# What Is Graph in Data Structure?

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Angela Bailey

What Is Graph in Data Structure?

A graph is a non-linear data structure that consists of a collection of nodes (also known as vertices) and edges. It is used to represent relationships between different entities. Graphs are widely used in various applications such as computer networks, social networks, transportation systems, and more.

## Nodes and Edges

In a graph, nodes are the fundamental building blocks. Each node represents an entity or an object.

Nodes can be connected to one another through edges. An edge represents the relationship or connection between two nodes.

A graph can be represented visually with nodes as circles or rectangles and edges as lines connecting these nodes. Each edge may have a direction (directed graph) or no direction (undirected graph).

## Types of Graphs

There are several types of graphs:

• Undirected Graph: In this type of graph, the edges have no direction or orientation. The relationship between two nodes is symmetric.
• Directed Graph: Also known as a digraph, it is a type of graph where the edges have a specific direction or orientation.
• Weighted Graph: In this type of graph, each edge has an associated weight or cost. It is used to represent situations where there are additional attributes or values associated with the relationships between nodes.
• Cyclic Graph: A cyclic graph contains at least one cycle, which means there is a path that starts and ends at the same node.
• Acyclic Graph: An acyclic graph does not contain any cycles.

## Graph Representation

There are different methods to represent a graph:

An adjacency matrix is a two-dimensional array where each cell represents the presence or absence of an edge between two nodes. If an edge exists, the corresponding cell value is usually 1 or a weight value in case of a weighted graph.

An adjacency list is a collection of linked lists or arrays. Each node has a list associated with it that contains its neighboring nodes. This representation is more memory-efficient for sparse graphs (graphs with fewer edges).

## Graph Traversal

Graph traversal refers to visiting all the nodes in a graph in a specific manner. There are two common methods for graph traversal:

• Breadth-First Search (BFS): It explores all the vertices at the same level before moving to the next level.
• Depth-First Search (DFS): It explores as far as possible along each branch before backtracking.

## Applications of Graphs

The versatility of graphs makes them useful in various real-world applications:

• Social Networks: Graphs can represent social connections and relationships between individuals.
• Road Networks: Graphs can be used to model road networks and find optimal routes.
• E-commerce Recommendations: Graphs can be used to recommend products based on user preferences and connections.
• Data Mining: Graphs are employed in data mining algorithms to discover patterns and relationships.

In conclusion, graphs are powerful data structures that facilitate the representation and analysis of relationships between entities. Understanding graphs and their applications is essential for solving complex problems in various domains.