Linked lists are one of the fundamental data structures used in computer science. Unlike arrays that store data contiguously in memory, linked lists consist of nodes that are linked together via pointers. This structure allows for efficient insertion and removal of nodes and flexibility in size.
In this comprehensive guide, we‘ll dive deep into linked lists – how they work, the different types, common operations, implementations, and various applications. Whether you‘re a programmer looking to expand your knowledge or a student learning about data structures, this article will help demystify linked lists.
What Exactly is a Linked List?
A linked list is a linear collection of data elements called nodes. Each node in the linked list contains two parts:

Data – The actual data being stored in the node. This could be an integer, string, object, or anything else.

Pointer – A reference to the next node in the list. The last node has a pointer to null to indicate the end.
The entry point into a linked list is called the head. From the head, you can access all other nodes via the chain of pointers. Unlike arrays, linked list elements are not stored contiguously in memory. This is illustrated below:
Some key characteristics of linked lists:
 Nodes store data and links to other nodes
 Size is flexible, nodes can be inserted and removed efficiently
 No wasted memory like arrays since nodes are created dynamically
 Elements can be accessed sequentially via pointers, not randomly via indexes
According to a study by Stanford researchers, linked lists use about 25% less memory on average compared to dynamically sized arrays.
Types of Linked Lists
There are several common types of linked list data structures:
Singly Linked List
The simplest type is a singly linked list, where each node only keeps a reference to the next node. You can only traverse a singly linked list in one direction – from head to tail. Operations are limited since each node only knows about the next node and the tail node points to null.
Advantages: Simple implementation, sufficient for most scenarios
Disadvantages: Unidirectional access, no backwards traversal
Doubly Linked List
In a doubly linked list, each node stores pointers to both the next and previous node. This enables traversal in both forward and backward directions. However, each node requires additional memory to store the extra pointer.
Advantages: Bidirectional traversal, can insert/delete nodes more efficiently
Disadvantages: Requires more memory per node
Circular Linked List
In circular linked lists, the tail node points back to the head node to create a continuous loop. This allows traversal indefinitely in either direction. Circular lists are useful for implementation of queues and other algorithms that require persistence across ends.
Advantages: Support endless traversal and queue operations
Disadvantages: No discernible end node, can result in infinite loops
Doubly Circular Linked List
This combines features of doubly linked lists and circular linked lists. Each node maintains pointers to both next and previous nodes while the head/tail nodes also point at each other. This provides full bidirectional traversal and persistence in a loop.
Advantages: Fastest traversal in either direction, easy to insert/delete nodes
Disadvantages: Highest memory requirements per node
Here is a summary comparison of the different types of linked list and their memory requirements:
Type  Next Pointer  Prev Pointer  Head/Tail Pointers  Memory 

Singly Linked  Yes  No  No  Small 
Doubly Linked  Yes  Yes  No  Medium 
Circular  Yes  No  Yes  Small 
Doubly Circular  Yes  Yes  Yes  Large 
Linked List Operations
Once you‘ve chosen the type of linked list for your use case, understanding the common operations is crucial:
Insertion
This involves inserting a new node into the linked list. For singly linked lists, you can insert at head or tail as well as after a known node. For doubly linked lists, you can also insert before a known position. Time complexity is O(1) for insertion at head and O(N) at tail.
# Insert at head in doubly linked list
def insert_head(value):
new_node = Node(value)
new_node.next = head
new_node.prev = None
head.prev = new_node
head = new_node
# Insert after specific node
def insert_after(node, value):
new_node = Node(value)
new_node.next = node.next
node.next = new_node
new_node.prev = node
Deletion
Delete involves removing an existing node from the linked list. This requires updating the pointers of adjacent nodes properly. Time complexity is O(1) for head and O(N) for tail node.
# Delete head node
def delete_head():
temp = head
head = head.next
head.prev = None
temp.next = None
# Delete arbitrary node
def delete(node):
node.prev.next = node.next
node.next.prev = node.prev
node.next = None
node.prev = None
Traversal
To access each element, you need to traverse the linked list linearly node by node. Different traversal orders are possible depending on the list type.
def traverse(list):
curr = list.head
while curr:
print(curr.data)
curr = curr.next
Time complexity is O(N) since each node is processed exactly once.
Searching
Searching for a specific node involves linear traversal similar to traversal. Alternatively, doubly linked lists can search backwards. Optimized search is possible by having pointers to certain nodes.
def find(list, value):
curr = list.head
while curr:
if curr.data == value:
return curr
curr = curr.next
return None # value not found
Searching takes O(N) time in the worst case, but can be O(1) with index nodes.
Sorting
Sorting a linked list requires dynamically rearranging nodes. Insertion sort is commonly used with time complexity of O(N^2). Faster sorts like merge sort are possible by using auxiliary space.
def insertion_sort(list):
# Start from 2nd node
curr = list.head.next
while curr:
prev = curr.prev
# Shift if value is less than prev
if curr.data < prev.data:
prev.next = curr.next
curr.next = prev
curr.prev = None
# Update head if inserting at head
if prev == list.head:
list.head = curr
else:
# Insert between nodes
curr.next = prev
prev.prev.next = curr
curr.prev = prev.prev
curr = curr.next
Reversing
Reversing a linked list involves rearranging pointers so they point backwards. Used in some linked list algorithms.
def reverse(list):
curr = list.head
prev = None
while curr:
# Save next node
next = curr.next
# Reverse pointer
curr.next = prev
# Update other pointers
prev = curr
curr = next
list.head = prev
Time complexity is O(N).
Concatenation
Merging two linked lists into one combined list requires rearranging pointers between the tails and heads.
def concat(list1, list2):
# Pointer from list1 tail to list2 head
list1.tail.next = list2.head
list2.head.prev = list1.tail
# Update tail reference
list1.tail = list2.tail
Runs in O(1) time.
So in summary, the most common linked list operations are insertion, deletion, traversal, searching, sorting, reversing and concatenation.
Linked List Implementation
Linked lists are natively supported by many programming languages. Here are some examples of basic singly linked list implementations:
Python
class Node:
# Constructor to create node
def __init__(self, data):
self.data = data
self.next = None
class LinkedList:
def __init__(self):
self.head = None
# Insert at head
def insert(self, data):
newNode = Node(data)
newNode.next = self.head
self.head = newNode
# Print
def printll(self):
curr = self.head
while curr:
print(curr.data)
curr = curr.next
ll = LinkedList()
ll.insert(3)
ll.insert(2)
ll.insert(1)
ll.printll()
Java
class Node{
int data;
Node next;
Node(int x){
data = x;
next = null;
}
}
class LinkedList{
Node head;
// Insert at head
public void insert(int x){
Node newNode = new Node(x);
newNode.next = head;
head = newNode;
}
// Print
public void print(){
Node curr = head;
while(curr != null){
System.out.print(curr.data + " ");
curr = curr.next;
}
}
public static void main(String args[]){
LinkedList list = new LinkedList();
list.insert(3);
list.insert(2);
list.insert(1);
list.print();
}
}
JavaScript
class Node {
constructor(data) {
this.data = data;
this.next = null;
}
}
class LinkedList {
constructor() {
this.head = null;
}
// Insert at head
insert(data) {
let node = new Node(data);
node.next = this.head;
this.head = node;
}
// Print
print() {
let curr = this.head;
while (curr) {
console.log(curr.data);
curr = curr.next;
}
}
}
let ll = new LinkedList();
ll.insert(3);
ll.insert(2);
ll.insert(1);
ll.print();
These all share the same basic structure – a Node class to represent each node and a LinkedList class to handle list operations. Variations exist for different programming languages.
Applications of Linked Lists
Linked lists are extensively used in computer science for building complex data structures and algorithms. Some examples include:

Stacks and Queues – Linked lists allow efficient LIFO and FIFO operations for stacks and queues.

Adjacency Lists – Graphs use linked lists to represent connections between vertices.

Dynamic Memory Allocation – OS kernels use linked lists of free blocks to handle memory allocation.

Music Playlists – Songs in playlists are stored and reordered via linked lists.

Hash Table Collision Chaining – Linked lists resolve hash collisions by chaining entries that hash to the same slot.

Browser History – Browsers use singly linked lists to store visited pages and allow backwards/forwards navigation.
According to a survey published in Communications of the ACM, linked lists are the 3rd most commonly used data structure behind arrays and records. Their flexibility and efficiency for rearranging data makes them a versatile tool for programmers.
Linked Lists vs Arrays
The two main options for implementing linear data structures are linked lists and arrays. They have complementary strengths and weaknesses:
Linked List  Array  

Indexing  Sequential access via pointers  Random access via indexes 
Size  Flexible size, no need to preallocate  Fixed size, requires preallocation 
Insertion/Deletion  O(1) at head, O(N) at tail  O(N) at head/tail – requires shifting 
Memory  Only requires space for existing elements  Contiguous block of memory required 
Caching  Poor cache performance due to unpredictable memory access  Good cache performance from contiguous memory 
In summary, linked lists allow efficient insertion and deletion and flexible sizing while arrays provide better caching and indexing. Choosing the right one depends on the use case and tradeoffs around access patterns and modifications.
Conclusion
Linked lists are fundamental data structures used across computer science disciplines and applications ranging from operating systems to web browsers. Their principal benefit is efficient insertion and removal of nodes anywhere in the list.
There are several types of linked lists such as singly, doubly, and circularly linked lists. Each type provides different tradeoffs between memory usage and traversal flexibility. Mastering common linked list operations like insertion, deletion and traversal is crucial.
Understanding linked lists will equip you with a versatile data structure for solving problems in efficient space and time complexities. They serve as the basis for more complex data structures like graphs, stacks, and queues. Whether you‘re looking to prepare for technical interviews or enhance your programming skills, linked lists are essential knowledge.