# What Is Graph Data Structure Explain With an Example?

//

Scott Campbell

In this tutorial, we will explore the concept of graph data structure and understand its significance in computer science and real-world applications. We will also delve into an example to illustrate its use.

## Introduction to Graph Data Structure

A graph is a non-linear data structure that consists of a set of nodes(vertices) and a set of edges that connect these nodes. It is widely used to represent relationships between different entities. Graphs are extensively used in various domains like social networks, computer networks, maps, recommendation systems, etc.

Key Terminologies:

• Node/Vertex: A node represents an entity or an object in a graph.
• Edge: An edge is a connection between two nodes and represents the relationship between them.
• Directed Graph: In a directed graph, the edges have a specific direction associated with them.
• Undirected Graph: In an undirected graph, the edges have no specific direction associated with them.

## An Example: Social Network Graph

To understand the concept better, let’s consider an example of a social network graph. Suppose we have a social network with four individuals: Alice, Bob, Charlie, and Dave. We can represent their connections using a graph as follows: In this example, each individual is represented as a node(vertex) in the graph. The connections between them are represented by edges. For instance,

• Alice is connected to Bob (Alice -> Bob)
• Alice is connected to Charlie (Alice -> Charlie)
• Bob is connected to Dave (Bob -> Dave)
• Charlie is connected to Dave (Charlie -> Dave)

This graph represents the relationships between individuals in the social network. It helps us analyze various aspects like finding mutual connections, suggesting new friends, etc.

## Types of Graphs

Graphs can be classified into various types based on their characteristics. Some common types include:

### 1. Directed Graph

In a directed graph, the edges have a specific direction associated with them. This indicates the flow or relationship between nodes. For example, if there is an edge from node A to node B, it implies that there is a directed connection from A to B.

### 2. Undirected Graph

In an undirected graph, the edges have no specific direction associated with them. The connection between nodes is bidirectional and does not imply any flow or relationship.

### 3. Weighted Graph

In a weighted graph, each edge has a weight associated with it. The weight represents a value or cost associated with traversing that edge. Weighted graphs are used in various applications like route planning, network optimization, etc.

### 4. Cyclic Graph

A cyclic graph contains at least one cycle i.e., it has a path that starts and ends at the same node.

### 5. Acyclic Graph

An acyclic graph does not contain any cycles i., there are no paths that start and end at the same node.

Note: There are several other types of graphs like bipartite graphs, complete graphs, etc., which are beyond the scope of this tutorial.

## Conclusion

In this tutorial, we learned about the graph data structure and its significance in various domains. We explored a social network graph example to understand how graphs can represent relationships and connections between different entities. Additionally, we discussed different types of graphs based on their characteristics.

Graphs provide a powerful tool for representing complex relationships and solving real-world problems efficiently. Understanding the fundamentals of graph data structure is crucial for computer science professionals and developers working on graph-based applications.