What Is Graph in Data Structure Explain?


Heather Bennett

Graph is a widely used data structure in computer science and has numerous applications in various fields such as social networks, transportation systems, and computer networks. A graph consists of a set of vertices (also known as nodes) connected by edges (also known as arcs or links). The relationships between these vertices and edges allow us to represent complex relationships and analyze them efficiently.

Basic Terminology

Before we delve into the details of graphs, let’s understand some basic terminology:

  • Vertex: A vertex is a fundamental unit of a graph. It represents an entity or an object.
  • Edge: An edge connects two vertices and represents the relationship between them.
  • Directed Graph: In a directed graph, the edges have a specific direction.

    The order of vertices matters.

  • Undirected Graph: In an undirected graph, the edges do not have any direction. The order of vertices does not matter.
  • Weighted Graph: In a weighted graph, each edge has an associated weight or cost. It represents the strength or distance between two vertices.

Main Types of Graphs

1. Directed Acyclic Graph (DAG)

A Directed Acyclic Graph (DAG) is a directed graph that does not contain any cycles.

It means that there are no paths that start from a vertex and return to the same vertex without visiting any other vertex in between. DAGs are commonly used in modeling dependencies and scheduling problems.

2. Undirected Graph

An Undirected Graph is a graph where all edges are bidirectional. It means that if there is an edge between vertex A and vertex B, there is also an edge between B and A. Undirected graphs are used to represent relationships where the direction does not matter, such as friendships in a social network.

3. Weighted Graph

A Weighted Graph is a graph where each edge has an associated weight or cost.

These weights can represent distances, time, or any other relevant metric. Weighted graphs are used in various applications like finding the shortest path between two vertices.

Operations on Graphs

Now that we have discussed the basic terminology and types of graphs, let’s explore some common operations performed on graphs:

  • Add Vertex: Adding a vertex to a graph involves creating a new node and connecting it to one or more existing vertices.
  • Add Edge: Adding an edge to a graph involves connecting two existing vertices with a new edge.
  • Delete Vertex: Deleting a vertex from a graph involves removing the node and all its associated edges.
  • Delete Edge: Deleting an edge from a graph involves removing the connection between two vertices.
  • Traverse: Graph traversal involves visiting all vertices in a graph in a systematic manner. There are various traversal algorithms like Depth-First Search (DFS) and Breadth-First Search (BFS).


In conclusion, graphs are powerful data structures that allow us to model complex relationships efficiently. Understanding the terminology and types of graphs is essential for solving real-world problems using graphs. By performing operations like adding or deleting vertices and edges, as well as traversing the graph, we can analyze and manipulate the data effectively.

By incorporating graphs into your programs, you can unlock the potential for solving a wide range of problems. So, start exploring and experimenting with graphs in your projects!

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