Python Data Structure and Algorithm Tutorial – Graph Data Structure

Graph Data Structure


In this tutorial, you will learn what a Graph Data Structure is. Also, you will find representations of a graph.

A graph data structure is a collection of nodes that have data and are connected to other nodes.

Let’s try to understand this through an example. On facebook, everything is a node. That includes User, Photo, Album, Event, Group, Page, Comment, Story, Video, Link, Note…anything that has data is a node.

Every relationship is an edge from one node to another. Whether you post a photo, join a group, like a page, etc., a new edge is created for that relationship.

graph data structure explained using facebook's example. Users, groups, pages, events, etc. are represented as nodes and their relationships - friend, joining a group, liking a page are represented as links between nodes
Example of graph data structure

All of facebook is then a collection of these nodes and edges. This is because facebook uses a graph data structure to store its data.

More precisely, 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
Vertices and edges


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
Graph adjacency matrix

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
Adjacency list representation

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.


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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