

1. Initialize two pointers: slow and fast, both pointing to the head of the list.
2. Traverse the list:

• Move slow one step.
• Move fast two steps.
• If slow and fast meet, a loop exists.

1. If fast or fast.next becomes null, no loop exists.

Python Code:

def detect_loop_floyd(head):
    slow = head
    fast = head
    while fast and fast.next:
        slow = slow.next
        fast = fast.next.next
        if slow == fast:
            return True  # Loop detected
    return False  # No loop

# Example usage
print("Loop detected (Floyd's Algorithm):", detect_loop_floyd(head))

Output:
Loop detected (Floyd's Algorithm): True

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3. Modify the List: Marking Visited Nodes

Concept:

• Modify the next pointer of visited nodes to point to a dummy node or a specific value.
• If a node already points to the dummy node, a loop exists.

Note: This method alters the linked list, which might not always be desirable.

Python Code:

def detect_loop_modify(head):
    marker = Node(-1)
    current = head
    while current:
        if current.next == marker:
            return True  # Loop detected
        temp = current.next
        current.next = marker
        current = temp
    return False  # No loop

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Comparison of Approaches

Approach	Time Complexity	Space Complexity	Notes
Hash Table	(O(n))	(O(n))	Simple but requires extra space.
Floyd’s Algorithm	(O(n))	(O(1))	Most efficient method.
Modify the List	(O(n))	(O(1))	Alters the structure of the list.

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Time and Space Complexity

1. Hash Table:

• Time Complexity: (O(n)) (traverse the list once).
• Space Complexity: (O(n)) (store node addresses).

1. Floyd’s Cycle Detection:

• Time Complexity: (O(n)) (traverse the list once).
• Space Complexity: (O(1)) (uses two pointers only).

1. Modify the List:

• Time Complexity: (O(n)).
• Space Complexity: (O(1)).

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Special Cases

1. Empty List:
   If the linked list is empty (head == null), there is no loop.
2. Single Node:
   If the list has only one node pointing to itself, it is a loop.
3. Multiple Nodes with No Loop:
   The traversal will terminate without detecting any loop.

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FAQs

1. Can a doubly linked list have a loop?
Yes, loops can exist in doubly linked lists. The same detection methods can be applied.

2. How can we remove a detected loop?
You can traverse the list, find the node causing the loop, and set its next pointer to null.

3. Why is Floyd’s Algorithm efficient?
It detects loops in (O(n)) time using only (O(1)) space, making it ideal for large linked lists.

4. What are some real-world applications of loop detection?

• Memory management in operating systems.
• Cycle detection in network packets.
• Error handling in linked data structures.

5. Can we detect a loop without modifying the linked list?
Yes, Floyd’s Algorithm and the hash table approach both preserve the list structure.

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Detecting a loop in a linked list is a critical operation in data structures. While the naive approach using a hash table is simple, Floyd’s Cycle Detection Algorithm is more efficient and widely preferred due to its (O(1)) space complexity. Understanding these methods will strengthen your grasp of linked list problems and pointer manipulation.

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Also Read –

Enjoy Algorithms – Detect Loop in Linked List​

EnjoyAlgorithmsLearn IT University – Loop Detection Guide​

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