# Graphs In Data Structures | Graph Representation In Data Structure | Data Structures

This data structures tutorial is dedicated to helping beginners understand the graphs in data structures. In this tutorial, you will understand the fundament...

This data structures tutorial is dedicated to helping beginners understand the graphs in data structures. In this tutorial, you will understand the fundamentals and terminologies of the graph data structure, their types and their representation using different methods. The graphs in this tutorial are represented using both arrays and linked lists. The graph traversal procedure is included in the video for providing a better learning experience. The video includes the following topic.

• 00:00 Introduction
• 01:00 What is a Graph?
• 02:25 Graph Terminologies
• 04:00 Types of Graphs
• 10:45 Representation of Graph
• 15:25 Graph Traversal
• 16:48 Applications of Graphs.

What is a Graph in Data structures? A Graph in Data Structure can be defined as an abstract and nonlinear data structure capable of storing, accessing, and manipulating the data elements. The graphs consist of two major structural elements called vertices and edges. The edges connect with vertices based on direction and weight. Based on the direction and weight, there are different types of graphs according to their utility. The graphs are traversed using the BFS and DFS algorithms.

What Is a Data Structure? The short answer is: a data structure is a specific means of organizing data in a system to access and use. The long answer is a data structure is a blend of data organization, management, retrieval, and storage, brought together into one format that allows efficient access and modification. It’s collecting data values, the relationships they share, and the applicable functions or operations.

Why Is Data Structure Important? The digital world processes an increasing amount of data every year. According to Forbes, there are 2.5 quintillion bytes of data generated daily. The world created over 90 percent of the existing data in 2018 in the previous two years! The Internet of Things (IoT) is responsible for a significant part of this data explosion. Data structures are necessary to manage the massive amounts of generated data and a critical factor in boosting algorithm efficiency. Finally, since nearly all software applications use data structures and algorithms, your education path needs to include learning data structure and algorithms if you want a career as a data scientist or programmer. Interviewers want qualified candidates who understand how to use data structures and algorithms, so the more you know about the concepts, the more comfortably and confidently you will answer data structure interview questions.

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