What Are Types of Graph in Data Structure?

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Angela Bailey

In the world of data structures, graphs play a vital role in representing connections and relationships between various entities. A graph is a non-linear data structure that consists of nodes (also known as vertices) connected by edges. These nodes can be any object or entity, while the edges represent the relationships between them.

There are several types of graphs in data structure that serve different purposes and have unique characteristics. Let’s delve into these types and explore their features:

1. Undirected Graph:
An undirected graph is a type of graph where edges have no direction or orientation.

In other words, the relationship between two nodes is bidirectional. This means that if node A is connected to node B, then node B is also connected to node A. In an undirected graph, we can traverse from any node to any other node.

2. Directed Graph:
Unlike an undirected graph, a directed graph has edges with specific directions or orientations.

The relationship between two nodes is unidirectional, meaning that if node A is connected to node B, it does not imply that node B is connected to node A unless explicitly defined. In a directed graph, we can only traverse from a source node to its destination node following the direction of the edges.

3. Weighted Graph:
A weighted graph assigns weights or values to each edge in addition to their connections between nodes.

These weights represent some kind of quantitative measure associated with the relationship between two nodes. For example, in a social network graph, the weight could represent the strength of friendship between individuals.

4. Unweighted Graph:
Conversely, an unweighted graph does not assign any weights or values to its edges. It simply represents the presence or absence of a connection between two nodes without considering any quantitative measures associated with it.

5. Cyclic Graph:
A cyclic graph is a graph that contains at least one cycle.

A cycle is a path that starts and ends at the same node, traversing through different nodes in between. In other words, it forms a closed loop. Cyclic graphs can be either directed or undirected.

6. Acyclic Graph:
As the name suggests, an acyclic graph is a graph that does not contain any cycles. It means that there are no paths that start and end at the same node, ensuring a linear structure without any closed loops.

7. Connected Graph:
A connected graph is a graph where there exists a path between every pair of nodes in the graph. In simpler terms, we can reach any node from any other node by following the edges of the graph.

8. Disconnected Graph:
In contrast to a connected graph, a disconnected graph consists of multiple connected components or subgraphs that have no paths connecting them. Each component itself may be connected but is not reachable from other components.

9. Bipartite Graph:
A bipartite graph is a special type of graph whose vertices can be divided into two disjoint sets such that every edge connects vertices from different sets. In other words, no two vertices within the same set are adjacent to each other.

These are some of the fundamental types of graphs in data structure, each serving its purpose in various applications such as network analysis, social network modeling, route planning, and more.

Understanding these types of graphs helps in choosing the appropriate data structure for specific scenarios and enables efficient manipulation and analysis of data relationships. So next time you encounter data with interconnected entities, consider using graphs to unlock valuable insights!