"Graphs have a wide range of applications in fields of study like computer science, mathematics, and engineering and are an effective tool for representing and analyzing complex relationships between data.”

In this blog, we will discuss the application of graph data structures and examine how they can be used to solve real-world problems.

What is a Graph in Data Structure?

Graphs, as non-linear data structures, consist of a limited set of nodes or vertices interconnected by edges. They offer practical solutions by mirroring various real-world scenarios, such as telephone networks, circuit networks, and social networks. For instance, in a telephone network, nodes symbolize individual users, while edges depict the connections forged through phone lines. Thus, graphs in data structures serve as powerful tools for modeling and addressing complex problems in a network-like fashion.

The application of graph data structure is used in computer science, mathematics, and physics and has many practical applications in fields such as computer networks, Artificial Intelligence, and logistics.

Get the tech career you deserve, faster!

Connect with our expert counsellors to understand how to hack your way to success

User rating 4.7/5

1:1 doubt support

95% placement record

Akash Pal

Senior Software Engineer

326% Hike After Job Bootcamp

Himanshu Gusain

Programmer Analyst

32 LPA After Job Bootcamp

After Job Bootcamp

Applications of Graph Data Structure

Real-life application of graph data structure in various fields are:

Computer Science

Graphs are used to model many problems and solutions in computer science, such as representing networks, web pages, and social media connections. Graph algorithms are used in pathfinding, data compression, and scheduling.

Social Networks

Graphs represent and analyze social networks, such as the connections between individuals and groups.

Transportation

Graphs can be used to model transportation systems, such as roads and flights, and to find the shortest or quickest routes between locations.

Computer Vision

Graphs represent and analyze images and videos, such as tracking objects and detecting edges.

Natural Language Processing

Graphs can represent and analyze text, such as in syntactic and semantic dependency graphs.

Telecommunication

Graphs are used to model telecommunication networks, such as telephone and computer networks, and to analyze traffic and routing.

Circuit Design

Graphs are used in the design of electronic circuits, such as logic circuits and circuit diagrams.

Bioinformatics

Graphs model and analyze biological data, such as protein-protein interaction and genetic networks.

Operations research

Graphs are used to model and analyze complex systems in operations research, such as transportation systems, logistics networks, and supply chain management.

Artificial Intelligence

Graphs are used to model and analyze data in many AI applications, such as machine learning, Artificial Intelligence, and natural language processing.

Features and Properties of Graphs

A graph is a type of mathematical representation of a set of objects called vertices or nodes, and the connections are called edges. Some common features and properties of graphs include:

1. Degree: The degree of a vertex is the number of edges incident to it.

2. Path: A path in a graph is a sequence of edges that connects two vertices.

3. Connected: A graph is said to be a connected graph if a path exists between every pair of vertices.

4. Complete: A graph is said to be a complete graph if there is an edge between every pair of vertices.

5. Directed/Undirected: A graph is said to be directed if edges have a direction and undirected if edges do not.

6. Weighted/Unweighted: A graph is said to be weighted if edges have a value or weight associated with them and unweighted if they do not.

7. Cyclic/Acyclic: A graph is cyclic if a path begins and ends at the same vertex and acyclic if there is no such path.

8. Subgraph: A subgraph of a graph is a graph that is a subset of the vertices and edges of the original graph.

9. Adjacency Matrix: A matrix representation of a graph where rows and columns represent vertices, and the entries indicate the presence or weight of edges between them.

10. Incidence Matrix: A matrix representation of a graph where rows represent vertices, columns represent edges, and the entries indicate the presence of a vertex in an edge.

Real-life Example of Graph Data Structure

Some real-life examples of the application of graph data structure are:-

1. Social Network: In a social network, individuals (represented as nodes) are connected through relationships (represented as edges). The nodes in this graph represent the individuals, and the edges represent their relationships, such as friendships or connections. The graph can analyze data such as the number of connections an individual has, the most popular individuals in the network, and the formation of communities within the network.

2. Transportation Network: A transportation network is another real-life example of a graph data structure. This could include a map of roads and highways, with intersections represented as nodes and the roads connecting them represented as edges.

The graph can be used to find the shortest route between two locations, the number of routes between two places, and the overall connectivity of the transportation network.

Additionally, it can be used for traffic management, route optimization, and fleet management. In short, the transportation network is an excellent example of a graph data structure that can be used to model real-world problems with nodes and edges. Check out this problem - No of Spanning Trees in a Graph

The adjacency matrix, adjacency list, and adjacency set are three data structures that can represent a graph. An adjacency matrix can be considered a table with rows and columns, an adjacency list can be viewed as a node object, and an adjacency set is made up of adjacent vertices set.

Name the types of graph data structure.

Twelve types of graph data structures are directed graphs, undirected graphs, trivial graphs, regular graphs, complete graphs, cyclic graphs, acyclic graphs, finite graphs, infinite graphs, bipartite graphs, simple graphs, and connected graphs.

How are graphs represented in data structure?

Graphs in data structures can be represented in two main ways: adjacency matrices and adjacency lists. An adjacency matrix uses a 2D array where each cell represents a connection. An adjacency list represents edges as a list for each node's connections.

What are the various applications of graph in data structure?

Graphs are utilised in computer science to describe the flow of analysis and computation. It is utilised to describe networks of communication. It is used to define data organisation.

What is the application of theory of graphs?

Graph Theory is crucial in solving optimisation problems, decision-making, and resource allocation, with applications in network analysis, shortest path algorithms, minimum spanning trees, traversal algorithms, and matching and covering.

Conclusion

This blog discussed the application of graph data structure. A graph data structure is a powerful tool for representing and analyzing relationships between entities. It can be used in various applications, from social networks to navigation systems.

To see and learn more about graphs related to the application of graph data structure, please read the following articles:

But you have just started your learning process and are looking for questions from tech giants like Amazon, Microsoft, Uber, etc. For placement preparations, you must look at the problems, interview experiences, and interview bundles.