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Table of contents
Erdos Renyi Model
Properties of G(n, P) model
Frequently Asked Questions 
What are graphs?
What are cyclic and acyclic graphs?
What is the Erdos-Renyi model?
Last Updated: Mar 27, 2024

Erdos RenyI Model for generating random graphs

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A Graph is a set of vertices connected together using edges. A random graph is one in which characteristics like the number of vertices, edges, and connections between them are chosen randomly. 

Erdos renyi model

In this blog, we will discuss Erdos–Rényi model that is used to generate random graphs. We can generate both directed as well as undirected graphs using this model.

Erdos Renyi Model

The Erdos-Renyi model is used to generate random graphs and it is either of two closely related models that are used to generate random graphs.

  • G(n, M) model
  • G(n, P) model

In the G(n, M) model, a graph with n nodes and M edges is selected uniformly at random from the entire collection of such graphs. For example, if we have G(4, 2) then any graph from the collection of graphs with 4 nodes and 2 edges will be selected uniformly randomly.

In the G(n, P) model, a graph is created by randomly linking the nodes. Every edge has an independent probability ‘p’ of being in the graph, from every other edge. All networks with n nodes and M edges have an equivalent probability of:


The value of p lies between 0 and 1 and as the value of p increases from 0 to 1 the probability of including more edges in the graph also increases.

In this article, we will be discussing only the G(n, P) model. 

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Properties of G(n, P) model

The model is named after Hungarian mathematicians Paul Erdős and Alfréd Rényi. In 1960, both mathematicians did an experiment to closely observe the behavior of the G(n, p) model for different values of p. Below are the points that they observed:

  • If np < 1, then a graph will almost have no connected components of size larger than O(log(n)).

  • If np = 1, then a graph will have a largest component whose size is of order n^{2/3}.

  • If np –>  c > 1, where c is a constant, then a graph will have a unique giant component containing a positive fraction of the vertices. No other component will contain more than O(log(n)) vertices.

  • If Probability distribution, then a graph in G(n, p) will almost surely contain isolated vertices, and thus be disconnected. 

  • If probability distribution, then a graph will be connected.



We will be using the below function available in networkx library in python for drawing erdos reyni graph.

Erdos_renyi_graph(n, p, seed=None, directed=False) 


n: number of nodes,

p: probability for edge

seed: seed for random number generation, by default it is set to none.

directed: if it's set to true, the graph returned will be a directed graph. By default, its value is set to false

The above function returns a G(n, P) graph created using the values passed.


# importing required libraries
import networkx as nx
import matplotlib.pyplot as plt

# value of n and p
n = 50
P = 0.25

# generating the graph
graph = nx.erdos_renyi_graph(n, P)

# drawing the graph
nx.draw(graph, with_labels=True, node_color="tab:orange")

# showing graph on screen



Output 1

Output 2

Output 3

Output 4

Frequently Asked Questions 

What are graphs?

One common type of data structure is a graph, which consists of a finite number of nodes (also called vertices) and a finite number of connected edges. An edge is a pair (u, v) that denotes a connection between u and v vertices.

What are cyclic and acyclic graphs?

If a network has a cycle—a continuous set of nodes without any repeating nodes or edges that connects back to itself—then it is said to be cyclic. Acyclic graphs are those without cycles.

What is the Erdos-Renyi model?

Erdos–Rényi is a model that is used to generate random graphs. We can generate both directed as well as an undirected graphs using this model.


In this article, we have extensively discussed the Erdos-Renyi model that is used to generate random graphs. We have also seen implementation in python. 

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