There are two types of data structures
Linear
Non – linear
We are aware of linear data structures such as arrays, stacks, queues, and linked lists. They are called linear because data elements are arranged linearly or sequentially.
Imagine a world map with different cities marked on it. Now, let's think about how these cities are connected. In the world of computers, we use a special way to represent these connections called a "graph data structure."
A graph is like a map, but instead of showing physical locations, it shows how different things are related to each other. In a graph, we have "nodes," which are like the cities on the map, and "edges," which are like the roads connecting the cities.
For example, let's say we have five cities: A, B, C, D, and E. In our graph, each city would be a node, and the roads between them would be the edges. If there's a road from city A to city B, we draw an edge between the nodes representing A and B.
Now, there are two types of graphs we can have. One is called an "undirected graph," where the roads are bidirectional, meaning you can go from city A to city B and also from city B to city A using the same road. The other type is a "directed graph," where the roads have a specific direction, like a one-way street.
In the real world, graphs are used in many ways. For example, think about how Google Maps helps you find the shortest route between two places. It uses a graph data structure to figure out the best path to take.
Now, let's talk about cycles in graphs. A cycle is like going in a loop, starting from a city and coming back to the same city without visiting any other city more than once. Not all graphs have cycles, though. Some graphs are like open structures, where you can start from one node and keep following the edges, but you won't come back to the same node.
The "degree" of a node in a graph tells us how many edges are connected to that node. In other words, it's like the number of roads leading to or from a city on the map. For undirected graphs, the degree is simply the number of edges connected to a node. In directed graphs, we have "indegree" and "outdegree," which tell us how many edges are coming into and going out of a node, respectively.
💡Example, D(3) = 3 D(4) = 2
Graphs can also have weights on their edges, which represent the cost of traveling between two nodes. This is like saying some roads on the map are longer or shorter than others. It helps us calculate the best route based on the cost.
In summary, graphs are like magical maps that help us understand and analyze relationships between different things. They are used in various real-world applications, like finding the best route, organizing data in social media, and much more. Understanding graphs is a key skill for computer students to explore the fascinating world of data and connections.
If facing any doubts, kindly let me know in the comments.