# What Is Adjacency in Graph Data Structure?

//

Angela Bailey

Graph data structure is a widely used concept in computer science, especially in the field of algorithms and data analysis. One of the fundamental aspects of graphs is adjacency, which plays a crucial role in understanding the relationships between vertices or nodes.

The term “adjacency” refers to the connection or relationship between two vertices in a graph. In simple terms, it determines if there is an edge connecting two nodes. An edge represents a connection between two vertices, indicating that there is a relationship or interaction between them.

In graph theory, an adjacency matrix or an adjacency list can be used to represent and store the adjacency information of a graph. These representations help us understand the connectivity and relationships within the graph.

An adjacency matrix is a square matrix that represents whether there is an edge between two vertices in a graph. The rows and columns of the matrix correspond to the vertices of the graph. If there is an edge from vertex i to vertex j, then the corresponding entry (i,j) in the matrix will be set to 1; otherwise, it will be set to 0.

Example:

```0 1 1
1 0 0
1 0 0
```

In this example, there are three vertices represented by rows and columns. The entry (1,2) and (1,3) are set to 1, indicating that there are edges from vertex 1 to vertex 2 and vertex 3.

The entry (2,1) is also set to 1, representing an edge from vertex 2 to vertex 1. The other entries are set to 0 as there are no edges between those vertices.

An adjacency list is another way to represent the adjacency information of a graph. In this representation, each vertex in the graph has a list of its adjacent vertices. The list can be implemented using an array, linked list, or any other suitable data structure.

Example:

• 1: 2, 3
• 2: 1
• 3: 1

In this example, vertex 1 is connected to vertices 2 and 3, vertex 2 is connected to vertex 1, and vertex 3 is connected to vertex 1. Each entry in the adjacency list represents the adjacent vertices for a particular vertex.

The concept of adjacency is widely used in various real-world applications. Some of the common applications include:

• Network analysis: Adjacency helps analyze network connections and relationships between entities in a network.
• Social network analysis: It assists in understanding social interactions and relationships between individuals.
• Pathfinding algorithms: Algorithms like Dijkstra’s algorithm and A* algorithm heavily rely on adjacency information to find the shortest path between two nodes.

### In conclusion,

Adjacency is a crucial concept in graph data structures that determines the relationship between vertices. Whether represented through an adjacency matrix or an adjacency list, understanding adjacency helps analyze connectivity and enables various algorithms to operate efficiently on graphs.

If you’re interested in exploring graphs further, consider learning about other concepts like traversal algorithms (BFS and DFS), minimum spanning trees, and graph coloring. These concepts are essential for understanding the power and applications of graph data structures.