Adjacency matrix meaning and definition in DSA

Last Updated : 01 Aug, 2023

An adjacency matrix is a square matrix of N x N size where N is the number of nodes in the graph and it is used to represent the connections between the edges of a graph.

Graph representation of undirected graph to Adjacency Matrix

Characteristics of the adjacency matrix are:

The size of the matrix is determined by the number of vertices in the graph.
The number of nodes in the graph determines the size of the matrix.
The number of edges in the graph is simply calculated.
If the graph has few edges, the matrix will be sparse.

How to build an Adjacency Matrix:

It is very easy and simple to construct an adjacency matrix for a graph there are certain steps given below that you need to follow:

Create an n x n matrix where n is the number of vertices in the graph.
Initialize all elements to 0.
For each edge (u, v) in the graph, if the graph is undirected mark a[u][v] and a[v][u] as 1, and if the edge is directed from u to v, mark a[u][v] as the 1. (Cells are filled with edge weight if the graph is weighted)

Applications of the Adjacency Matrix:

Graph algorithms: Many graph algorithms like Dijkstra’s algorithm, Floyd-Warshall algorithm, and Kruskal’s algorithm use adjacency matrices to represent graphs.
Image processing: Adjacency matrices are used in image processing to represent the adjacency relationship between pixels in an image.
Finding the shortest path between two nodes: By performing matrix multiplication on the adjacency matrix, one can find the shortest path between any two nodes in a graph.

Advantages of using Adjacency Matrix:

An adjacency matrix is simple and easy to understand.
Adding or removing edges from a graph is quick and easy.
It allows constant time access to any edge in the graph.

Disadvantages of using Adjacency Matrix:

It is inefficient in terms of space utilisation for sparse graphs because it takes up O(N²) space.
Computing all neighbors of a vertex takes O(N) time.