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Graph Data Structure

by Anup Maurya
5 minutes read

A graph data structure is a representation of various interconnected elements, where each element is called a node and holds specific information.

Let’s consider Facebook as an example, where everything on the platform is treated as a node. These nodes encompass a wide range of data-rich entities like Users, Photos, Albums, Events, Groups, Pages, Comments, Stories, Videos, Links, and Notes.

The relationships between these entities are depicted as edges connecting one node to another. For instance, whenever actions like posting a photo, joining a group, or liking a page occur, a new edge is formed to signify the association between the relevant nodes.

What is Graph

A graph is a data structure (V, E) that consists of

  • A collection of vertices V
  • A collection of edges E, represented as ordered pairs of vertices (u,v)
a graph contains vertices that are like points and edges that connect the points

In the graph,

V = {0, 1, 2, 3}
E = {(0,1), (0,2), (0,3), (1,2)}
G = {V, E}

Graph Terminology

  • Adjacency: A vertex is said to be adjacent to another vertex if there is an edge connecting them. Vertices 2 and 3 are not adjacent because there is no edge between them.
  • Path: A sequence of edges that allows you to go from vertex A to vertex B is called a path. 0-1, 1-2 and 0-2 are paths from vertex 0 to vertex 2.
  • Directed Graph: A graph in which an edge (u,v) doesn’t necessarily mean that there is an edge (v, u) as well. The edges in such a graph are represented by arrows to show the direction of the edge.

Graph Representation

Graphs are commonly represented in two ways:

1. Adjacency Matrix

An adjacency matrix is a 2D array of V x V vertices. Each row and column represent a vertex.

If the value of any element a[i][j] is 1, it represents that there is an edge connecting vertex i and vertex j.

The adjacency matrix for the graph we created above is

graph adjacency matrix for sample graph shows that the value of matrix element is 1 for the row and column that have an edge and 0 for row and column that don't have an edge

Since it is an undirected graph, for edge (0,2), we also need to mark edge (2,0); making the adjacency matrix symmetric about the diagonal.

Edge lookup(checking if an edge exists between vertex A and vertex B) is extremely fast in adjacency matrix representation but we have to reserve space for every possible link between all vertices(V x V), so it requires more space.

2. Adjacency List

An adjacency list represents a graph as an array of linked lists.

The index of the array represents a vertex and each element in its linked list represents the other vertices that form an edge with the vertex.

The adjacency list for the graph we made in the first example is as follows:

adjacency list representation represents graph as array of linked lists where index represents the vertex and each element in linked list represents the edges connected to that vertex

An adjacency list is efficient in terms of storage because we only need to store the values for the edges. For a graph with millions of vertices, this can mean a lot of saved space.

Other ways of graph representation are Cost Adjacency Matrix, Cost Adjacency List , Compact List representation.

Graph Operations

The most common graph operations are:

  • Check if the element is present in the graph
  • Graph Traversal
  • Add elements(vertex, edges) to graph
  • Finding the path from one vertex to another

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