Linked Lists: A Beginner's Guide to Data Structures

In the previous tutorial, we discussed arrays—a fundamental data structure—covering their characteristics, advantages and disadvantages, and practical applications. Today, we’ll delve deeper into another essential data structure: the linked list, helping you understand its composition, key features, and typical use cases.

I. Basic Concept of Linked Lists

A linked list is a linear data structure composed of a sequence of nodes. Each node consists of two parts:

1. Data field: Stores the actual data.
2. Pointer (or reference) field: Points to the next node in the sequence.

A defining feature of linked lists is that their elements do not need to occupy contiguous memory locations. This enables dynamic resizing—nodes can be added or removed flexibly during runtime.

Basic Structure of a Linked List

In practice, a linked list node is commonly represented using a struct or class. For example, in Python, we can define a node as follows:

class Node:
    def __init__(self, data):
        self.data = data  # Data field
        self.next = None  # Pointer field

Here, data holds the node’s payload, while next stores a reference to the subsequent node.

II. Types of Linked Lists

There are three common types of linked lists:

1. Singly Linked List: Each node contains only one pointer—to the next node.
2. Doubly Linked List: Each node contains two pointers—one pointing to the previous node and another to the next node—enabling bidirectional traversal.
3. Circular Linked List: The last node points back to the first node, forming a closed loop.

Example: Singly Linked List

Let’s illustrate with a singly linked list. We’ll define the list class itself along with basic operations such as appending nodes and traversing the list:

class LinkedList:
    def __init__(self):
        self.head = None  # Head of the list

    def append(self, data):
        new_node = Node(data)
        if not self.head:
            self.head = new_node
            return
        last_node = self.head
        while last_node.next:
            last_node = last_node.next
        last_node.next = new_node

    def display(self):
        current_node = self.head
        while current_node:
            print(current_node.data, end=" -> ")
            current_node = current_node.next
        print("None")

Usage Example

Here’s how to insert elements and traverse the list:

linked_list = LinkedList()
linked_list.append(1)
linked_list.append(2)
linked_list.append(3)
linked_list.display()  # Output: 1 -> 2 -> 3 -> None

III. Advantages and Disadvantages of Linked Lists

Advantages

• Dynamic Size: The size of a linked list can grow or shrink dynamically, making it ideal for handling data whose size fluctuates frequently.
• Efficient Insertions/Deletions: When inserting or deleting at a known position, only pointer adjustments are needed—no bulk shifting of elements (unlike arrays), resulting in better performance.

Disadvantages

• Higher Space Overhead: Each node requires extra memory to store the pointer(s), increasing overall memory usage compared to arrays.
• Slower Random Access: To access an arbitrary node, traversal must start from the head, yielding an $O(n)$ time complexity. In contrast, arrays support $O(1)$ direct access via indexing.

IV. Common Use Cases

Linked lists are widely applied in various scenarios, including:

1. Implementing Queues and Stacks: Linked lists naturally support FIFO (queue) and LIFO (stack) behavior.
2. Dynamic Data Storage: For example, underlying implementations of dynamic arrays (e.g., Python’s list or Java’s ArrayList) often leverage linked structures—or hybrid approaches—for efficient resizing.
3. Adjacency List Representation of Graphs: Linked lists efficiently represent edges in graphs, simplifying adjacency queries and neighbor iteration.

V. Summary

As a foundational data structure, the linked list excels in flexibility and efficiency for insertions and deletions. Although it lags behind arrays in random-access speed, its ability to adapt dynamically to changing data volumes makes it indispensable in many real-world applications.

In our next tutorial, we’ll continue the Algorithm Zero series with “Overview of Data Structures: Stacks and Queues.” By mastering these core structures, you’ll strengthen your ability to design and apply algorithms effectively.