Graphs are fundamental data structures used to model relationships between objects. Undirected and directed graphs are fundamental concepts in graph theory, which is a branch of mathematics that studies the relationships between objects using a set of points (vertices) and the connections between them (edges). Let’s explore the differences between directed and undirected graphs:

**Undirected Graph:**

An undirected graph is a collection of vertices and edges, where edges have no direction. This means that the edges simply represent a connection between two vertices without any indication of one being a starting point and the other being an endpoint. In other words, if there’s an edge between vertices A and B, you can traverse it in either direction: A to B or B to A.

Undirected graphs are used to model relationships where the connection is symmetric and has no inherent direction. Examples include social networks, where the relationship of being friends is mutual, or road networks, where streets are two-way.

Example:

A---B | | C---D

**Directed Graph (Digraph):**

A directed graph, also known as a digraph, is a graph where edges have a direction associated with them. This means that each edge goes from one vertex (the starting vertex) to another vertex (the ending vertex). In this case, the relationship between two vertices has an inherent direction.

Directed graphs are used to model relationships where the connection is asymmetric or directional. Examples include web pages with hyperlinks (a link from page A to page B doesn’t necessarily imply a link from B to A) or flow networks, where edges represent the flow of resources (like water or data) in a particular direction.

Example:

A-->B | | V V C-->D

## Another types of Graph data structure:

**Weighted Graph:** A weighted graph assigns a weight or cost to each edge. It’s used to represent situations where edges have associated values, such as distances, costs, or capacities. Example:

A-2-B |\ | 1 \ | 3 | \| C---D

**Unweighted Graph:** An unweighted graph is a graph where all edges have the same weight or are considered equivalent. Example:

A---B | | C---D

**Cyclic Graph:** A cyclic graph contains at least one cycle, which is a path that starts and ends at the same node without repeating any edges or nodes. Example:

A---B |\ /| | X | |/ \| C---D

**Acyclic Graph:** An acyclic graph doesn’t contain any cycles. These graphs are often used in various applications, including dependency resolution and topological sorting. Example:

A-->B-->C | / V D

**Bipartite Graph:** A bipartite graph is a graph whose nodes can be divided into two disjoint sets such that no two nodes within the same set are connected by an edge. Example:

A---B | | C---D

**Complete Graph:** A complete graph is a graph in which every pair of distinct nodes is connected by a unique edge. Example:

A---B |\ /| | X | |/ \| C---D

**Connected Graph:** A connected graph has a path between every pair of nodes, meaning there are no isolated nodes. Example:

A---B---C | | D-------E

**Disconnected Graph:** A disconnected graph has at least two components that are not connected to each other. Example:

A---B C---D

**Sparse Graph:** A sparse graph is a graph in which the number of edges is much smaller than the number of possible edges. It has relatively few connections between nodes.

**Dense Graph:** A dense graph is a graph in which the number of edges is close to the maximum possible number of edges. It has a high density of connections between nodes.

## Conclusion

In summary:

- In an undirected graph, edges represent symmetric relationships with no direction.
- In a directed graph, edges represent asymmetric relationships with a specific direction.

Both types of graphs have their own properties, algorithms, and applications in various fields, including computer science, social sciences, transportation planning, and more. Graph theory provides a powerful framework for understanding and analyzing relationships and connections between different entities.

**more related content on Data Structure and Algorithms(DSA)**