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Conclusion

A graph is a non-linear data structure that consists of vertices (or nodes) and edges. It consists of a finite set of vertices and set of edges that connect a pair of nodes. The graph is used to solve the most challenging and complex programming problems. It has different terminologies which are Path, Degree, Adjacent vertices, Connected components, etc.

Graph

Characteristics of Graph: 

The graph has various different characteristics which are as follows:

  • The maximum distance from a vertex to all the other vertices is considered the Eccentricity of that vertex.
  • The vertex having minimum Eccentricity is considered the central point of the graph.
  • The minimum value of Eccentricity from all vertices is considered the radius of a connected graph.

Applications of Graph: 

Different applications of Graphs are as follows:

  • The graph is used to represent the flow of computation.
  • It is used in modeling graphs.
  • The operating system uses Resource Allocation Graph.
  • Also used in the World Wide Web where the web pages represent the nodes.

Operation performed on Graph:

A graph is a non-linear data structure consisting of nodes and edges. Here are some common operations performed on graphs:

  • Add Vertex: New vertices can be added to the graph to represent a new node.
  • Add Edge: Edges can be added between vertices to represent a relationship between nodes.
  • Remove Vertex: Vertices can be removed from the graph by updating the references of adjacent vertices to remove the reference to the current vertex.
  • Remove Edge: Edges can be removed by updating the references of the adjacent vertices to remove the reference to the current edge.
  • Depth-First Search (DFS): A graph can be traversed using a depth-first search by visiting the vertices in a depth-first manner.
  • Breadth-First Search (BFS): A graph can be traversed using a breadth-first search by visiting the vertices in a breadth-first manner.
  • Shortest Path: The shortest path between two vertices can be determined using algorithms such as Dijkstra’s algorithm or A* algorithm.
  • Connected Components: The connected components of a graph can be determined by finding sets of vertices that are connected to each other but not to any other vertices in the graph.
  • Cycle Detection: Cycles in a graph can be detected by checking for back edges during a depth-first search.

These are some of the most common operations performed on graphs. The specific operations and algorithms used may vary based on the requirements of the problem and the programming language used. Graphs are commonly used in applications such as computer networks, social networks, and routing problems.

Real-Life Applications of Graph: 

  • One of the most common real-world examples of a graph is Google Maps where cities are located as vertices and paths connecting those vertices are located as edges of the graph.
  • A social network is also one real-world example of a graph where every person on the network is a node, and all of their friendships on the network are the edges of the graph.
  • A graph is also used to study molecules in physics and chemistry. 

Want to get started with Graph? You can try out our curated articles and lists for the best practice:

Advantages of data structure:

  1. Improved data organization and storage efficiency.
  2. Faster data retrieval and manipulation.
  3. Facilitates the design of algorithms for solving complex problems.
  4. Eases the task of updating and maintaining the data.
  5. Provides a better understanding of the relationships between data elements.
     

Disadvantage of Data Structure:

  1. Increased computational and memory overhead.
  2. Difficulty in designing and implementing complex data structures.
  3. Limited scalability and flexibility.
  4. Complexity in debugging and testing.
  5. Difficulty in modifying existing data structures.

Reference:

Data structures can be found in various computer science textbooks and online resources. Some popular texts include:

  1. “Introduction to Algorithms” by Thomas H. Cormen, Charles E. Leiserson, Ronald L. Rivest, and Clifford Stein.
  2. “Data Structures and Algorithm Analysis in Java” by Mark Allen Weiss.
  3. “The Algorithm Design Manual” by Steven S. Skiena.
  4. Online resources such as Coursera, Udemy, and Khan Academy also offer courses on data structures and algorithms.

Data Structure Types, Classifications and Applications

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What is Data Structure:

A data structure is a storage that is used to store and organize data. It is a way of arranging data on a computer so that it can be accessed and updated efficiently....

How Data Structure varies from Data Type:

We already have learned about data structure. Many times, what happens is that people get confused between data type and data structure. So let’s see a few differences between data type and data structure to make it clear....

Classification of Data Structure:

Data structure has many different uses in our daily life. There are many different data structures that are used to solve different mathematical and logical problems. By using data structure, one can organize and process a very large amount of data in a relatively short period. Let’s look at different data structures that are used in different situations....

Need Of Data structure :

The structure of the data and the synthesis of the algorithm are relative to each other. Data presentation must be easy to understand so the developer, as well as the user, can make an efficient implementation of the operation.Data structures provide an easy way of organizing, retrieving, managing, and storing data.Here is a list of the needs for data....

Arrays:

An array is a linear data structure and it is a collection of items stored at contiguous memory locations. The idea is to store multiple items of the same type together in one place. It allows the processing of a large amount of data in a relatively short period. The first element of the array is indexed by a subscript of 0. There are different operations possible in an array, like Searching, Sorting, Inserting, Traversing, Reversing, and Deleting....

Linked list:

A linked list is a linear data structure in which elements are not stored at contiguous memory locations. The elements in a linked list are linked using pointers as shown in the below image:...

Stack:

Stack is a linear data structure that follows a particular order in which the operations are performed. The order is LIFO(Last in first out). Entering and retrieving data is possible from only one end. The entering and retrieving of data is also called push and pop operation in a stack. There are different operations possible in a stack like reversing a stack using recursion, Sorting, Deleting the middle element of a stack, etc....

Queue:

Queue is a linear data structure that follows a particular order in which the operations are performed. The order is First In First Out(FIFO) i.e. the data item stored first will be accessed first. In this, entering and retrieving data is not done from only one end. An example of a queue is any queue of consumers for a resource where the consumer that came first is served first. Different operations are performed on a Queue like Reversing a Queue (with or without using recursion), Reversing the first K elements of a Queue, etc. A few basic operations performed In Queue are enqueue, dequeue, front, rear, etc....

Tree:

A tree is a non-linear and hierarchical data structure where the elements are arranged in a tree-like structure. In a tree, the topmost node is called the root node. Each node contains some data, and data can be of any type. It consists of a central node, structural nodes, and sub-nodes which are connected via edges. Different tree data structures allow quicker and easier access to the data as it is a non-linear data structure. A tree has various terminologies like Node, Root, Edge, Height of a tree, Degree of a tree, etc....

Graph:

A graph is a non-linear data structure that consists of vertices (or nodes) and edges. It consists of a finite set of vertices and set of edges that connect a pair of nodes. The graph is used to solve the most challenging and complex programming problems. It has different terminologies which are Path, Degree, Adjacent vertices, Connected components, etc....

Conclusion

Although these are the most widely known and used data structures, there are some other forms of data structures as well which are used in Computer Science, such as policy-based data structures, etc. But no matter which data structure you choose, each one has its perks and disadvantages, without the knowledge of which, it can be very costly to choose the wrong type of data structure. So it is very important to understand the need of the situation, and then decide which kind of data structure suits best for the job....

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Tree:

Conclusion